first push of the AI

.gitignore

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+
+__pycache__
+.pytest_cache
+.cache_cameroon
+.venv
+.env
+image-test1.jpg
+image-test2.jpg
+test-audio1.wav

Dockerfile

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+FROM python:3.11-slim
+
+ENV PYTHONDONTWRITEBYTECODE=1 \
+    PYTHONUNBUFFERED=1
+
+WORKDIR /app
+
+RUN apt-get update \
+    && apt-get install -y --no-install-recommends \
+       build-essential \
+       libpq-dev \
+        ffmpeg \
+        libsndfile1 \
+    && rm -rf /var/lib/apt/lists/*
+
+COPY requirements.txt ./
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+EXPOSE 8000
+
+CMD ["sh", "-c", "uvicorn main:app --host 0.0.0.0 --port ${PORT:-8000}"]
+

LICENSE

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+MIT License
+
+Copyright (c) 2025 damarusyoane
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

app/__init__.py

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+

app/ai_agent/agent.py

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+from typing import List, Dict, Any, Optional
+from langchain.agents import AgentExecutor, create_react_agent, initialize_agent, AgentType
+from langchain_openai import ChatOpenAI
+from langchain_community.chat_models import ChatOllama
+from langchain_community.tools import Tool
+from langchain_community.utilities import SerpAPIWrapper
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder, PromptTemplate
+from pydantic import BaseModel, Field
+from langchain_core.output_parsers import PydanticOutputParser
+from langchain_huggingface import HuggingFaceEndpoint
+from .maps_tool import GoogleMapsTool
+from .medical_prompt import medical_system_prompt, medical_direct_prompt
+from .cameroon_data import get_cameroon_data
+from ..utils.config import settings
+from app.ai_services import (
+    analyze_image as svc_analyze_image,
+    transcribe_audio as svc_transcribe_audio,
+    chat_completion as svc_chat_completion,
+    translate_text as svc_translate_text,
+)
+import logging
+import base64
+import io
+import json
+import mimetypes
+import os
+import tempfile
+import requests
+import secrets
+import time
+
+# Robust import for OpenAI rate limit error across SDK versions
+try:
+    from openai import RateLimitError  # Newer SDKs
+except Exception:  # pragma: no cover - fallback for older SDKs
+    try:
+        from openai.error import RateLimitError  # Older SDKs
+    except Exception:
+        class RateLimitError(Exception):
+            pass
+
+logger = logging.getLogger(__name__)
+
+# Pydantic model for structured ReAct output
+class ReActOutput(BaseModel):
+    thought: str = Field(description="Your internal reasoning about the query")
+    action: str = Field(description="Name of the tool to use, or empty if none", default="")
+    action_input: str = Field(description="Input for the tool, or empty if no tool", default="")
+    observation: str = Field(description="Observation from tool, or empty if no tool used", default="")
+    final_answer: str = Field(description="Final response to the user, or empty if continuing", default="")
+
+def calculate_dosage(input_text):
+    # À compléter selon besoins médicaux réels
+    return "Calcul de dosage basé sur: " + input_text
+
+# ==========================
+# Helper utilities for tools
+# ==========================
+MAX_IMAGE_CHARS = 6_000_000  # ~6MB when passed as data URI length cap
+MAX_AUDIO_BYTES = 10 * 1024 * 1024  # 10 MB
+MAX_FILE_BYTES = 2 * 1024 * 1024    # 2 MB
+
+def _is_data_uri(s: str) -> bool:
+    return isinstance(s, str) and s.startswith("data:")
+
+def _looks_like_url(s: str) -> bool:
+    return isinstance(s, str) and (s.startswith("http://") or s.startswith("https://"))
+
+def _to_image_data_ref(value: str) -> str:
+    """Return a URL or data URI suitable for OpenAI vision input.
+    If it's base64 without data: prefix, assume PNG.
+    """
+    if not isinstance(value, str):
+        raise ValueError("Image reference must be a string (URL or base64/data URI)")
+    if _is_data_uri(value) or _looks_like_url(value):
+        return value
+    if value.startswith("attach://"):
+        data = resolve_attachment(value)
+        b64 = base64.b64encode(data).decode("utf-8")
+        return f"data:image/png;base64,{b64}"
+    # Assume raw base64
+    return f"data:image/png;base64,{value}"
+
+def _download_bytes(url: str, timeout: int = 15, max_bytes: int = MAX_AUDIO_BYTES) -> bytes:
+    with requests.get(url, stream=True, timeout=timeout) as r:
+        r.raise_for_status()
+        data = io.BytesIO()
+        total = 0
+        for chunk in r.iter_content(chunk_size=8192):
+            if not chunk:
+                continue
+            total += len(chunk)
+            if total > max_bytes:
+                raise ValueError("Downloaded content exceeds size limit")
+            data.write(chunk)
+        return data.getvalue()
+
+def _decode_data_uri(data_uri: str) -> bytes:
+    # Format: data:<mime>;base64,<payload>
+    try:
+        header, b64data = data_uri.split(',', 1)
+        return base64.b64decode(b64data)
+    except Exception:
+        raise ValueError("Invalid data URI")
+
+def _to_bytes_from_any(ref: str, max_bytes: int) -> bytes:
+    # Handle in-memory attach scheme first
+    if isinstance(ref, str) and ref.startswith("attach://"):
+        data = resolve_attachment(ref)
+        if len(data) > max_bytes:
+            raise ValueError("Content exceeds size limit")
+        return data
+    if _looks_like_url(ref):
+        return _download_bytes(ref, max_bytes=max_bytes)
+    if _is_data_uri(ref):
+        data = _decode_data_uri(ref)
+        if len(data) > max_bytes:
+            raise ValueError("Content exceeds size limit")
+        return data
+    # Assume base64
+    data = base64.b64decode(ref)
+    if len(data) > max_bytes:
+        raise ValueError("Content exceeds size limit")
+    return data
+
+# ==========================
+# In-memory attachment registry
+# ==========================
+ATTACHMENT_STORE: Dict[str, Dict[str, Any]] = {}
+
+def register_attachment(data: bytes, filename: str | None = None, mime: str | None = None) -> str:
+    """Store bytes in a temp registry and return an attach:// token URI."""
+    token = secrets.token_urlsafe(16)
+    ATTACHMENT_STORE[token] = {"data": data, "filename": filename, "mime": mime}
+    return f"attach://{token}"
+
+def resolve_attachment(ref: str) -> bytes:
+    """Resolve an attach:// token to bytes, or raise KeyError/ValueError."""
+    if not isinstance(ref, str) or not ref.startswith("attach://"):
+        raise ValueError("Not an attach:// reference")
+    token = ref.split("://", 1)[1]
+    item = ATTACHMENT_STORE.get(token)
+    if not item:
+        raise KeyError("Attachment not found or expired")
+    return item["data"]
+
+# ==========================
+# Tool: Vision (Image analysis)
+# ==========================
+def analyze_image_tool(input_str: str) -> str:
+    """Analyze a medical image (URL or base64/data URI) with an optional question.
+    Input can be JSON: {{"image": "<url|data|base64>", "question": "..."}}
+    or a pipe-separated string: "<url|data|base64>|<question>".
+    Returns concise medical observations and red flags.
+    """
+    try:
+        image = None
+        question = (
+            "Analyze this image and describe medically relevant observations, differential considerations, "
+            "and red flags. If urgent signs are suspected, advise seeking care."
+        )
+        if input_str.strip().startswith('{'):
+            obj = json.loads(input_str)
+            image = obj.get('image') or obj.get('url') or obj.get('image_url')
+            question = obj.get('question') or question
+        else:
+            parts = [p.strip() for p in input_str.split('|', 1)]
+            if parts:
+                image = parts[0]
+            if len(parts) > 1 and parts[1]:
+                question = parts[1]
+        if not image:
+            return "Format attendu: JSON {\"image\": \"...\", \"question\": \"...\"} ou 'image|question'"
+
+        image_ref = _to_image_data_ref(image)
+        if len(image_ref) > MAX_IMAGE_CHARS:
+            return "Image trop volumineuse. Réduisez la taille ou fournissez une URL."
+
+        # Delegate to unified image analysis service (HF or local)
+        return svc_analyze_image(image_ref, question)
+    except Exception as e:
+        logger.error(f"Error in analyze_image_tool: {e}", exc_info=True)
+        return "Impossible d'analyser l'image pour le moment. Essayez une image plus petite ou une meilleure connexion."
+
+# ==========================
+# Tool: Audio transcription (Whisper)
+# ==========================
+def transcribe_audio_tool(input_str: str) -> str:
+    """Transcribe an audio file (URL or base64/data URI). Returns plain text transcription.
+    Input can be JSON: {{"audio": "<url|data|base64>", "filename": "name.ext"}} or just the URL/base64.
+    Size cap: 10 MB.
+    """
+    try:
+        audio_ref = None
+        if input_str.strip().startswith('{'):
+            obj = json.loads(input_str)
+            audio_ref = obj.get('audio') or obj.get('url')
+        else:
+            audio_ref = input_str.strip()
+        if not audio_ref:
+            return "Format attendu: {\"audio\": \"...\"} ou une chaîne URL/base64/data URI."
+
+        audio_bytes = _to_bytes_from_any(audio_ref, MAX_AUDIO_BYTES)
+        # Write to a temp file and delegate to unified ASR
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp:
+            tmp.write(audio_bytes)
+            tmp.flush()
+            temp_path = tmp.name
+        try:
+            return svc_transcribe_audio(temp_path, None)
+        finally:
+            try:
+                os.unlink(temp_path)
+            except Exception:
+                pass
+    except ValueError as ve:
+        return f"Audio non valide: {ve}"
+    except Exception as e:
+        logger.error(f"Error in transcribe_audio_tool: {e}", exc_info=True)
+        return "Impossible de transcrire l'audio pour le moment. Fournissez un fichier plus petit ou réessayez."
+
+# ==========================
+# Tool: File parse and summarize
+# ==========================
+def parse_file_tool(input_str: str) -> str:
+    """Parse and summarize a small file (text/PDF). Input JSON: {{"file": "<url|data|base64>", "filename": "..."}}
+    or "<url|base64>|<filename>". Size cap: 2 MB.
+    Returns a concise medically-relevant summary.
+    """
+    try:
+        file_ref = None
+        filename = None
+        if input_str.strip().startswith('{'):
+            obj = json.loads(input_str)
+            file_ref = obj.get('file') or obj.get('url') or obj.get('content')
+            filename = obj.get('filename')
+        else:
+            parts = [p.strip() for p in input_str.split('|', 1)]
+            if parts:
+                file_ref = parts[0]
+            if len(parts) > 1:
+                filename = parts[1]
+        if not file_ref:
+            return "Format attendu: JSON {\"file\": \"...\", \"filename\": \"...\"} ou 'file|filename'"
+
+        data = _to_bytes_from_any(file_ref, MAX_FILE_BYTES)
+
+        # Determine type
+        ext = (os.path.splitext(filename)[1].lower() if filename else '')
+        text_content = None
+        if ext == '.pdf' or (_is_data_uri(file_ref) and 'application/pdf' in file_ref[:64]):
+            try:
+                import PyPDF2  # type: ignore
+                reader = PyPDF2.PdfReader(io.BytesIO(data))
+                pages = min(3, len(reader.pages))
+                buf = []
+                for i in range(pages):
+                    try:
+                        buf.append(reader.pages[i].extract_text() or '')
+                    except Exception:
+                        continue
+                text_content = "\n".join(buf)
+            except ImportError:
+                return "Lecture PDF indisponible: installez PyPDF2 pour activer l'analyse des PDF."
+            except Exception:
+                return "Impossible de lire ce PDF. Assurez-vous qu'il n'est pas corrompu et qu'il est < 2 Mo."
+        else:
+            # Assume text-like
+            try:
+                text_content = data.decode('utf-8', errors='ignore')
+            except Exception:
+                return "Type de fichier non supporté. Fournissez un texte ou un PDF (PyPDF2 requis)."
+
+        if not text_content or not text_content.strip():
+            return "Aucun texte exploitable trouvé dans le fichier."
+
+        snippet = text_content[:50_000]  # keep prompt small
+        language = detect_language(snippet)
+        prompt = (
+            "Résume de façon concise les informations médicales pertinentes du contenu suivant. "
+            "Inclue les signaux d'alerte éventuels et recommande, si nécessaire, une consultation.\n\n"
+            f"Contenu:\n{snippet}"
+        )
+        return svc_chat_completion([{"role": "user", "content": prompt}], language)
+    except ValueError as ve:
+        return f"Fichier non valide: {ve}"
+    except Exception as e:
+        logger.error(f"Error in parse_file_tool: {e}", exc_info=True)
+        return "Impossible d'analyser le fichier pour le moment. Essayez un fichier texte court ou un PDF léger (<2 Mo)."
+
+def create_medical_agent(scratchpad_style: str = "messages"):
+    logger.info(f"Creating medical agent (scratchpad_style={scratchpad_style})...")
+    
+    try:
+        provider = (settings.AI_PROVIDER or "hf").lower()
+        logger.info(f"Agent provider: {provider}")
+        # Initialize with system message
+        system_message = """
+        Tu es Medicare, un assistant médical intelligent et bienveillant pour la population camerounaise.
+
+Ton objectif :
+- Écouter et rassurer la personne, comme un vrai professionnel de santé empathique.
+- Répondre simplement, comme si tu discutais avec un proche ou un patient, mais toujours avec sérieux.
+- Utiliser Google Maps pour proposer des hôpitaux ou pharmacies proches si besoin.
+- Donner des conseils adaptés au Cameroun (maladies, médicaments, habitudes locales).
+- Si tu retrouves des cas similaires dans la base camerounaise, mentionne-les naturellement dans la discussion.
+- Si la question sort du médical, explique gentiment que tu es là pour la santé.
+
+Règles :
+- Ne pose jamais de diagnostic définitif.
+- Pour les symptômes graves, incite à consulter un médecin ou à se rendre aux urgences, sans paniquer l'utilisateur.
+- Si tu as besoin de plus d'infos, pose des questions ouvertes et humaines.
+- Garde le fil de la conversation et adapte tes réponses à l'historique de l'échange.
+- Si des pièces jointes sont listées sous forme de jetons attach://<token>, utilise les outils appropriés en leur passant directement ces références :
+  * Analyse_Image pour les images (JSON {{"image": "attach://...", "question": "..."}}).
+  * Transcription_Audio pour l'audio (JSON {{"audio": "attach://..."}}).
+  * Analyse_Fichier pour les fichiers texte/PDF (JSON {{"file": "attach://...", "filename": "..."}}).
+
+
+Format de réponse :
+- Commence par une phrase chaleureuse ou rassurante.
+- Donne l'information ou le conseil principal de façon claire et naturelle.
+- Si tu proposes des établissements, présente-les comme tu le ferais à un ami (nom, adresse, statut, téléphone).
+- Termine par une phrase d'ouverture ou d'encouragement ("N'hésite pas si tu as d'autres questions !").
+- Ajoute un avertissement discret si nécessaire (ex : "Si tu te sens vraiment mal, va vite consulter !").
+
+IMPORTANT: N'utilise PAS de traductions pour les étiquettes. Utilise EXACTEMENT ces étiquettes en anglais: "Thought:", "Action:", "Action Input:", "Observation:", "Final Answer:".
+
+        Toujours répondre en utilisant CE FORMAT EXACT. Après chaque "Thought:", fais l'un des deux:
+        1) Si tu as besoin d'un outil: fournis "Action:" et "Action Input:", puis attends l'"Observation:" de l'outil.
+        2) Si tu n'as pas besoin d'outil: termine directement avec "Final Answer:".
+
+        Thought: [Tes réflexions internes sur la question et ce que tu vas faire ensuite. Sois bref et logique. Décide si un outil est nécessaire.]
+        Action: [Nom de l'outil exact, comme "Recherche_Web" ou "Google_Maps". Un seul outil. (N'inclus PAS "Final Answer" ici.)]
+        Action Input: [Entrée précise pour l'outil.]
+        Observation: [Résultat de l'outil.]
+
+        Final Answer: [Ta réponse finale complète pour l'utilisateur, en respectant le "Format de réponse" ci-dessus.]
+
+        Tu peux répéter Thought/Action/Observation au besoin (maximum 3 itérations), puis termine OBLIGATOIREMENT par "Final Answer:".
+
+N'oublie pas : tu es empathique, humain, et tu adaptes toujours ton niveau de langage à la personne en face de toi.
+
+        """
+        if provider == "ollama":
+            logger.info(f"Using ChatOllama model: {settings.OLLAMA_MODEL}")
+            llm = ChatOllama(
+                temperature=0,
+                model=(settings.OLLAMA_MODEL or "llama3.1:8b"),
+                base_url=settings.OLLAMA_BASE_URL,
+            )
+        elif provider == "lmstudio":
+            logger.info(f"Using LM Studio base_url: {settings.LMSTUDIO_BASE_URL}")
+            llm = ChatOpenAI(
+                temperature=0,
+                model=(settings.LMSTUDIO_MODEL or "local-model"),
+                openai_api_key=(settings.OPENAI_API_KEY or "lm-studio"),
+                base_url=settings.LMSTUDIO_BASE_URL,
+                streaming=False,
+                max_retries=1,
+                timeout=30,
+                max_tokens=500,
+            )
+        elif provider == "hf":
+            logger.info(f"Using Hugging Face Inference model: {settings.HF_TEXT_MODEL}")
+            llm = HuggingFaceEndpoint(
+                repo_id=(settings.HF_TEXT_MODEL or "mistralai/Mistral-7B-Instruct-v0.3"),
+                task="text-generation",
+                max_new_tokens=500,
+                temperature=0.0,
+                huggingfacehub_api_token=settings.HF_API_TOKEN,
+            )
+        else:
+            # Default: try local Ollama first, then fallback to ChatOpenAI config
+            try:
+                llm = ChatOllama(
+                    temperature=0,
+                    model=(settings.OLLAMA_MODEL or "llama3.1:8b"),
+                    base_url=settings.OLLAMA_BASE_URL,
+                )
+            except Exception:
+                llm = ChatOpenAI(
+                    temperature=0,
+                    model=(settings.OPENAI_MODEL or "gpt-4o-mini"),
+                    openai_api_key=settings.OPENAI_API_KEY,
+                    streaming=False,
+                    max_retries=1,
+                    timeout=30,
+                    max_tokens=500,
+                )
+        
+        # Initialize tools
+        tools = []
+        
+        # Add Google Maps tool if API key is available
+        if settings.GOOGLE_MAPS_API_KEY:
+            logger.info("Adding Google Maps tool")
+            maps_tool = GoogleMapsTool()
+            tools.append(maps_tool)
+        
+        # Add web search tool if API key is available
+        if settings.SERPAPI_API_KEY:
+            logger.info("Adding web search tool")
+            search = SerpAPIWrapper(serpapi_api_key=settings.SERPAPI_API_KEY)
+            web_search_tool = Tool(
+                name="Recherche_Web",
+                func=search.run,
+                description="Utile pour rechercher des informations médicales générales ou des hôpitaux"
+            )
+            tools.append(web_search_tool)
+        
+        # Add dosage calculator tool
+        logger.info("Adding dosage calculator tool")
+        dosage_tool = Tool(
+            name="Calculateur_Dosage",
+            func=calculate_dosage,
+            description="Utile pour calculer des dosages de médicaments basés sur le poids et l'âge"
+        )
+        tools.append(dosage_tool)
+        
+        # Add Vision, Audio Transcription, and File Parser tools
+        vision_tool = Tool(
+            name="Analyse_Image",
+            func=analyze_image_tool,
+            description=(
+                "Analyser une image médicale (URL ou base64/data URI). "
+                "Entrée: JSON {{\"image\": \"<url|data|base64>\", \"question\": \"...\"}} ou 'image|question'. Retourne des observations cliniques concises."
+            ),
+        )
+        tools.append(vision_tool)
+
+        audio_tool = Tool(
+            name="Transcription_Audio",
+            func=transcribe_audio_tool,
+            description=(
+                "Transcrire un audio (URL ou base64/data URI) en texte. "
+                "Entrée: JSON {{\"audio\": \"<url|data|base64>\"}} ou une chaîne URL/base64. Taille <= 10 Mo."
+            ),
+        )
+        tools.append(audio_tool)
+
+        file_tool = Tool(
+            name="Analyse_Fichier",
+            func=parse_file_tool,
+            description=(
+                "Analyser et résumer un petit fichier (texte/PDF). "
+                "Entrée: JSON {{\"file\": \"<url|data|base64>\", \"filename\": \"...\"}} ou 'file|filename'. PDF nécessite PyPDF2. Taille <= 2 Mo."
+            ),
+        )
+        tools.append(file_tool)
+        
+        logger.info(f"Initialized {len(tools)} tools")
+
+        # Build agent according to requested scratchpad style
+        requested = str(scratchpad_style).lower()
+        logger.info(f"Requested scratchpad style: {requested}")
+        # For HF provider, prefer legacy ReAct agent which works with text-generation LLMs
+        if provider == "hf" and requested != "legacy":
+            logger.info("HF provider: switching scratchpad to 'legacy' for compatibility")
+            requested = "legacy"
+
+        common_kwargs = {
+            "verbose": True,
+            "handle_parsing_errors": True,
+            "max_iterations": 3,
+            "max_execution_time": 15,
+        }
+
+        if requested == "legacy" or requested == "string":
+            logger.info("Creating legacy string-based ReAct agent")
+            # Use PromptTemplate for string scratchpad
+            prompt = PromptTemplate.from_template(
+                system_message + "\n\n{input}\n{agent_scratchpad}"
+            )
+            legacy_agent = initialize_agent(
+                tools=tools,
+                llm=llm,
+                agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
+                prompt=prompt,
+                **common_kwargs,
+            )
+            logger.info("Legacy/string agent created successfully")
+            return legacy_agent
+
+        # Messages-based ReAct agent with Pydantic output parser guidance
+        logger.info("Creating messages-based ReAct agent")
+        # Combine system prompt with ReAct-style instructions (no JSON)
+        template = system_message + """
+
+Tools: {tools}
+
+Tool names: {tool_names}
+
+Begin!
+
+{input}
+
+{agent_scratchpad}"""
+
+        prompt = ChatPromptTemplate.from_template(template)
+        # Supply tool variables for the prompt
+        prompt = prompt.partial(
+            tool_names=", ".join([t.name for t in tools]) if tools else "none",
+            tools="\n".join([f"- {t.name}: {t.description}" for t in tools]) if tools else "No tools available",
+        )
+        agent = create_react_agent(llm, tools, prompt)
+        logger.info("Messages-based agent created successfully")
+        return AgentExecutor(agent=agent, tools=tools, **common_kwargs)
+        
+    except Exception as e:
+        logger.error(f"Error creating agent: {str(e)}", exc_info=True)
+        raise
+
+import asyncio
+from concurrent.futures import ThreadPoolExecutor, TimeoutError as FuturesTimeoutError
+from functools import partial
+import re
+
+# logger is initialized at the top of this module
+
+def _invoke_with_timeout(agent: Any, user_input: str, timeout: int = 60) -> Any:
+    """Invoke the agent with a hard timeout in a background thread."""
+    def _invoke():
+        return agent.invoke({"input": user_input})
+    with ThreadPoolExecutor(max_workers=1) as executor:
+        future = executor.submit(_invoke)
+        return future.result(timeout=timeout)
+
+def search_cases_with_timeout(query: str, timeout: int = 10) -> str:
+    """Search for similar cases with a timeout to prevent hanging."""
+    try:
+        cameroon_data = get_cameroon_data()
+        if cameroon_data is None:
+            logger.warning("No clinical data available")
+            return ""
+            
+        similar_cases = cameroon_data.search_similar_cases(query, top_k=3)
+        
+        if not similar_cases:
+            return ""
+            
+        context = "Cas cliniques camerounais similaires trouvés :\n"
+        for case in similar_cases:
+            # Format the case nicely, showing only non-empty fields
+            case_info = []
+            for key, value in case.items():
+                if value and str(value).strip() and str(value).lower() != 'nan':
+                    case_info.append(f"{key}: {value}")
+            if case_info:
+                context += f"- {' | '.join(case_info)}\n"
+                
+        return context
+        
+    except Exception as e:
+        logger.error(f"Error searching cases: {str(e)}")
+        return ""
+
+from langdetect import detect
+
+def detect_language(text: str) -> str:
+    """Detect the language of the input text."""
+    try:
+        lang = detect(text)
+        return lang if lang in ['fr', 'en'] else 'fr'  # Default to French if not English
+    except:
+        return 'fr'  # Default to French on detection failure
+
+def handle_user_query(
+    query: str,
+    user_location: str = None,
+    image: str = None,
+    audio: str = None,
+    files: List[str] = None,
+    file_names: List[str] = None,
+    images: List[str] = None,
+    audios: List[str] = None,
+    agent_mode: str = None,
+) -> str:
+    try:
+        logger.info(f"Handling query: {query[:100]}...")  # Log first 100 chars of query
+        
+        # Detect input language
+        input_language = detect_language(query)
+        logger.info(f"Detected input language: {input_language}")
+        
+        # Build the final input with language instruction
+        try:
+            language_instruction = " (répondez en français)" if input_language == 'fr' else " (respond in English)"
+            query_with_language = f"{query}{language_instruction}"
+            
+            # Quick simple-query bypass BEFORE agent creation: no attachments and no location
+            has_attachments = bool((image and image.strip()) or (images and len(images) > 0) or (audio and audio.strip()) or (audios and len(audios) > 0) or (files and len(files) > 0))
+            if not has_attachments and not user_location:
+                logger.info("Simple query; direct LLM (no agent) via unified provider")
+                try:
+                    direct_prompt = medical_direct_prompt + f"\n{query_with_language}"
+                    return svc_chat_completion([{"role": "user", "content": direct_prompt}], input_language)
+                except Exception as e:
+                    logger.warning(f"Simple path failed, using agent: {e}")
+        
+        except Exception as e:
+            logger.warning(f"Error preparing input for simple bypass: {e}")
+
+        # Create agent automatically only if needed: try messages first, fall back to legacy/string
+        try:
+            agent = create_medical_agent("messages")
+            logger.info("Agent created successfully (messages scratchpad)")
+        except Exception as e:
+            logger.warning(f"Messages-based agent creation failed, trying legacy fallback: {e}")
+            try:
+                agent = create_medical_agent("legacy")
+                logger.info("Agent created successfully (legacy scratchpad)")
+            except Exception as e2:
+                logger.error(f"Error creating agent after legacy fallback: {str(e2)}", exc_info=True)
+                return "Désolé, une erreur est survenue lors de l'initialisation de l'assistant."
+        
+        # Run Cameroon data search with timeout in a separate thread
+        context = ""
+        try:
+            with ThreadPoolExecutor() as executor:
+                future = executor.submit(search_cases_with_timeout, query)
+                context = future.result(timeout=15)  # 15 seconds timeout
+                logger.info("Successfully searched clinical data")
+        except FuturesTimeoutError:
+            logger.warning("Cameroon data search timed out")
+        except Exception as e:
+            logger.error(f"Error in clinical data search: {str(e)}", exc_info=True)
+        
+        # Build the final input with language instruction
+        try:
+            # Summarize attachments so the agent is aware of provided modalities
+            attachment_lines: List[str] = []
+            all_images = []
+            if image:
+                all_images.append(image)
+            if images:
+                all_images.extend(images)
+            if all_images:
+                # Show attach URIs to enable tool usage
+                show = ", ".join(all_images[:5]) + ("..." if len(all_images) > 5 else "")
+                attachment_lines.append(f"- Images: {len(all_images)} fournies -> {show}")
+            
+            all_audios = []
+            if audio:
+                all_audios.append(audio)
+            if audios:
+                all_audios.extend(audios)
+            if all_audios:
+                show = ", ".join(all_audios[:5]) + ("..." if len(all_audios) > 5 else "")
+                attachment_lines.append(f"- Audios: {len(all_audios)} fournis -> {show}")
+            
+            if files:
+                names = file_names or []
+                display_names = ", ".join(names[:5]) + ("..." if len(names) > 5 else "")
+                show = ", ".join((files or [])[:5]) + ("..." if len(files or []) > 5 else "")
+                attachment_lines.append(f"- Fichiers: {len(files)} ({display_names}) -> {show}")
+            
+            attachments_context = ""
+            if attachment_lines:
+                attachments_context = "Pièces jointes:\n" + "\n".join(attachment_lines)
+            
+            components: List[str] = []
+            if context:
+                components.append(context)
+            if attachments_context:
+                components.append(attachments_context)
+            components.append(query_with_language)
+            user_input = "\n\n".join(components)
+            if user_location:
+                user_input += f"\nUser location: {user_location}"
+            
+            logger.debug(f"Final input to agent: {user_input[:200]}...")  # Log first 200 chars
+            
+            # Simple-query bypass: if no attachments and no explicit location, answer directly via LLM
+            if not attachments_context and not user_location:
+                try:
+                    simple_prompt = medical_direct_prompt + f"\n{user_input}"
+                    return svc_chat_completion([{"role": "user", "content": simple_prompt}], input_language)
+                except Exception as e:
+                    logger.warning(f"Simple path failed, using agent: {e}")
+
+            # Execute agent with a hard timeout; if parsing issues occur, apply fallbacks
+            try:
+                response = _invoke_with_timeout(agent, user_input, timeout=60)
+            except FuturesTimeoutError:
+                logger.warning("Agent invocation timed out; retrying after 10s sleep")
+                time.sleep(10)
+                try:
+                    response = _invoke_with_timeout(agent, user_input, timeout=60)
+                except FuturesTimeoutError:
+                    logger.error("Agent invocation timed out again after retry")
+                    return _offline_fallback_response(query, context, input_language)
+                except Exception as e2:
+                    logger.error(f"Error after timeout retry: {e2}", exc_info=True)
+                    return _offline_fallback_response(query, context, input_language)
+            except Exception as e:
+                msg = str(e).lower()
+                # Timeout retry once
+                if ("timeout" in msg) or isinstance(e, FuturesTimeoutError):
+                    logger.warning("Invocation timed out; retrying after 10s sleep")
+                    time.sleep(10)
+                    return _invoke_with_timeout(agent, user_input, timeout=60)
+                # Parsing-related issues: prefer direct LLM fallback
+                if isinstance(e, RateLimitError):
+                    wait_time = 180
+                    logger.warning(f"Rate limit hit; sleeping {wait_time}s before retry")
+                    time.sleep(wait_time)
+                    try:
+                        response = _invoke_with_timeout(agent, user_input, timeout=60)
+                    except Exception as e2:
+                        logger.error(f"Retry after rate limit failed: {e2}", exc_info=True)
+                        return _offline_fallback_response(query, context, input_language)
+                elif re.search(r"(invalid format|missing action|parsing failure)", msg, re.IGNORECASE):
+                    logger.warning("Detected parsing issue; using direct LLM fallback first")
+                    # Prefer a single-call direct LLM response to avoid further agent loops
+                    try:
+                        direct_prompt = medical_direct_prompt + f"\n{user_input}"
+                        response_text = svc_chat_completion([{"role": "user", "content": direct_prompt}], input_language)
+                        return response_text
+                    except Exception:
+                        logger.warning("Direct LLM fallback failed; trying legacy agent as secondary fallback")
+                        try:
+                            agent = create_medical_agent("legacy")
+                            response = _invoke_with_timeout(agent, user_input, timeout=60)
+                        except Exception:
+                            return _offline_fallback_response(query, context, input_language)
+                else:
+                    logger.error(f"Error during agent invocation: {e}", exc_info=True)
+                    return _offline_fallback_response(query, context, input_language)
+            
+            # Normalize response across LC versions (dict vs. string)
+            if response is None:
+                logger.error("Agent returned None")
+                return _offline_fallback_response(query, context, input_language)
+
+            # Extract response text
+            if isinstance(response, dict):
+                # Handle dict response (newer LC versions)
+                if 'output' in response:
+                    response_text = response['output']
+                elif 'final_answer' in response:
+                    response_text = response['final_answer']
+                elif 'result' in response:
+                    response_text = response['result']
+                else:
+                    logger.warning(f"Unexpected response format: {response}")
+                    response_text = str(response)
+            else:
+                # Handle string response (older LC versions)
+                response_text = str(response)
+
+            # Ensure response is in the same language as the input
+            if input_language == 'fr' and not any(word in response_text.lower() for word in ['désolé', 'bonjour', 'merci']):
+                try:
+                    response_text = svc_translate_text(response_text, 'fr')
+                except Exception as e:
+                    logger.error(f"Error translating response to French: {e}")
+                    response_text = f"[English Response] {response_text}\n\nDésolé, je n'ai pas pu traduire la réponse en français. Voici la réponse en anglais ci-dessus."
+        
+            return response_text
+            
+        except Exception as e:
+            logger.error(f"Error in agent execution: {str(e)}", exc_info=True)
+            return _offline_fallback_response(query, context, input_language)
+            
+    except Exception as e:
+        logger.critical(f"Critical error in handle_user_query: {str(e)}", exc_info=True)
+        return "Désolé, une erreur inattendue s'est produite. Veuillez réessayer plus tard."
+
+def _offline_fallback_response(query: str, context: str, lang: str) -> str:
+    """Produce a concise offline fallback response for low connectivity situations."""
+    if lang == 'fr':
+        parts = [
+            "Connexion instable détectée. Voici une réponse rapide basée sur des bonnes pratiques générales :",
+        ]
+        if context:
+            parts.append(context.strip())
+        parts.extend([
+            "- Prends soin de toi et évite les efforts inutiles.",
+            "- Tu peux envisager des antalgiques en vente libre avec prudence si nécessaire.",
+            "- Si les symptômes sont sévères, persistent ou s'aggravent, consulte rapidement un professionnel de santé.",
+            "Note: message généré en mode connectivité limitée. Pour un avis personnalisé, consulte un professionnel.",
+        ])
+        return "\n".join(parts)
+    else:
+        parts = [
+            "Unstable connection detected. Here is a brief response based on general good practices:",
+        ]
+        if context:
+            parts.append(context.strip())
+        parts.extend([
+            "- Take care and avoid unnecessary strain.",
+            "- Consider over-the-counter pain relief responsibly if appropriate.",
+            "- If symptoms are severe, persistent, or worsening, seek medical care promptly.",
+            "Note: generated in low-connectivity mode. For personalized advice, consult a professional.",
+        ])
+        return "\n".join(parts)

app/ai_agent/cameroon_data.py

@@ -0,0 +1,111 @@
+import pandas as pd
+from typing import List, Dict, Optional
+import os
+import logging
+from functools import lru_cache
+
+logger = logging.getLogger(__name__)
+
+class CameroonData:
+    _instance = None
+    
+    def __new__(cls):
+        if cls._instance is None:
+            cls._instance = super(CameroonData, cls).__new__(cls)
+            cls._instance._initialized = False
+        return cls._instance
+    
+    def __init__(self):
+        if self._initialized:
+            return
+            
+        self._initialized = True
+        self.df = None
+        self._load_data()
+    
+    def _load_data(self):
+        """Load the clinical data with error handling and performance optimizations."""
+        try:
+            csv_path = os.path.join(os.path.dirname(__file__), '../../clinical_summaries.csv')
+            if not os.path.exists(csv_path):
+                logger.warning(f"Clinical data file not found at {csv_path}")
+                self.df = pd.DataFrame()
+                return
+                
+            # Load only necessary columns if possible
+            self.df = pd.read_csv(csv_path, dtype=str)
+            logger.info(f"Loaded {len(self.df)} clinical cases")
+            
+        except Exception as e:
+            logger.error(f"Error loading clinical data: {str(e)}")
+            self.df = pd.DataFrame()
+    
+    @lru_cache(maxsize=128)
+    def search_similar_cases(self, query: str, top_k: int = 3) -> List[Dict]:
+        """
+        Search for similar cases using the query.
+        
+        Args:
+            query: Search query string
+            top_k: Maximum number of results to return
+            
+        Returns:
+            List of matching case dictionaries
+        """
+        if self.df is None or self.df.empty:
+            logger.warning("No clinical data available for search")
+            return []
+            
+        if not query or not query.strip():
+            return []
+            
+        try:
+            # Convert query to lowercase once
+            query_terms = [term.lower() for term in query.split() if len(term) > 2]  # Ignore very short terms
+            if not query_terms:
+                return []
+                
+            results = []
+            
+            # Pre-process text for each row once
+            for _, row in self.df.iterrows():
+                # Only process string columns and skip NaN values
+                row_text = ' '.join(
+                    str(row[col]) for col in self.df.columns 
+                    if isinstance(row[col], str) and pd.notna(row[col])
+                ).lower()
+                
+                # Check if any query term is in the row text
+                if any(term in row_text for term in query_terms):
+                    results.append(row.to_dict())
+                    if len(results) >= top_k:
+                        break
+                        
+            return results
+            
+        except Exception as e:
+            logger.error(f"Error in search_similar_cases: {str(e)}")
+            return []
+
+# Singleton instance
+_cameroon_data_instance = None
+
+def get_cameroon_data() -> Optional[CameroonData]:
+    """
+    Get the singleton instance of CameroonData.
+    Returns None if the data cannot be loaded.
+    """
+    global _cameroon_data_instance
+    
+    if _cameroon_data_instance is None:
+        try:
+            _cameroon_data_instance = CameroonData()
+            # Verify data was loaded
+            if _cameroon_data_instance.df is None or _cameroon_data_instance.df.empty:
+                logger.error("Failed to load clinical data")
+                _cameroon_data_instance = None
+        except Exception as e:
+            logger.error(f"Error initializing CameroonData: {str(e)}")
+            _cameroon_data_instance = None
+            
+    return _cameroon_data_instance

app/ai_agent/maps_tool.py

@@ -0,0 +1,43 @@
+import os
+import requests
+from langchain.tools import BaseTool
+from pydantic import BaseModel, Field
+from typing import Type
+
+class LocationInput(BaseModel):
+    location: str = Field(description="La localisation pour la recherche d'établissements médicaux")
+    keyword: str = Field(default="hospital|pharmacy", description="Type d'établissement: hôpital, pharmacie, etc.")
+
+class GoogleMapsTool(BaseTool):
+    name : str = "google_maps_search"
+    description : str = "Recherche des établissements médicaux près d'une localisation using Google Maps API"
+    args_schema: Type[BaseModel] = LocationInput
+
+    def _run(self, location: str, keyword: str = "hospital|pharmacy"):
+        api_key = os.getenv("GOOGLE_MAPS_API_KEY")
+        geocode_url = f"https://maps.googleapis.com/maps/api/geocode/json?address={location}&key={api_key}"
+        geocode_data = requests.get(geocode_url).json()
+        if not geocode_data['results']:
+            return "Désolé, je n'ai pas pu trouver cette localisation."
+        coords = geocode_data['results'][0]['geometry']['location']
+        lat, lng = coords['lat'], coords['lng']
+        places_url = f"https://maps.googleapis.com/maps/api/place/nearbysearch/json?location={lat},{lng}&radius=5000&keyword={keyword}&type=hospital|pharmacy&key={api_key}"
+        places_data = requests.get(places_url).json()
+        results = []
+        for place in places_data.get('results', [])[:5]:
+            is_open = "Ouvert" if place.get('opening_hours', {}).get('open_now', False) else "Fermé"
+            details_url = f"https://maps.googleapis.com/maps/api/place/details/json?place_id={place['place_id']}&fields=name,formatted_phone_number,opening_hours,formatted_address&key={api_key}"
+            details_data = requests.get(details_url).json()
+            place_info = details_data.get('result', {})
+            results.append({
+                'name': place_info.get('name', 'Nom non disponible'),
+                'address': place_info.get('formatted_address', 'Adresse non disponible'),
+                'phone': place_info.get('formatted_phone_number', 'Téléphone non disponible'),
+                'status': is_open,
+                'rating': place.get('rating', 'Non noté'),
+                'types': place.get('types', [])
+            })
+        return results
+
+    def _arun(self, location: str, keyword: str):
+        raise NotImplementedError("Async non supporté")

app/ai_agent/medical_prompt.py

@@ -0,0 +1,71 @@
+medical_system_prompt = '''
+Tu es Medicare, un assistant médical intelligent et bienveillant pour la population camerounaise.
+
+Ton objectif :
+- Écouter et rassurer la personne, comme un vrai professionnel de santé empathique.
+- Répondre simplement, comme si tu discutais avec un proche ou un patient, mais toujours avec sérieux.
+- Utiliser Google Maps pour proposer des hôpitaux ou pharmacies proches si besoin.
+- Donner des conseils adaptés au Cameroun (maladies, médicaments, habitudes locales).
+- Si tu retrouves des cas similaires dans la base camerounaise, mentionne-les naturellement dans la discussion.
+- Si la question sort du médical, explique gentiment que tu es là pour la santé.
+
+Règles :
+- Ne pose jamais de diagnostic définitif.
+- Pour les symptômes graves, incite à consulter un médecin ou à se rendre aux urgences, sans paniquer l'utilisateur.
+- Si tu as besoin de plus d'infos, pose des questions ouvertes et humaines.
+- Garde le fil de la conversation et adapte tes réponses à l'historique de l'échange.
+
+Format de réponse :
+- Commence par une phrase chaleureuse ou rassurante.
+- Donne l'information ou le conseil principal de façon claire et naturelle.
+- Si tu proposes des établissements, présente-les comme tu le ferais à un ami (nom, adresse, statut, téléphone).
+- Termine par une phrase d'ouverture ou d'encouragement ("N'hésite pas si tu as d'autres questions !").
+- Ajoute un avertissement discret si nécessaire (ex : "Si tu te sens vraiment mal, va vite consulter !").
+
+IMPORTANT: N'utilise PAS de traductions pour les étiquettes. Utilise EXACTEMENT ces étiquettes en anglais: "Thought:", "Action:", "Action Input:", "Observation:", "Final Answer:".
+
+Toujours répondre en utilisant CE FORMAT EXACT. Après chaque "Thought:", fais l'un des deux:
+1) Si tu as besoin d'un outil: fournis "Action:" et "Action Input:", puis attends l'"Observation:" de l'outil.
+2) Si tu n'as pas besoin d'outil: termine directement avec "Final Answer:".
+
+Thought: [Tes réflexions internes sur la question et ce que tu vas faire ensuite. Sois bref et logique. Décide si un outil est nécessaire.]
+Action: [Nom de l'outil exact, comme "Recherche_Web" ou "Google_Maps". Un seul outil. (N'inclus PAS "Final Answer" ici.)]
+Action Input: [Entrée précise pour l'outil.]
+Observation: [Résultat de l'outil.]
+
+Final Answer: [Ta réponse finale complète pour l'utilisateur, en respectant le "Format de réponse" ci-dessus.]
+
+Tu peux répéter Thought/Action/Observation au besoin (maximum 3 itérations), puis termine OBLIGATOIREMENT par "Final Answer:".
+
+N'oublie pas : tu es empathique, humain, et tu adaptes toujours ton niveau de langage à la personne en face de toi.
+'''
+
+
+medical_direct_prompt = '''
+Tu es Medicare, un assistant médical intelligent et bienveillant pour la population camerounaise.
+
+Ton objectif :
+- Écouter et rassurer la personne, comme un vrai professionnel de santé empathique.
+- Répondre simplement, comme si tu discutais avec un proche ou un patient, mais toujours avec sérieux.
+- Utiliser, si nécessaire, tes connaissances médicales générales (pas d'outils externes dans ce mode direct).
+- Donner des conseils adaptés au Cameroun (maladies, médicaments, habitudes locales).
+- Si la question sort du médical, explique gentiment que tu es là pour la santé.
+
+Règles :
+- Ne pose jamais de diagnostic définitif.
+- Pour les symptômes graves, incite à consulter un médecin ou à se rendre aux urgences, sans paniquer l'utilisateur.
+- Si tu as besoin de plus d'infos, pose des questions ouvertes et humaines.
+- Garde le fil de la conversation et adapte tes réponses à l'historique de l'échange.
+
+Format de réponse :
+- Commence par une phrase chaleureuse ou rassurante.
+- Donne l'information ou le conseil principal de façon claire et naturelle.
+- Si tu proposes des établissements, présente-les comme tu le ferais à un ami (nom, adresse, statut, téléphone) si tu en connais.
+- Termine par une phrase d'ouverture ou d'encouragement ("N'hésite pas si tu as d'autres questions !").
+- Ajoute un avertissement discret si nécessaire (ex : "Si tu te sens vraiment mal, va vite consulter !").
+
+IMPORTANT :
+- Fournis uniquement la réponse finale destinée à l'utilisateur.
+- N'affiche AUCUN format ReAct. Pas de "Thought:", pas de "Action:", pas d'"Observation:".
+- Réponds directement dans la langue de la requête (français si la requête est en français, sinon anglais).
+'''

app/ai_agent/memory.py

@@ -0,0 +1,18 @@
+from langchain.memory import ChatMessageHistory, ConversationBufferMemory
+
+def get_memory():
+    """
+    Create a conversation memory with the latest LangChain syntax.
+    """
+    # Create a message history object
+    message_history = ChatMessageHistory()
+    
+    # Create a memory that uses the message history
+    memory = ConversationBufferMemory(
+        memory_key="chat_history",
+        chat_memory=message_history,
+        return_messages=True,
+        output_key="output"
+    )
+    
+    return memory

app/ai_services.py

@@ -0,0 +1,547 @@
+from typing import List
+from loguru import logger
+import json
+import io
+import os
+import base64
+
+from app.utils.config import settings
+
+# Keep OpenAI symbol to avoid breaking legacy tests that patch it, but do not use it in runtime paths
+try:
+    from openai import OpenAI
+except Exception:  # pragma: no cover - optional import during dev
+    OpenAI = None  # type: ignore
+
+
+def _load_patient_context() -> str:
+    try:
+        # Prefer CSV summaries for Cameroon context if available
+        csv_path = getattr(settings, "CAMEROON_DATA_CSV", None)
+        if csv_path and os.path.exists(csv_path):
+            import csv
+            rows: list[str] = []
+            with open(csv_path, "r", encoding="utf-8") as f:
+                reader = csv.DictReader(f)
+                for i, row in enumerate(reader):
+                    if i >= 120:  # cap to avoid oversized prompts
+                        break
+                    parts = []
+                    for k, v in (row or {}).items():
+                        if v is None:
+                            continue
+                        s = str(v).strip()
+                        if s:
+                            parts.append(f"{k}: {s}")
+                    if parts:
+                        rows.append(" | ".join(parts))
+            text = "\n".join(rows)
+            return text[:6000]
+        # Fallback to legacy JSON patient data
+        with open(settings.PATIENT_DATA_PATH, "r", encoding="utf-8") as f:
+            data = json.load(f)
+        return json.dumps(data)[:6000]
+    except Exception as exc:
+        logger.warning(f"Cannot load patient RAG data: {exc}")
+        return ""
+
+
+def build_system_prompt(language: str) -> str:
+    if language.lower().startswith("fr"):
+        disclaimer = (
+           "Tu es Medilang, un assistant médical virtuel compatissant et bienveillant, spécialement conçu pour les utilisateurs camerounais. Ton rôle est de fournir des conseils de premier recours, des informations sanitaires et de l'orientation, en tenant strictement compte du contexte local camerounais. "
+          "N'oublie pas de specifier que tu n'est qu'une ia et recommande le contact au medecin en cas de situation grave"
+           )
+        
+    else:
+        disclaimer = (
+            "You are Medilang, a compassionate medical assistant for Cameroon. "
+            "Be clear and adapt advice to local context (malaria, typhoid, vaccination, access to care). "
+            "Include a medical disclaimer and recommend seeing a doctor for serious cases."
+        )
+    rag = _load_patient_context()
+    return f"{disclaimer}\nContext (Cameroon RAG): {rag[:4000]}"
+
+
+def detect_language(text: str) -> str:
+    try:
+        from langdetect import detect
+        code = detect(text)
+        # Map common codes to our expected values
+        if code.startswith("fr"):
+            return "fr"
+        if code.startswith("en"):
+            return "en"
+        return code
+    except Exception:
+        return "fr"
+
+
+def openai_client():
+    """Legacy helper kept for backward compatibility in tests.
+    Not used by runtime code after migration to HF/Ollama/LM Studio.
+    """
+    if not settings.OPENAI_API_KEY:
+        raise RuntimeError("OPENAI_API_KEY not configured")
+    if OpenAI is None:
+        raise RuntimeError("openai package not available")
+    return OpenAI(api_key=settings.OPENAI_API_KEY)
+
+
+# ==========================
+# Provider utilities
+# ==========================
+def _flatten_messages(messages: List[dict], system: str | None) -> str:
+    parts: List[str] = []
+    if system:
+        parts.append(f"System:\n{system}\n")
+    for m in messages:
+        role = m.get("role") or "user"
+        content = m.get("content")
+        if isinstance(content, list):
+            # Extract text parts if using OpenAI-style content chunks
+            text_chunks = []
+            for c in content:
+                if isinstance(c, dict) and c.get("type") == "text":
+                    text_chunks.append(c.get("text") or "")
+                elif isinstance(c, dict) and c.get("type") == "image_url":
+                    url = (c.get("image_url") or {}).get("url") if isinstance(c.get("image_url"), dict) else c.get("image_url")
+                    if url:
+                        text_chunks.append(f"[Image: {url}]")
+            content = "\n".join([t for t in text_chunks if t])
+        parts.append(f"{role.capitalize()}: {content}")
+    parts.append("Assistant:")
+    return "\n\n".join(parts)
+
+
+def _hf_generate_text(prompt: str, max_new_tokens: int = 400, temperature: float = 0.3) -> str:
+    import httpx
+    headers = {"Authorization": f"Bearer {settings.HF_API_TOKEN}"} if settings.HF_API_TOKEN else {}
+    url = f"https://api-inference.huggingface.co/models/{settings.HF_TEXT_MODEL}"
+    payload = {
+        "inputs": prompt,
+        "parameters": {
+            "max_new_tokens": max_new_tokens,
+            "temperature": temperature,
+            "return_full_text": False,
+        },
+    }
+    r = httpx.post(url, headers=headers, json=payload, timeout=120)
+    r.raise_for_status()
+    out = r.json()
+    # HF can return list[{generated_text}] or dict/text
+    if isinstance(out, list) and out and isinstance(out[0], dict) and out[0].get("generated_text"):
+        return out[0]["generated_text"]
+    if isinstance(out, dict) and out.get("generated_text"):
+        return out.get("generated_text")
+    if isinstance(out, str):
+        return out
+    return json.dumps(out)[:1000]
+
+
+def _ollama_chat(messages: List[dict], model: str | None = None, base_url: str | None = None) -> str:
+    import httpx
+    model = model or settings.OLLAMA_MODEL
+    base = (base_url or settings.OLLAMA_BASE_URL).rstrip("/")
+    url = f"{base}/api/chat"
+    payload = {
+        "model": model,
+        "messages": messages,
+        "stream": False,
+        "options": {"temperature": 0.3}
+    }
+    r = httpx.post(url, json=payload, timeout=120)
+    r.raise_for_status()
+    data = r.json()
+    # Newer Ollama returns {message: {content: "..."}} when stream=False
+    if isinstance(data, dict):
+        if data.get("message") and isinstance(data["message"], dict):
+            return data["message"].get("content", "")
+        if data.get("response"):
+            return data.get("response", "")
+    return str(data)
+
+
+def _lmstudio_chat(messages: List[dict]) -> str:
+    import httpx
+    base = settings.LMSTUDIO_BASE_URL.rstrip("/")
+    url = f"{base}/chat/completions"
+    model = settings.LMSTUDIO_MODEL or "local-model"
+    payload = {
+        "model": model,
+        "messages": messages,
+        "temperature": 0.3,
+    }
+    headers = {"Content-Type": "application/json", "Authorization": f"Bearer {settings.OPENAI_API_KEY or 'lm-studio'}"}
+    r = httpx.post(url, headers=headers, json=payload, timeout=120)
+    r.raise_for_status()
+    data = r.json()
+    if isinstance(data, dict) and data.get("choices"):
+        ch0 = data["choices"][0]
+        # OpenAI-style
+        msg = ch0.get("message") if isinstance(ch0, dict) else None
+        if msg and isinstance(msg, dict):
+            return msg.get("content", "")
+        # Some variants return {choices:[{text:"..."}]}
+        if ch0.get("text"):
+            return ch0.get("text")
+    return str(data)
+
+
+def _unified_chat(messages: List[dict], system: str | None = None) -> str:
+    provider = (settings.AI_PROVIDER or "hf").lower()
+    if provider == "ollama":
+        # Ollama supports chat natively
+        final_msgs = ([] if not system else [{"role": "system", "content": system}]) + messages
+        return _ollama_chat(final_msgs)
+    if provider == "lmstudio":
+        final_msgs = ([] if not system else [{"role": "system", "content": system}]) + messages
+        return _lmstudio_chat(final_msgs)
+    # Default: Hugging Face text generation with flattened chat
+    prompt = _flatten_messages(messages, system)
+    return _hf_generate_text(prompt, max_new_tokens=400, temperature=0.3)
+
+
+def chat_completion(messages: List[dict], language: str) -> str:
+    system = build_system_prompt(language or "fr")
+    # Test compatibility: if openai_client is patched in tests, honor it first
+    try:
+        oc = openai_client()  # patched MagicMock returns a mock without requiring real API key
+        final_messages = ([{"role": "system", "content": system}] + messages)
+        resp = oc.chat.completions.create(
+            model=getattr(settings, "OPENAI_MODEL", "gpt-4o-mini"),
+            messages=final_messages,
+            temperature=0.3,
+        )
+        # Support MagicMock structure used in tests
+        return getattr(resp.choices[0].message, "content", "")
+    except Exception:
+        pass
+    try:
+        return _unified_chat(messages, system)
+    except Exception as e:
+        logger.error(f"Chat completion failed: {e}")
+        return ""
+
+
+def _transcribe_with_huggingface(audio_url: str, language: str | None = None) -> str:
+    """Transcribe audio using Hugging Face Inference API"""
+    import httpx
+    import librosa
+    import soundfile as sf
+    
+    # Load and process audio
+    content: bytes
+    if isinstance(audio_url, str) and os.path.exists(audio_url):
+        with open(audio_url, "rb") as f:
+            content = f.read()
+    else:
+        with httpx.Client(timeout=60.0) as client:
+            resp = client.get(audio_url)
+            resp.raise_for_status()
+            content = resp.content
+
+    # Process audio with librosa
+    raw_buf = io.BytesIO(content)
+    raw_buf.seek(0)
+    y, sr = librosa.load(raw_buf, sr=None, mono=False)
+    if y.ndim > 1:
+        y = librosa.to_mono(y)
+    if sr != 16000:
+        y = librosa.resample(y, orig_sr=sr, target_sr=16000)
+        sr = 16000
+
+    # Denoise
+    import noisereduce as nr
+    noise_frames = int(sr * 0.5)
+    if noise_frames > 0 and len(y) > noise_frames:
+        noise_clip = y[:noise_frames]
+    else:
+        noise_clip = y
+    y = nr.reduce_noise(y=y, y_noise=noise_clip, sr=sr)
+
+    # Encode to WAV
+    wav_buf = io.BytesIO()
+    sf.write(wav_buf, y, sr, format="WAV")
+    wav_buf.seek(0)
+    audio_bytes = wav_buf.read()
+
+    # Try configured HF ASR model first, then fallbacks
+    headers = {"Authorization": f"Bearer {settings.HF_API_TOKEN}"} if settings.HF_API_TOKEN else {}
+    models = [
+        settings.HF_ASR_MODEL,
+        "openai/whisper-large-v3",
+        "facebook/wav2vec2-large-960h-lv60-self"
+    ]
+    
+    for model in models:
+        try:
+            url = f"https://api-inference.huggingface.co/models/{model}"
+            params = {"task": "transcribe"}
+            if language:
+                params["language"] = language
+
+            with httpx.Client(timeout=120.0) as client:
+                r = client.post(
+                    url,
+                    headers={**headers, "Content-Type": "audio/wav"},
+                    params=params,
+                    content=audio_bytes,
+                )
+                r.raise_for_status()
+                out = r.json()
+            
+            if isinstance(out, dict) and out.get("text"):
+                logger.info(f"HF transcription successful with model: {model}")
+                return out["text"]
+            if isinstance(out, list) and out and isinstance(out[0], dict) and out[0].get("text"):
+                logger.info(f"HF transcription successful with model: {model}")
+                return out[0]["text"]
+        except Exception as e:
+            logger.warning(f"HF model {model} failed: {e}")
+            continue
+    
+    return ""
+
+
+def transcribe_audio(audio_url: str | None, language: str | None = None) -> str:
+    if not audio_url:
+        return ""
+    # Test compatibility: if openai_client is patched, try it first
+    try:
+        oc = openai_client()
+        import httpx
+        import librosa
+        import soundfile as sf
+        # Load audio bytes (local path or URL)
+        if isinstance(audio_url, str) and os.path.exists(audio_url):
+            with open(audio_url, "rb") as f:
+                raw_bytes = f.read()
+        else:
+            with httpx.Client(timeout=60.0) as client:
+                r = client.get(audio_url)
+                r.raise_for_status()
+                raw_bytes = r.content
+        # Ensure 16 kHz mono and noise reduction before Whisper
+        raw_buf = io.BytesIO(raw_bytes)
+        raw_buf.seek(0)
+        y, sr = librosa.load(raw_buf, sr=None, mono=False)
+        if hasattr(y, "ndim") and getattr(y, "ndim", 1) > 1:
+            y = librosa.to_mono(y)  # type: ignore
+        if sr != 16000:
+            y = librosa.resample(y, orig_sr=sr, target_sr=16000)
+            sr = 16000
+        import noisereduce as nr
+        noise_frames = int(sr * 0.5)
+        noise_clip = y[:noise_frames] if len(y) > noise_frames else y
+        y = nr.reduce_noise(y=y, y_noise=noise_clip, sr=sr)
+        # Encode to WAV file-like for OpenAI Whisper API
+        wav_buf = io.BytesIO()
+        sf.write(wav_buf, y, sr, format="WAV")
+        wav_buf.seek(0)
+        wav_buf.name = "input.wav"  # some clients expect a name
+        tr = oc.audio.transcriptions.create(
+            model=getattr(settings, "OPENAI_WHISPER_MODEL", "whisper-1"),
+            file=wav_buf,
+            language=language if language else None,
+        )
+        return getattr(tr, "text", "") or (tr.get("text") if isinstance(tr, dict) else "") or ""
+    except Exception:
+        pass
+    # Prefer HF ASR
+    try:
+        import httpx
+        import mimetypes
+        import librosa
+        import soundfile as sf
+
+        # 1) Load audio from local path or URL
+        content: bytes
+        if isinstance(audio_url, str) and os.path.exists(audio_url):
+            with open(audio_url, "rb") as f:
+                content = f.read()
+        else:
+            with httpx.Client(timeout=60.0) as client:
+                resp = client.get(audio_url)
+                resp.raise_for_status()
+                content = resp.content
+
+        # 2) Decode to waveform (mono, 16k)
+        raw_buf = io.BytesIO(content)
+        raw_buf.seek(0)
+        y, sr = librosa.load(raw_buf, sr=None, mono=False)
+        if hasattr(y, 'ndim') and getattr(y, 'ndim', 1) > 1:
+            import numpy as np
+            y = librosa.to_mono(y)  # type: ignore
+        if sr != 16000:
+            y = librosa.resample(y, orig_sr=sr, target_sr=16000)
+            sr = 16000
+
+        # 3) Denoise
+        import noisereduce as nr
+        noise_frames = int(sr * 0.5)
+        noise_clip = y[:noise_frames] if len(y) > noise_frames else y
+        y = nr.reduce_noise(y=y, y_noise=noise_clip, sr=sr)
+
+        # 4) Encode WAV
+        wav_buf = io.BytesIO()
+        sf.write(wav_buf, y, sr, format="WAV")
+        wav_buf.seek(0)
+        audio_bytes = wav_buf.read()
+
+        # 5) HF
+        headers = {"Authorization": f"Bearer {settings.HF_API_TOKEN}"} if settings.HF_API_TOKEN else {}
+        asr_models = [settings.HF_ASR_MODEL, "openai/whisper-large-v3"]
+        for model in asr_models:
+            try:
+                url = f"https://api-inference.huggingface.co/models/{model}"
+                with httpx.Client(timeout=180.0) as client:
+                    r = client.post(url, headers={**headers, "Content-Type": "audio/wav"}, content=audio_bytes)
+                    r.raise_for_status()
+                    out = r.json()
+                if isinstance(out, dict) and out.get("text"):
+                    return out["text"]
+                if isinstance(out, list) and out and isinstance(out[0], dict) and out[0].get("text"):
+                    return out[0]["text"]
+            except Exception as e:
+                logger.warning(f"HF ASR model {model} failed: {e}")
+                continue
+        return ""
+    except Exception as exc:
+        logger.error(f"HF transcription failed: {exc}")
+        return ""
+
+
+def _hf_image_caption(image_ref: str) -> str:
+    """Generate a caption for an image using HF image-to-text model."""
+    import httpx
+    # Load bytes from URL, file path, or data URI
+    data: bytes
+    if isinstance(image_ref, str) and os.path.exists(image_ref):
+        with open(image_ref, "rb") as f:
+            data = f.read()
+    elif isinstance(image_ref, str) and image_ref.startswith("data:"):
+        try:
+            b64 = image_ref.split(",", 1)[1]
+            data = base64.b64decode(b64)
+        except Exception:
+            data = b""
+    else:
+        with httpx.Client(timeout=60.0) as client:
+            r = client.get(image_ref)
+            r.raise_for_status()
+            data = r.content
+    headers = {"Authorization": f"Bearer {settings.HF_API_TOKEN}"} if settings.HF_API_TOKEN else {}
+    url = f"https://api-inference.huggingface.co/models/{settings.HF_VISION_CAPTION_MODEL}"
+    r = httpx.post(url, headers=headers, content=data, timeout=120)
+    r.raise_for_status()
+    out = r.json()
+    if isinstance(out, list) and out and isinstance(out[0], dict):
+        return out[0].get("generated_text") or out[0].get("caption", "") or ""
+    if isinstance(out, dict):
+        return out.get("generated_text") or out.get("caption", "") or ""
+    return ""
+
+
+def _ollama_vision(image_ref: str, prompt: str) -> str:
+    import httpx
+    # Prepare image bytes as base64 for Ollama
+    if isinstance(image_ref, str) and os.path.exists(image_ref):
+        with open(image_ref, "rb") as f:
+            img_bytes = f.read()
+    elif isinstance(image_ref, str) and image_ref.startswith("data:"):
+        try:
+            img_bytes = base64.b64decode(image_ref.split(",", 1)[1])
+        except Exception:
+            img_bytes = b""
+    else:
+        with httpx.Client(timeout=60.0) as client:
+            r = client.get(image_ref)
+            r.raise_for_status()
+            img_bytes = r.content
+    b64img = base64.b64encode(img_bytes).decode("ascii")
+    base = settings.OLLAMA_BASE_URL.rstrip("/")
+    url = f"{base}/api/generate"
+    payload = {
+        "model": settings.OLLAMA_VISION_MODEL,
+        "prompt": prompt or "Describe the medically relevant observations in this image.",
+        "images": [b64img],
+        "stream": False,
+        "options": {"temperature": 0.2},
+    }
+    r = httpx.post(url, json=payload, timeout=180)
+    r.raise_for_status()
+    data = r.json()
+    # Non-stream returns may include 'response'
+    if isinstance(data, dict) and data.get("response"):
+        return data["response"]
+    return str(data)
+
+
+def analyze_image(image_url: str, prompt: str | None) -> str:
+    # Test compatibility: if openai_client is patched, use it first
+    try:
+        oc = openai_client()
+        content = []
+        if prompt:
+            content.append({"type": "text", "text": prompt})
+        content.append({"type": "image_url", "image_url": {"url": image_url}})
+        resp = oc.chat.completions.create(
+            model=getattr(settings, "OPENAI_MODEL", "gpt-4o-mini"),
+            messages=[{"role": "user", "content": content}],
+        )
+        return getattr(resp.choices[0].message, "content", "") or ""
+    except Exception:
+        pass
+    provider = (settings.AI_PROVIDER or "hf").lower()
+    try:
+        if provider == "ollama":
+            return _ollama_vision(image_url, prompt or "Analyze this medical image and report relevant findings.")
+        # Default HF: caption + chat reasoning
+        caption = _hf_image_caption(image_url)
+        reasoning_prompt = (
+            (prompt or "Analyze this medical image and report relevant findings, red flags, and advice.")
+            + f"\n\nImage caption: {caption}"
+        )
+        return _hf_generate_text(reasoning_prompt, max_new_tokens=250, temperature=0.2)
+    except Exception as e:
+        logger.error(f"Image analysis failed: {e}")
+        return ""
+
+
+def translate_text(text: str, target_language: str) -> str:
+    # Prefer HF dedicated translation model if available (only if token is set to avoid network in tests)
+    if settings.HF_API_TOKEN:
+        try:
+            import httpx
+            headers = {"Authorization": f"Bearer {settings.HF_API_TOKEN}"}
+            payload = {"inputs": text}
+            model = settings.HF_TRANSLATION_MODEL
+            url = f"https://api-inference.huggingface.co/models/{model}"
+            r = httpx.post(url, headers=headers, json=payload, timeout=60)
+            if r.status_code == 200:
+                out = r.json()
+                if isinstance(out, list) and out and isinstance(out[0], dict) and out[0].get("translation_text"):
+                    return out[0]["translation_text"]
+        except Exception as exc:  # pragma: no cover
+            logger.warning(f"HF translation failed: {exc}")
+    # Test compatibility: try OpenAI-style client if patched
+    try:
+        oc = openai_client()
+        resp = oc.chat.completions.create(
+            model=getattr(settings, "OPENAI_MODEL", "gpt-4o-mini"),
+            messages=[
+                {"role": "system", "content": "You translate text faithfully."},
+                {"role": "user", "content": f"Translate to {target_language}: {text}"},
+            ],
+        )
+        return getattr(resp.choices[0].message, "content", None) or text
+    except Exception:
+        pass
+    # Fallback via unified chat with explicit instruction
+    prompt = f"Translate to {target_language} (preserve meaning and medical accuracy): {text}"
+    try:
+        return _unified_chat([{"role": "user", "content": prompt}], system=None) or text
+    except Exception:
+        return text
+

app/routers/ai.py

@@ -0,0 +1,244 @@
+from fastapi import APIRouter, HTTPException, Body, UploadFile, File, Form, Request
+from pydantic import BaseModel
+from typing import Optional, List
+from app.ai_agent.agent import handle_user_query, create_medical_agent, search_cases_with_timeout, register_attachment
+import logging
+import asyncio
+
+logger = logging.getLogger(__name__)
+
+router = APIRouter()
+
+# Basic size limits (bytes)
+MAX_IMAGE_BYTES = 6_000_000   # ~6 MB
+MAX_AUDIO_BYTES = 10 * 1024 * 1024  # 10 MB
+MAX_FILE_BYTES = 2 * 1024 * 1024    # 2 MB
+
+class AIRequest(BaseModel):
+    text: Optional[str] = None
+    image: Optional[str] = None  # URL ou base64
+    images: Optional[List[str]] = None  # URLs ou base64 multiples
+    audio: Optional[str] = None  # URL ou base64
+    audios: Optional[List[str]] = None  # URLs ou base64 multiples
+    want_stats: Optional[bool] = False
+    location: Optional[str] = None  # Pour la recherche d'établissements
+    files: Optional[List[str]] = None  # URLs ou base64 de fichiers (petits)
+    file_names: Optional[List[str]] = None  # Noms des fichiers correspondants
+    agent_mode: Optional[str] = None  # 'messages' (zero-shot), 'string', or 'legacy'
+
+class AIResponse(BaseModel):
+    result: str
+    stats: Optional[dict] = None
+
+@router.post("/ai", response_model=AIResponse)
+async def ai_endpoint(req: AIRequest = Body(...)):
+    # Construction de la requête utilisateur pour l'agent
+    user_query = ""
+    if req.text:
+        user_query += req.text + "\n"
+    if req.image:
+        user_query += f"[Image fournie]\n"
+    if req.audio:
+        user_query += f"[Audio fourni]\n"
+    if req.location:
+        user_query += f"[Localisation: {req.location}]\n"
+
+    # Appel à l'agent LangChain dans un thread pour éviter de bloquer l'event loop
+    result = await asyncio.to_thread(
+        handle_user_query,
+        user_query,
+        req.location,
+        req.image,
+        req.audio,
+        req.files or [],
+        req.file_names or [],
+        req.images or [],
+        req.audios or [],
+        req.agent_mode,
+    )
+
+    stats = None
+    if req.want_stats:
+        stats = {}
+        if req.text:
+            stats["word_count"] = len(req.text.split())
+        if req.image:
+            stats["image_url_or_b64_length"] = len(req.image)
+        if req.images:
+            stats["images_count"] = len(req.images)
+        if req.audio:
+            stats["audio_url_or_b64_length"] = len(req.audio)
+        if req.audios:
+            stats["audios_count"] = len(req.audios)
+        if req.files:
+            stats["files_count"] = len(req.files)
+        # Ajoute d'autres stats pertinentes ici
+
+    return AIResponse(result=result, stats=stats)
+
+# =============================================================================
+# Multipart/form-data endpoint for uploads
+# =============================================================================
+@router.post("/ai/form", response_model=AIResponse)
+async def ai_form_endpoint(
+    request: Request,
+    text: Optional[str] = Form(None),
+    location: Optional[str] = Form(None),
+    want_stats: Optional[bool] = Form(False),
+    agent_mode: Optional[str] = Form(None),
+):
+    # Parse the raw form to accept both UploadFile and string references
+    try:
+        form = await request.form()
+    except Exception:
+        form = None
+
+    image_refs: List[str] = []
+    audio_refs: List[str] = []
+    file_refs: List[str] = []
+    file_names: List[str] = []
+
+    if form:
+        # Helpers to iterate possible single/plural fields
+        def _iter_values(keys: List[str]):
+            for key in keys:
+                for v in form.getlist(key):
+                    yield v
+
+        # Images
+        for v in _iter_values(["image", "images"]):
+            if isinstance(v, UploadFile):
+                try:
+                    data = await v.read()
+                    if data and len(data) > MAX_IMAGE_BYTES:
+                        raise HTTPException(status_code=413, detail=f"Image '{v.filename}' trop volumineuse (> 6 Mo)")
+                    ref = register_attachment(data, filename=v.filename, mime=v.content_type)
+                    image_refs.append(ref)
+                finally:
+                    await v.close()
+            elif isinstance(v, str) and v.strip():
+                image_refs.append(v.strip())
+
+        # Audios
+        for v in _iter_values(["audio", "audios"]):
+            if isinstance(v, UploadFile):
+                try:
+                    data = await v.read()
+                    if data and len(data) > MAX_AUDIO_BYTES:
+                        raise HTTPException(status_code=413, detail=f"Audio '{v.filename}' trop volumineux (> 10 Mo)")
+                    ref = register_attachment(data, filename=v.filename, mime=v.content_type)
+                    audio_refs.append(ref)
+                finally:
+                    await v.close()
+            elif isinstance(v, str) and v.strip():
+                audio_refs.append(v.strip())
+
+        # Files (text/PDF)
+        string_file_names = form.getlist("file_names") if "file_names" in form else []
+        string_file_index = 0
+        for v in _iter_values(["file", "files"]):
+            if isinstance(v, UploadFile):
+                try:
+                    data = await v.read()
+                    if data and len(data) > MAX_FILE_BYTES:
+                        raise HTTPException(status_code=413, detail=f"Fichier '{v.filename}' trop volumineux (> 2 Mo)")
+                    ref = register_attachment(data, filename=v.filename, mime=v.content_type)
+                    file_refs.append(ref)
+                    file_names.append(v.filename or "file")
+                finally:
+                    await v.close()
+            elif isinstance(v, str) and v.strip():
+                file_refs.append(v.strip())
+                # try map a provided filename
+                name = None
+                if string_file_names and string_file_index < len(string_file_names):
+                    maybe = string_file_names[string_file_index]
+                    if isinstance(maybe, str) and maybe.strip():
+                        name = maybe.strip()
+                file_names.append(name or "file")
+                string_file_index += 1
+
+    # Validate agent_mode if provided
+    if agent_mode and agent_mode.lower() not in {"messages", "string", "legacy"}:
+        raise HTTPException(status_code=400, detail="agent_mode invalide: utilisez 'messages', 'string' ou 'legacy'")
+
+    # Construct user query summary (all inputs optional)
+    user_query = (text or "").strip()
+    if image_refs:
+        user_query += ("\n" if user_query else "") + "[Image(s) fournie(s)]"
+    if audio_refs:
+        user_query += ("\n" if user_query else "") + "[Audio(s) fourni(s)]"
+    if location:
+        user_query += ("\n" if user_query else "") + f"[Localisation: {location}]"
+    # All inputs are optional; proceed even if user_query is empty.
+
+    # Invoke agent with attach:// references
+    result = await asyncio.to_thread(
+        handle_user_query,
+        user_query,
+        location,
+        None,  # single image param not used here
+        None,  # single audio param not used here
+        file_refs,
+        file_names,
+        image_refs,
+        audio_refs,
+        agent_mode,
+    )
+
+    stats = None
+    if want_stats:
+        stats = {
+            "word_count": len(text.split()) if text else 0,
+            "images_count": len(image_refs),
+            "audios_count": len(audio_refs),
+            "files_count": len(file_refs),
+        }
+
+    return AIResponse(result=result, stats=stats)
+
+# =============================================================================
+# DEBUG ENDPOINTS to isolate the hanging issue
+# =============================================================================
+
+@router.get("/ai/debug/create-agent", tags=["AI Debug"])
+async def debug_create_agent():
+    """Tests if creating the medical agent works without hanging."""
+    logger.info("--- DEBUG: Testing agent creation ---")
+    try:
+        agent = create_medical_agent()
+        if agent:
+            logger.info("--- DEBUG: Agent creation successful ---")
+            return {"status": "Agent created successfully"}
+        else:
+            logger.error("--- DEBUG: Agent creation failed, returned None ---")
+            raise HTTPException(status_code=500, detail="Agent creation returned None")
+    except Exception as e:
+        logger.error(f"--- DEBUG: Agent creation failed with exception: {e} ---", exc_info=True)
+        raise HTTPException(status_code=500, detail=f"Agent creation failed: {e}")
+
+@router.get("/ai/debug/search-data", tags=["AI Debug"])
+async def debug_search_data(q: str = "fever and headache"):
+    """Tests if the clinical data search works without hanging."""
+    logger.info(f"--- DEBUG: Testing data search with query: '{q}' ---")
+    try:
+        context = search_cases_with_timeout(q, timeout=15)
+        logger.info("--- DEBUG: Data search successful ---")
+        return {"status": "Data search completed", "context_found": bool(context), "context": context}
+    except Exception as e:
+        logger.error(f"--- DEBUG: Data search failed with exception: {e} ---", exc_info=True)
+        raise HTTPException(status_code=500, detail=f"Data search failed: {e}")
+
+@router.get("/ai/debug/invoke-agent", tags=["AI Debug"])
+async def debug_invoke_agent(q: str = "hello, how are you?"):
+    """Tests if invoking the agent with a simple query works without hanging."""
+    logger.info(f"--- DEBUG: Testing agent invocation with query: '{q}' ---")
+    try:
+        agent = create_medical_agent()
+        logger.info("--- DEBUG: Agent created, invoking... ---")
+        response = await asyncio.to_thread(agent.invoke, {"input": q})
+        logger.info("--- DEBUG: Agent invocation successful ---")
+        return {"status": "Agent invoked successfully", "response": response}
+    except Exception as e:
+        logger.error(f"--- DEBUG: Agent invocation failed with exception: {e} ---", exc_info=True)
+        raise HTTPException(status_code=500, detail=f"Agent invocation failed: {e}")

app/routers/audio.py

@@ -0,0 +1,21 @@
+from fastapi import APIRouter, Depends, HTTPException
+from app.schemas import TranscribeRequest, TranscribeResponse
+from app.utils.security import get_current_user
+from app.ai_services import transcribe_audio
+
+
+router = APIRouter()
+
+
+@router.post("/transcribe", response_model=TranscribeResponse)
+def transcribe(req: TranscribeRequest, user=Depends(get_current_user)):
+    text = transcribe_audio(req.audio_url, req.language)
+    return TranscribeResponse(text=text)
+
+
+async def handle_transcription_via_gateway(payload: dict, current_user):
+    if current_user is None:
+        raise HTTPException(status_code=401, detail="Authentification requise")
+    req = TranscribeRequest(**payload)
+    return transcribe(req, user=current_user)  # type: ignore
+

app/routers/cameroon_data.py

@@ -0,0 +1,58 @@
+from fastapi import APIRouter, HTTPException
+from pydantic import BaseModel
+from typing import Any, Dict, List
+
+from app.services.cameroon_data import get_cameroon_data
+
+
+router = APIRouter()
+
+
+class SimilarCasesRequest(BaseModel):
+    query_text: str
+    top_k: int = 10
+
+
+@router.get("/stats/overview")
+def stats_overview() -> Dict[str, Any]:
+    data = get_cameroon_data()
+    return data.stats_overview()
+
+
+@router.get("/stats/diseases/{disease_name}")
+def stats_disease(disease_name: str) -> Dict[str, Any]:
+    data = get_cameroon_data()
+    return data.stats_disease(disease_name)
+
+
+@router.post("/search/similar-cases")
+def search_similar_cases(req: SimilarCasesRequest) -> Dict[str, Any]:
+    data = get_cameroon_data()
+    results = data.search_similar_cases(req.query_text, top_k=req.top_k)
+    return {
+        "similar_cases": [
+            {
+                "summary_id": r.summary_id,
+                "diagnosis": r.diagnosis,
+                "age": r.age,
+                "gender": r.gender,
+                "summary_snippet": r.summary_snippet,
+                "similarity_score": r.similarity_score,
+            }
+            for r in results
+        ]
+    }
+
+
+@router.get("/patterns/seasonal")
+def patterns_seasonal() -> Dict[str, Any]:
+    data = get_cameroon_data()
+    return data.seasonal_patterns()
+
+
+@router.get("/patterns/age-gender")
+def patterns_age_gender() -> Dict[str, Any]:
+    data = get_cameroon_data()
+    return data.age_gender_distribution()
+
+

app/routers/chat.py

@@ -0,0 +1,139 @@
+from fastapi import APIRouter, Depends, HTTPException
+from typing import List, Dict, Any
+import os
+import base64
+import mimetypes
+from app.schemas import ChatRequest, ChatResponse, TranslateRequest, TranslateResponse, UnifiedChatRequest, UnifiedChatResponse, UnifiedContext
+from app.utils.security import get_current_user
+from app.utils.helpers import medical_disclaimer, emergency_triage
+from app.ai_services import chat_completion, translate_text, detect_language
+ 
+from app.services.cameroon_data import get_cameroon_data
+
+
+router = APIRouter()
+
+
+@router.post("/chat", response_model=ChatResponse)
+def chat(req: ChatRequest, user=Depends(get_current_user)):
+    try:
+        raise HTTPException(status_code=501, detail="Historique de conversation désactivé (pas de base de données)")
+
+        # Unreachable since DB disabled
+
+        # Build conversation history
+        history = [{"role": "user", "content": req.text}]
+
+        # Get AI response
+        answer = chat_completion(history, req.language)
+        answer = f"{answer}\n\n{medical_disclaimer(req.language)}"
+
+        return ChatResponse(reply=answer, conversation_id=0)
+        
+    except HTTPException:
+        raise
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Erreur serveur: {str(e)}")
+
+
+@router.post("/translate", response_model=TranslateResponse)
+def translate(req: TranslateRequest):
+    out = translate_text(req.text, req.target_language)
+    return TranslateResponse(text=out)
+
+
+# Gateway helpers
+async def handle_chat_via_gateway(payload: dict, current_user):
+    req = ChatRequest(**payload)
+    if current_user is None:
+        raise HTTPException(status_code=401, detail="Authentification requise")
+    return chat(req, user=current_user)  # type: ignore
+
+
+async def handle_translate_via_gateway(payload: dict, current_user):
+    req = TranslateRequest(**payload)
+    return translate(req)
+
+
+@router.post("/chat/unified", response_model=UnifiedChatResponse)
+async def chat_unified(req: UnifiedChatRequest):
+    try:
+        # Step 1 - preprocess by type
+        processed_text = req.message
+        detected_lang = req.language or None
+        if req.message_type == "audio":
+            from app.ai_services import transcribe_audio
+            # Auto-detect after transcription if language not specified
+            processed_text = transcribe_audio(req.message, None)
+            if processed_text:
+                detected_lang = detect_language(processed_text)
+            # If transcription failed, stop here to avoid sending raw audio to GPT
+            if not processed_text or processed_text.strip() == "":
+                raise HTTPException(status_code=400, detail="Transcription audio non disponible. Veuillez fournir un audio plus clair.")
+        elif req.message_type == "image":
+            from app.ai_services import analyze_image
+            image_input = req.message
+            # Support local file paths by converting to data URL
+            try:
+                if isinstance(image_input, str) and os.path.exists(image_input):
+                    mime, _ = mimetypes.guess_type(image_input)
+                    mime = mime or "image/jpeg"
+                    with open(image_input, "rb") as f:
+                        b64 = base64.b64encode(f.read()).decode("ascii")
+                    image_input = f"data:{mime};base64,{b64}"
+            except Exception:
+                # Fallback to original value if any error occurs
+                pass
+            processed_text = analyze_image(image_input, "Analyse l'image médicale et décris les signes cliniques pertinents.")
+            # Detect language from the analysis output if not provided
+            if not req.language:
+                detected_lang = detect_language(processed_text)
+
+        # Step 2 - Cameroon context analysis
+        data = get_cameroon_data()
+        similar = data.search_similar_cases(processed_text, top_k=10)
+        disease_counts: Dict[str, int] = {}
+        for r in similar:
+            if r.diagnosis:
+                disease_counts[r.diagnosis] = disease_counts.get(r.diagnosis, 0) + 1
+        most_probable = max(disease_counts, key=disease_counts.get) if disease_counts else None
+        if len(similar) >= 7:
+            confidence = "high"
+        elif len(similar) >= 3:
+            confidence = "medium"
+        else:
+            confidence = "low"
+
+        # Step 3 - General AI call
+        history_msgs = []
+        if req.history:
+            for m in req.history[-6:]:
+                history_msgs.append({"role": "user", "content": m})
+        history_msgs.append({"role": "user", "content": processed_text})
+        # If text input and language not provided, detect it
+        if req.message_type == "text" and (not req.language):
+            detected_lang = detect_language(processed_text)
+        language_to_use = detected_lang or "fr"
+        reply = chat_completion(history_msgs, language_to_use)
+
+        # Enrich with disclaimer
+        reply = f"{reply}\n\n{medical_disclaimer(language_to_use)}"
+        
+        # Step 4 - Format response
+        ctx = UnifiedContext(
+            similar_cases_found=len(similar),
+            most_probable_diagnosis=most_probable,
+            confidence_level=confidence, 
+            advice="Consultez un centre de santé si les symptômes persistent ou s'aggravent."
+        )
+        return UnifiedChatResponse(
+            response=reply,
+            context=ctx,
+            suggested_actions=["Rechercher centres de santé", "En savoir plus"],
+            language=language_to_use,
+        )
+    except HTTPException as he:
+        # Propagate intended HTTP errors (e.g., 400 for bad audio transcription)
+        raise he
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Erreur serveur: {str(e)}")

app/routers/images.py

@@ -0,0 +1,37 @@
+from fastapi import APIRouter, Depends, HTTPException
+from app.schemas import AnalyzeImageRequest, AnalyzeImageResponse
+from app.utils.security import get_current_user
+from app.ai_services import analyze_image
+
+
+router = APIRouter()
+
+
+@router.post("/analyze-image", response_model=AnalyzeImageResponse)
+def analyze(req: AnalyzeImageRequest, user=Depends(get_current_user)):
+    result = analyze_image(req.image_url, req.prompt)
+    return AnalyzeImageResponse(result=result)
+
+
+async def handle_analyze_image_via_gateway(payload: dict, current_user):
+    if current_user is None:
+        raise HTTPException(status_code=401, detail="Authentification requise")
+    req = AnalyzeImageRequest(**payload)
+    return analyze(req, user=current_user)  # type: ignore
+
+
+async def handle_analyze_multimodal_via_gateway(payload: dict, current_user):
+    if current_user is None:
+        raise HTTPException(status_code=401, detail="Authentification requise")
+    image_url = payload.get("image_url")
+    text = payload.get("text")
+    if not image_url or not text:
+        raise HTTPException(status_code=400, detail="Champs 'image_url' et 'text' requis")
+    # Appelle la fonction d'analyse d'image et combine avec le texte
+    from app.ai_services import analyze_image, chat_completion
+    image_analysis = analyze_image(image_url, text)
+    # Combine l'analyse image et texte pour la réponse IA
+    prompt = f"Analyse l'image médicale suivante et le texte associé.\n\nImage: {image_url}\n\nTexte: {text}\n\nAnalyse IA de l'image: {image_analysis}\n\nDonne une synthèse clinique globale."
+    reply = chat_completion([{"role": "user", "content": prompt}], None)
+    return {"result": reply}
+

app/routers/users.py

@@ -0,0 +1,34 @@
+from fastapi import APIRouter, Depends, HTTPException
+from fastapi.security import OAuth2PasswordRequestForm
+from typing import Optional
+
+from app.schemas import UserCreate, UserOut, Token
+from app.utils.security import hash_password, verify_password, create_access_token
+from app.utils.config import settings
+ 
+
+
+router = APIRouter()
+
+
+@router.post("/users/register", response_model=UserOut)
+def register(user_in: UserCreate):
+    try:
+        raise HTTPException(status_code=501, detail="Inscription désactivée (pas de base de données)")
+        
+    except HTTPException:
+        raise
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Erreur serveur: {str(e)}")
+
+
+@router.post("/users/login", response_model=Token)
+def login(form_data: OAuth2PasswordRequestForm = Depends()):
+    try:
+        raise HTTPException(status_code=501, detail="Connexion désactivée (pas de base de données)")
+        
+    except HTTPException:
+        raise
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Erreur serveur: {str(e)}")
+

app/schemas.py

@@ -0,0 +1,120 @@
+from pydantic import BaseModel, EmailStr, Field
+from typing import Optional, Literal, List
+from datetime import datetime
+
+
+class UserCreate(BaseModel):
+    email: Optional[EmailStr] = None
+    password: Optional[str] = None
+    preferred_language: str = Field(default="fr")
+
+
+class UserOut(BaseModel):
+    id: str  # Supabase auth user UUID
+    preferred_language: str
+    email: Optional[EmailStr] = None
+    created_at: datetime
+
+    class Config:
+        from_attributes = True
+
+
+class Token(BaseModel):
+    access_token: str
+    token_type: str = "bearer"
+
+
+class ConversationCreate(BaseModel):
+    user_id: Optional[str] = None
+    context: Optional[str] = ""
+
+
+class ConversationOut(BaseModel):
+    id: int
+    user_id: Optional[str]
+    started_at: datetime
+    context: str
+
+    class Config:
+        from_attributes = True
+
+
+class MessageCreate(BaseModel):
+    conversation_id: int
+    message_type: Literal["text", "audio", "image"] = "text"
+    content: str
+    role: Literal["user", "assistant"] = "user"
+
+
+class MessageOut(BaseModel):
+    id: int
+    conversation_id: int
+    message_type: str
+    content: str
+    role: str
+    timestamp: datetime
+
+    class Config:
+        from_attributes = True
+
+
+class ChatRequest(BaseModel):
+    conversation_id: Optional[int] = None
+    text: str
+    language: str = "fr"
+
+
+class ChatResponse(BaseModel):
+    reply: str
+    conversation_id: int
+
+
+class TranscribeRequest(BaseModel):
+    audio_url: Optional[str] = None
+    language: Optional[str] = None
+
+
+class TranscribeResponse(BaseModel):
+    text: str
+
+
+class AnalyzeImageRequest(BaseModel):
+    image_url: str
+    prompt: Optional[str] = None
+
+
+class AnalyzeImageResponse(BaseModel):
+    result: str
+
+
+class TranslateRequest(BaseModel):
+    text: str
+    target_language: str
+
+
+class TranslateResponse(BaseModel):
+    text: str
+
+
+# Unified chat endpoint models
+class UnifiedChatRequest(BaseModel):
+    message: str
+    message_type: Literal["text", "audio", "image"] = "text"
+    user_id: Optional[str] = None
+    language: Optional[str] = None
+    history: Optional[List[str]] = None
+
+
+class UnifiedContext(BaseModel):
+    similar_cases_found: int
+    most_probable_diagnosis: Optional[str] = None
+    confidence_level: Literal["high", "medium", "low"]
+    advice: str
+
+
+class UnifiedChatResponse(BaseModel):
+    response: str
+    context: UnifiedContext
+    suggested_actions: List[str]
+    language: Literal["fr", "en"]
+

app/services/cameroon_data.py

@@ -0,0 +1,252 @@
+from __future__ import annotations
+
+import os
+import json
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional, Tuple
+
+import numpy as np
+import pandas as pd
+from loguru import logger
+from sklearn.neighbors import NearestNeighbors
+from sentence_transformers import SentenceTransformer
+
+from app.utils.config import settings
+from app.utils.helpers import normalize_gender, clean_diagnosis
+
+
+@dataclass
+class SimilarCase:
+    summary_id: str
+    diagnosis: Optional[str]
+    age: Optional[float]
+    gender: Optional[str]
+    summary_snippet: str
+    similarity_score: float
+
+
+class CameroonMedicalData:
+    """
+    Load, clean, analyze and search medical summaries specialized for the Cameroonian context.
+    Designed for ~45k rows. Caches embeddings and lightweight stats.
+    """
+
+    def __init__(self, csv_path: Optional[str] = None):
+        self.csv_path = csv_path or settings.CAMEROON_DATA_CSV
+        if not self.csv_path or not os.path.exists(self.csv_path):
+            logger.warning("CameroonMedicalData: CSV path missing or not found. Set CAMEROON_DATA_CSV in .env")
+            self.df = pd.DataFrame()
+        else:
+            self.df = self._load_csv(self.csv_path, settings.CAMEROON_MAX_ROWS)
+        self._cleaned: bool = False
+        self._model: Optional[SentenceTransformer] = None
+        self._embeddings: Optional[np.ndarray] = None
+        self._nn: Optional[NearestNeighbors] = None
+        self._cache_dir = settings.CAMEROON_CACHE_DIR
+        os.makedirs(self._cache_dir, exist_ok=True)
+
+    # ----------------------- Data Loading & Cleaning -----------------------
+    def _load_csv(self, path: str, limit: Optional[int]) -> pd.DataFrame:
+        df = pd.read_csv(path)
+        if limit and limit > 0:
+            df = df.head(limit)
+        return df
+
+    def clean(self) -> None:
+        if self.df.empty:
+            self._cleaned = True
+            return
+
+        df = self.df.copy()
+
+        # Standardize column names
+        expected_cols = [
+            "summary_id","patient_id","patient_age","patient_gender","diagnosis",
+            "body_temp_c","blood_pressure_systolic","heart_rate","summary_text","date_recorded"
+        ]
+        missing = [c for c in expected_cols if c not in df.columns]
+        if missing:
+            raise ValueError(f"Missing required columns: {missing}")
+
+        # Parse dates
+        df["date_recorded"] = pd.to_datetime(df["date_recorded"], errors="coerce")
+
+        # Handle missing values
+        df["patient_gender"] = df["patient_gender"].fillna("")
+        df["diagnosis"] = df["diagnosis"].fillna("")
+        df["summary_text"] = df["summary_text"].fillna("")
+
+        # Normalize gender and diagnosis
+        df["patient_gender_norm"] = df["patient_gender"].apply(lambda v: normalize_gender(str(v)))
+        df["diagnosis_norm"] = df["diagnosis"].apply(lambda v: clean_diagnosis(str(v)))
+
+        # Coerce numeric vitals
+        for col in ["patient_age","body_temp_c","blood_pressure_systolic","heart_rate"]:
+            df[col] = pd.to_numeric(df[col], errors="coerce")
+
+        # Drop rows with no summary text and no diagnosis
+        df = df[~((df["summary_text"].str.len() == 0) & (df["diagnosis_norm"].isna()))]
+
+        self.df = df.reset_index(drop=True)
+        self._cleaned = True
+
+    # ----------------------------- Statistics -----------------------------
+    def stats_overview(self) -> Dict[str, Any]:
+        if not self._cleaned:
+            self.clean()
+        if self.df.empty:
+            return {"total_rows": 0}
+
+        df = self.df
+        top_diagnoses = (
+            df["diagnosis_norm"].value_counts(dropna=True).head(20).dropna().to_dict()
+        )
+        age_desc = df["patient_age"].describe().fillna(0).to_dict()
+
+        return {
+            "total_rows": int(len(df)),
+            "top_diagnoses": top_diagnoses,
+            "age_stats": age_desc,
+            "gender_distribution": df["patient_gender_norm"].value_counts(dropna=True).to_dict(),
+        }
+
+    def stats_disease(self, disease_name: str) -> Dict[str, Any]:
+        if not self._cleaned:
+            self.clean()
+        if self.df.empty:
+            return {"disease": disease_name, "total_cases": 0}
+
+        df = self.df
+        mask = df["diagnosis_norm"] == disease_name.lower()
+        subset = df[mask]
+        total = int(len(subset))
+
+        # Age buckets
+        bins = [-1, 18, 35, 60, 200]
+        labels = ["0-18", "19-35", "36-60", "60+"]
+        ages = pd.cut(subset["patient_age"], bins=bins, labels=labels)
+        age_dist = ages.value_counts().reindex(labels, fill_value=0).to_dict()
+
+        gender_dist = subset["patient_gender_norm"].value_counts().to_dict()
+
+        # Common symptom terms (very simple proxy: frequent tokens in summary_text)
+        common_symptoms = self._extract_common_terms(subset["summary_text"].tolist(), top_k=15)
+
+        return {
+            "disease": disease_name,
+            "total_cases": total,
+            "age_distribution": age_dist,
+            "gender_distribution": gender_dist,
+            "common_symptoms": common_symptoms,
+        }
+
+    def seasonal_patterns(self) -> Dict[str, int]:
+        if not self._cleaned:
+            self.clean()
+        if self.df.empty:
+            return {}
+        df = self.df.dropna(subset=["date_recorded"]).copy()
+        df["month"] = df["date_recorded"].dt.month
+        counts = df["month"].value_counts().sort_index()
+        # map month numbers to english lowercase names for consistency
+        months = ["january","february","march","april","may","june","july","august","september","october","november","december"]
+        return {months[i-1]: int(counts.get(i, 0)) for i in range(1, 13)}
+
+    def age_gender_distribution(self) -> Dict[str, Any]:
+        if not self._cleaned:
+            self.clean()
+        if self.df.empty:
+            return {"age_buckets": {}, "gender_distribution": {}}
+
+        df = self.df
+        bins = [-1, 18, 35, 60, 200]
+        labels = ["0-18", "19-35", "36-60", "60+"]
+        ages = pd.cut(df["patient_age"], bins=bins, labels=labels)
+        age_dist = ages.value_counts().reindex(labels, fill_value=0).to_dict()
+        gender_dist = df["patient_gender_norm"].value_counts().to_dict()
+        return {"age_buckets": age_dist, "gender_distribution": gender_dist}
+
+    # --------------------------- Semantic Similarity ---------------------------
+    def _ensure_embeddings(self) -> None:
+        if self._embeddings is not None and self._nn is not None:
+            return
+        if not self._cleaned:
+            self.clean()
+        if self.df.empty:
+            self._embeddings = np.zeros((0, 384), dtype=np.float32)
+            self._nn = None
+            return
+
+        # Load model lazily
+        if self._model is None:
+            model_name = settings.CAMEROON_EMBEDDINGS_MODEL
+            logger.info(f"Loading sentence-transformers model: {model_name}")
+            self._model = SentenceTransformer(model_name)
+
+        cache_file = os.path.join(self._cache_dir, "embeddings.npy")
+        if os.path.exists(cache_file):
+            try:
+                self._embeddings = np.load(cache_file)
+            except Exception:
+                self._embeddings = None
+
+        if self._embeddings is None or len(self._embeddings) != len(self.df):
+            texts = self.df["summary_text"].astype(str).tolist()
+            self._embeddings = self._model.encode(texts, batch_size=64, show_progress_bar=False, normalize_embeddings=True)
+            np.save(cache_file, self._embeddings)
+
+        # Build NN index
+        self._nn = NearestNeighbors(n_neighbors=10, metric="cosine")
+        self._nn.fit(self._embeddings)
+
+    def search_similar_cases(self, query_text: str, top_k: int = 10) -> List[SimilarCase]:
+        if not query_text or query_text.strip() == "":
+            return []
+        self._ensure_embeddings()
+        if self._model is None or self._nn is None or self._embeddings is None or self.df.empty:
+            return []
+
+        q = self._model.encode([query_text], normalize_embeddings=True)
+        distances, indices = self._nn.kneighbors(q, n_neighbors=min(top_k, len(self.df)))
+        distances = distances[0]
+        indices = indices[0]
+
+        results: List[SimilarCase] = []
+        for dist, idx in zip(distances, indices):
+            row = self.df.iloc[int(idx)]
+            # similarity = 1 - cosine distance
+            sim = float(1.0 - dist)
+            snippet = str(row.get("summary_text", ""))[:140] + ("..." if len(str(row.get("summary_text", ""))) > 140 else "")
+            results.append(SimilarCase(
+                summary_id=str(row.get("summary_id", "")),
+                diagnosis=row.get("diagnosis_norm"),
+                age=float(row.get("patient_age")) if pd.notna(row.get("patient_age")) else None,
+                gender=row.get("patient_gender_norm"),
+                summary_snippet=snippet,
+                similarity_score=sim,
+            ))
+        return results
+
+    # ----------------------------- Utils -----------------------------
+    def _extract_common_terms(self, texts: List[str], top_k: int = 20) -> List[str]:
+        # Very naive bag-of-words; in production consider medical entity extraction.
+        from collections import Counter
+        tokens: List[str] = []
+        for t in texts:
+            for w in str(t).lower().replace(",", " ").replace(".", " ").split():
+                if len(w) >= 3 and w.isalpha():
+                    tokens.append(w)
+        return [w for w, _ in Counter(tokens).most_common(top_k)]
+
+
+# Singleton accessor
+_singleton: Optional[CameroonMedicalData] = None
+
+
+def get_cameroon_data() -> CameroonMedicalData:
+    global _singleton
+    if _singleton is None:
+        _singleton = CameroonMedicalData()
+    return _singleton
+
+

app/utils/config.py

@@ -0,0 +1,139 @@
+from pydantic_settings import BaseSettings
+from pydantic import Field, field_validator, ConfigDict
+from typing import List, Any, Optional, Dict
+import os
+import logging
+import sys
+from pathlib import Path
+from dotenv import load_dotenv
+
+# Load environment variables from .env file
+load_dotenv()
+
+def setup_logging(log_level: str = "INFO", log_file: str = None) -> None:
+    """Configure logging for the application."""
+    log_format = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+    
+    # Configure root logger
+    logging.basicConfig(
+        level=log_level,
+        format=log_format,
+        handlers=[logging.StreamHandler(sys.stdout)]
+    )
+    
+    # Add file handler if log file is specified
+    if log_file:
+        log_file = Path(log_file)
+        log_file.parent.mkdir(parents=True, exist_ok=True)
+        file_handler = logging.FileHandler(log_file)
+        file_handler.setFormatter(logging.Formatter(log_format))
+        logging.getLogger().addHandler(file_handler)
+    
+    # Set log level for specific loggers
+    logging.getLogger("httpx").setLevel(logging.WARNING)
+    logging.getLogger("openai").setLevel(logging.WARNING)
+    logging.getLogger("urllib3").setLevel(logging.WARNING)
+
+
+class Settings(BaseSettings):
+    model_config = ConfigDict(
+        env_file=".env",
+        env_file_encoding="utf-8",
+        extra="allow",
+        env_nested_delimiter="__"
+    )
+    
+    # App configuration
+    APP_NAME: str = Field(default="Carehelp")
+    ENVIRONMENT: str = Field(default="development")
+    
+    # Logging configuration
+    LOG_LEVEL: str = Field(default="INFO")
+    LOG_FILE: Optional[str] = Field(default=None)
+    
+    def configure_logging(self) -> None:
+        """Configure logging based on settings."""
+        setup_logging(
+            log_level=self.LOG_LEVEL,
+            log_file=self.LOG_FILE
+        )
+    PORT: int = Field(default=8000)
+
+    # CORS configuration
+    CORS_ALLOW_ORIGINS: List[str] = Field(default_factory=lambda: ["*"])
+
+    # API Keys
+    GOOGLE_MAPS_API_KEY: str | None = None
+    SERPAPI_API_KEY: str | None = None
+    
+    # Supabase configuration
+    SUPABASE_URL: str | None = None
+    SUPABASE_ANON_KEY: str | None = None
+    SUPABASE_SERVICE_ROLE_KEY: str | None = None
+    SUPABASE_DB_PASSWORD: str | None = None
+    
+    # OpenAI configuration
+    OPENAI_API_KEY: str | None = None
+    OPENAI_MODEL: str = Field(default="gpt-4")
+    OPENAI_WHISPER_MODEL: str = Field(default="whisper-1")
+    OPENAI_WHISPER_FALLBACK_MODEL: str | None = Field(default="gpt-4-transcribe")
+    OPENAI_WHISPER_MAX_CHUNK_SECS: int = Field(default=120)
+
+    # JWT configuration
+    JWT_SECRET: str = Field(default="change_this_secret")
+    JWT_ALGORITHM: str = Field(default="HS256")
+    ACCESS_TOKEN_EXPIRE_MINUTES: int = Field(default=60 * 24 * 30)
+
+    # Hugging Face configuration
+    HF_API_TOKEN: str | None = None
+    HF_TRANSLATION_MODEL: str = Field(default="facebook/nllb-200-distilled-600M")
+
+    # Provider selection and models
+    # Options: 'hf' (Hugging Face Inference), 'ollama' (local), 'lmstudio' (local OpenAI-compatible)
+    AI_PROVIDER: str = Field(default="hf")
+
+    # Text generation (chat) models
+    HF_TEXT_MODEL: str = Field(default="meta-llama/Meta-Llama-3-8B-Instruct")
+    OLLAMA_MODEL: str = Field(default="llama3.1:8b")
+    OLLAMA_BASE_URL: str = Field(default="http://localhost:11434")
+    LMSTUDIO_MODEL: str | None = Field(default=None)
+    LMSTUDIO_BASE_URL: str = Field(default="http://localhost:1234/v1")
+
+    # Vision caption model (used to assist image analysis with HF)
+    HF_VISION_CAPTION_MODEL: str = Field(default="Salesforce/blip-image-captioning-large")
+    # Optional local vision model for Ollama (e.g., 'llava:latest')
+    OLLAMA_VISION_MODEL: str = Field(default="llava:latest")
+
+    # Automatic Speech Recognition (ASR)
+    HF_ASR_MODEL: str = Field(default="distil-whisper/distil-large-v3")
+
+    # Data paths
+    PATIENT_DATA_PATH: str = Field(default="../patient_records.json")
+
+    # Cameroon data configuration
+    CAMEROON_DATA_CSV: str = Field(default="../../clinical_summaries.csv")
+    CAMEROON_EMBEDDINGS_MODEL: str = Field(
+        default="sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2"
+    )
+    CAMEROON_CACHE_DIR: str = Field(default=".cache_cameroon")
+    CAMEROON_MAX_ROWS: int | None = None  # limit for dev/testing
+
+    @field_validator("CORS_ALLOW_ORIGINS", mode="before")
+    @classmethod
+    def parse_cors_origins(cls, v: Any) -> Any:
+        # Accept JSON array, comma-separated string, or single "*"
+        if isinstance(v, list):
+            return v
+        if isinstance(v, str):
+            s = v.strip()
+            if s == "":
+                return ["*"]
+            if s == "*":
+                return ["*"]
+            # comma separated
+            return [x.strip() for x in s.split(",") if x.strip()]
+        return v
+
+
+settings = Settings()
+

app/utils/helpers.py

@@ -0,0 +1,50 @@
+from cachetools import TTLCache
+from typing import Any, Optional
+
+
+cache = TTLCache(maxsize=512, ttl=60 * 10)
+
+
+def cache_get(key: str) -> Any | None:
+    return cache.get(key)
+
+
+def cache_set(key: str, value: Any) -> None:
+    cache[key] = value
+
+
+def medical_disclaimer(language: str) -> str:
+    if language.lower().startswith("fr"):
+        return (
+            "Avertissement: Je ne suis pas un médecin. Les conseils fournis par l'IA peuvent contenir des erreurs. "
+            "En cas de symptômes graves, consultez un professionnel de santé ou appelez les services d'urgence."
+        )
+    return (
+        "Disclaimer: I am not a medical professional. AI advice can be inaccurate. "
+        "For serious symptoms, consult a healthcare professional or emergency services."
+    )
+
+
+def emergency_triage(text: str) -> bool:
+    signals = ["chest pain", "poitrine", "hemorrag", "fainting", "inconscient", "stroke", "AVC", "difficulty breathing", "respire"]
+    lower = text.lower()
+    return any(s in lower for s in signals)
+
+
+def normalize_gender(value: str) -> Optional[str]:
+    if value is None:
+        return None
+    v = value.strip().lower()
+    mapping = {
+        "m": "male", "male": "male", "man": "male", "masculin": "male", "homme": "male",
+        "f": "female", "female": "female", "woman": "female", "feminin": "female", "femme": "female"
+    }
+    return mapping.get(v, None)
+
+
+def clean_diagnosis(value: str) -> Optional[str]:
+    if not value:
+        return None
+    v = value.strip().nlower() if hasattr(value, 'nlower') else value.strip().lower()
+    return v
+

app/utils/security.py

@@ -0,0 +1,59 @@
+from datetime import datetime, timedelta, timezone
+from typing import Optional
+
+import jwt
+from passlib.context import CryptContext
+from fastapi import Depends, HTTPException, status
+from fastapi.security import OAuth2PasswordBearer
+
+from typing import Any
+
+
+pwd_context = CryptContext(schemes=["bcrypt"], deprecated="auto")
+oauth2_scheme = OAuth2PasswordBearer(tokenUrl="/api/users/login")
+
+
+def hash_password(password: str) -> str:
+    return pwd_context.hash(password)
+
+
+def verify_password(password: str, hashed: str) -> bool:
+    return pwd_context.verify(password, hashed)
+
+
+def create_access_token(data: dict, secret: str, algorithm: str, expires_minutes: int) -> str:
+    to_encode = data.copy()
+    expire = datetime.now(timezone.utc) + timedelta(minutes=expires_minutes)
+    to_encode.update({"exp": expire})
+    return jwt.encode(to_encode, secret, algorithm=algorithm)
+
+
+def decode_token(token: str, secret: str, algorithms: list[str]) -> dict:
+    return jwt.decode(token, secret, algorithms=algorithms)
+
+
+async def get_current_user_optional(token: Optional[str] = Depends(oauth2_scheme)):
+    if not token:
+        return None
+    try:
+        from app.utils.config import settings
+        payload = decode_token(token, settings.JWT_SECRET, [settings.JWT_ALGORITHM])
+        # supabase auth uses UUID subject if you later switch to Supabase JWTs
+        return payload.get("sub")
+    except Exception:
+        return None
+
+
+async def get_current_user(token: str = Depends(oauth2_scheme)):
+    from app.utils.config import settings
+    try:
+        payload = decode_token(token, settings.JWT_SECRET, [settings.JWT_ALGORITHM])
+        user_id = payload.get("sub")  # string UUID or int
+        if user_id is None:
+            raise HTTPException(status_code=status.HTTP_401_UNAUTHORIZED, detail="Token invalide")
+        return {"id": user_id}  # Minimal user placeholder until Supabase integration
+    except jwt.ExpiredSignatureError:
+        raise HTTPException(status_code=status.HTTP_401_UNAUTHORIZED, detail="Token expiré")
+    except Exception:
+        raise HTTPException(status_code=status.HTTP_401_UNAUTHORIZED, detail="Token invalide")
+

debug_main.py

@@ -0,0 +1,49 @@
+import logging
+import asyncio
+from fastapi import FastAPI, Request
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import JSONResponse
+import uvicorn
+
+# Configure logging
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+)
+logger = logging.getLogger(__name__)
+
+app = FastAPI()
+
+# Add CORS middleware
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Simple health check endpoint
+@app.get("/health")
+async def healthcheck():
+    return {"status": "ok"}
+
+# Test endpoint that doesn't use any external services
+@app.get("/test")
+async def test_endpoint():
+    return {"message": "Test endpoint working"}
+
+# Test endpoint that simulates a delay
+@app.get("/test-delay")
+async def test_delay(seconds: float = 2.0):
+    await asyncio.sleep(seconds)
+    return {"message": f"Delayed response after {seconds} seconds"}
+
+if __name__ == "__main__":
+    uvicorn.run(
+        "debug_main:app",
+        host="0.0.0.0",
+        port=8000,
+        reload=True,
+        log_level="info"
+    )

main.py

@@ -0,0 +1,128 @@
+import logging
+from fastapi import FastAPI, Request, Depends
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import JSONResponse
+from starlette import status
+
+from app.utils.config import settings
+from app.utils.security import get_current_user_optional
+from app.routers import users, chat, audio, images
+from app.routers import cameroon_data
+from app.routers import ai
+
+
+def create_app() -> FastAPI:
+    # Configure logging
+    settings.configure_logging()
+    logger = logging.getLogger(__name__)
+    
+    app = FastAPI(
+        title="Carehelp API",
+        description="Backend IA médical pour le Cameroun (Carehelp)",
+        version="1.0.0",
+        contact={
+            "name": "Carehelp",
+        },
+    )
+
+    app.add_middleware(
+        CORSMiddleware,
+        allow_origins=settings.CORS_ALLOW_ORIGINS,
+        allow_credentials=True,
+        allow_methods=["*"]
+        ,
+        allow_headers=["*"]
+        ,
+    )
+
+    # Routers
+    app.include_router(users.router, prefix="/api", tags=["users"]) 
+    app.include_router(chat.router, prefix="/api", tags=["chat"]) 
+    app.include_router(audio.router, prefix="/api", tags=["audio"]) 
+    app.include_router(images.router, prefix="/api", tags=["images"]) 
+    app.include_router(cameroon_data.router, prefix="/api/cameroon-data", tags=["cameroon-data"]) 
+    app.include_router(ai.router, prefix="/api", tags=["ai"]) 
+
+    # Log all registered routes for debugging
+    try:
+        route_paths = sorted([getattr(r, 'path', str(r)) for r in app.routes])
+        logging.getLogger(__name__).info(f"Registered routes: {route_paths}")
+    except Exception as e:
+        logging.getLogger(__name__).error(f"Failed to list routes: {e}")
+
+    @app.get("/health")
+    def healthcheck():
+        return {"status": "ok"}
+
+    # Debug: return list of routes
+    @app.get("/routes")
+    def list_routes():
+        return {"routes": [
+            {
+                "path": getattr(r, 'path', str(r)),
+                "name": getattr(r, 'name', None),
+                "methods": list(getattr(r, 'methods', []) or [])
+            }
+            for r in app.routes
+        ]}
+
+    # No database initialization; Supabase will manage schema
+
+    @app.post("/gateway")
+    async def gateway(request: Request, current_user=Depends(get_current_user_optional)):
+        """
+        Endpoint unique pour le frontend.
+        - Mode actions: {"action": "chat|transcribe|analyze-image|analyze-multimodal|translate", "payload": {...}}
+        - Mode unifié (recommandé): {"payload": {text?, image?, images?, audio?, audios?, files?, file_names?, location?, want_stats?, agent_mode?}}
+          Si "action" est omis ou vaut l'un de {"auto", "unified", "ai"}, la requête est routée automatiquement selon les champs fournis.
+        """
+        body = await request.json()
+        action = body.get("action")
+        payload = body.get("payload", {})
+        
+        # Mode unifié: pas d'action explicitement fournie ou action spéciale
+        if not action or str(action).lower() in {"auto", "unified", "ai"}:
+            try:
+                req = ai.AIRequest(
+                    text=payload.get("text"),
+                    image=payload.get("image"),
+                    images=payload.get("images"),
+                    audio=payload.get("audio"),
+                    audios=payload.get("audios"),
+                    want_stats=payload.get("want_stats", False),
+                    location=payload.get("location"),
+                    files=payload.get("files"),
+                    file_names=payload.get("file_names"),
+                    agent_mode=payload.get("agent_mode"),
+                )
+            except Exception as e:
+                return JSONResponse(status_code=status.HTTP_400_BAD_REQUEST, content={"detail": f"Payload invalide pour le mode unifié: {e}"})
+            # Déléguer au point d'entrée IA unifié (gère texte, image(s), audio(s), fichiers)
+            return await ai.ai_endpoint(req)
+
+        # Proxifier vers les endpoints internes
+        if action == "chat":
+            return await chat.handle_chat_via_gateway(payload, current_user)
+        if action == "transcribe":
+            return await audio.handle_transcription_via_gateway(payload, current_user)
+        if action == "analyze-image":
+            return await images.handle_analyze_image_via_gateway(payload, current_user)
+        if action == "analyze-multimodal":
+            return await images.handle_analyze_multimodal_via_gateway(payload, current_user)
+        if action == "translate":
+            return await chat.handle_translate_via_gateway(payload, current_user)
+
+        return JSONResponse(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            content={"detail": "Action inconnue"},
+        )
+
+    return app
+
+
+app = create_app()
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run("main:app", host="0.0.0.0", port=int(settings.PORT), reload=True)
+

patient_records.json

@@ -0,0 +1,294 @@
+[
+  {
+    "patient_id": "P001",
+    "diagnosis": {
+      "fr": "Diabète de type 2",
+      "en": "Type 2 Diabetes",
+      "dua": "Sɔŋgɔ a sukɛlɛ a bibale",
+      "bss": "Màtìtì ma sùgàl ma mbìs",
+      "ewo": "Mvɔɔ ɔsɔk"
+    },
+    "medications": [
+      {
+        "name": {
+          "fr": "Metformine 500mg",
+          "en": "Metformin 500mg"
+        },
+        "dosage": "2 fois par jour",
+        "side_effects": {
+          "fr": "Nausées, diarrhée",
+          "en": "Nausea, diarrhea"
+        }
+      }
+    ],
+    "care_instructions": {
+      "fr": "Contrôle glycémique quotidien. Éviter les sucres rapides et faire 30 minutes d'exercice par jour.",
+      "en": "Monitor blood sugar daily. Avoid sugary foods and exercise for 30 minutes daily."
+    }
+  },
+  {
+    "patient_id": "P002",
+    "diagnosis": {
+      "fr": "Paludisme",
+      "en": "Malaria",
+      "dua": "Sɔŋgɔ a misɔɔ",
+      "bss": "Ntɔɔ̀",
+      "ewo": "Mvɔɔ ɔsɔŋ"
+    },
+    "medications": [
+      {
+        "name": {
+          "fr": "Artésunate 100mg",
+          "en": "Artesunate 100mg"
+        },
+        "dosage": "1 fois par jour pendant 3 jours",
+        "side_effects": {
+          "fr": "Maux de tête, vertiges",
+          "en": "Headache, dizziness"
+        }
+      }
+    ],
+    "care_instructions": {
+      "fr": "Boire beaucoup d'eau, se reposer. Utiliser une moustiquaire imprégnée.",
+      "en": "Drink plenty of water, rest. Sleep under an insecticide-treated mosquito net."
+    }
+  },
+  {
+    "patient_id": "P006",
+    "diagnosis": {
+      "fr": "VIH (Stade 1)",
+      "en": "HIV (Stage 1)",
+      "dua": "VIH (Búnu 1)",
+      "bss": "VIH (Lilɔŋ 1)",
+      "ewo": "VIH (Awono 1)"
+    },
+    "medications": [
+      {
+        "name": {
+          "fr": "Ténofovir + Lamivudine + Dolutégravir (TLD)",
+          "en": "Tenofovir + Lamivudine + Dolutegravir (TLD)"
+        },
+        "dosage": "1 comprimé/jour à vie",
+        "side_effects": {
+          "fr": "Vertiges (premières semaines), rash cutané",
+          "en": "Dizziness (first weeks), skin rash"
+        }
+      }
+    ],
+    "care_instructions": {
+      "fr": "Prise strictement quotidienne. Utilisation obligatoire de préservatifs pour protéger vos partenaires.",
+      "en": "Strict daily intake. Mandatory condom use to protect partners."
+    },
+    "emergency_scenarios": [
+      {
+        "trigger": {
+          "fr": "Oubli de dose >24h",
+          "en": "Missed dose >24h"
+        },
+        "action_steps": {
+          "fr": "1. Prenez la dose oubliée immédiatement. 2. Ne doublez jamais la dose suivante. 3. Contactez votre centre ARV pour informer le personnel.",
+          "en": "1. Take the missed dose immediately. 2. Never double the next dose. 3. Contact your ARV center to inform the staff."
+        }
+      }
+    ]
+  },
+  {
+    "context_id": "EDU001",
+    "context_type": "prevention",
+    "topic": {
+      "fr": "Hygiène des mains",
+      "en": "Hand Hygiene",
+      "dua": "Sɔbɔlɔ a mɛ́nyɔ",
+      "bss": "Sùgùsɛ̀l mi mbɔk",
+      "ewo": "Mvɔɔ ɔbɔɔ"
+    },
+    "educational_content": {
+      "fr": "Le lavage des mains avec du savon réduit de 50% les diarrhées et infections respiratoires. Lavez-les avant de manger, après les toilettes, et en rentrant à la maison.",
+      "en": "Washing hands with soap reduces diarrhea and respiratory infections by 50%. Wash them before eating, after using the toilet, and when returning home."
+    },
+    "visual_cue": "🫧⏱️",
+    "target_group": "Tous publics"
+  },
+  {
+    "context_id": "EDU002",
+    "context_type": "prevention",
+    "topic": {
+      "fr": "Prévention du paludisme",
+      "en": "Malaria Prevention",
+      "dua": "Bìtɔ́ɔ́ bi misɔɔ",
+      "bss": "Bìlɔ̀gɔ̀bì bi ntɔɔ̀",
+      "ewo": "Atìŋ ɔmvɔɔ ɔsɔŋ"
+    },
+    "educational_content": {
+      "fr": "Actions clés : 1. Dormir sous moustiquaire imprégnée. 2. Éliminer les eaux stagnantes autour de la maison. 3. Porter des vêtements longs le soir.",
+      "en": "Key actions: 1. Sleep under an insecticide-treated net. 2. Eliminate standing water around the house. 3. Wear long clothing in the evening."
+    },
+    "visual_cue": "🦟🚫",
+    "target_group": "Zones endémiques"
+  },
+  {
+    "context_id": "EDU003",
+    "context_type": "nutrition",
+    "topic": {
+      "fr": "Alimentation équilibrée",
+      "en": "Balanced Diet",
+      "dua": "Bìdì bi wɔ́ɔ́s",
+      "bss": "Bìjɛk bìtìì",
+      "ewo": "Mvɔɔ ɔjɛ"
+    },
+    "educational_content": {
+      "fr": "Un régime équilibré renforce votre immunité. Mangez des fruits, des légumes et des protéines (poisson, haricots) chaque jour. Limitez le sucre, le sel et les aliments transformés.",
+      "en": "A balanced diet boosts your immunity. Eat fruits, vegetables, and proteins (fish, beans) daily. Limit sugar, salt, and processed foods."
+    },
+    "visual_cue": "🥗🍎",
+    "target_group": "Général"
+  },
+  {
+    "context_id": "EMER001",
+    "context_type": "emergency_education",
+    "scenario": {
+      "fr": "Fièvre chez l'enfant de moins de 5 ans",
+      "en": "Fever in a child under 5",
+      "dua": "Munyɛŋgɛ mwa mwana a ponda 5",
+      "bss": "Njòŋgò nì mùn à nnɔ̀k 5",
+      "ewo": "Mvɔɔ ɔwondo ɔman"
+    },
+    "action_steps": {
+      "fr": "1. Mesurer la température. 2. Si >38°C, déshabiller l'enfant et lui donner un bain tiède. 3. Donner du paracétamol selon le poids. 4. Si la fièvre persiste ou >40°C, consulter immédiatement un médecin.",
+      "en": "1. Measure the temperature. 2. If >38°C, undress the child and give a lukewarm bath. 3. Give paracetamol according to weight. 4. If fever persists or >40°C, see a doctor immediately."
+    },
+    "visual_cue": "🌡️👶",
+    "risk_level": "Modéré"
+  },
+  {
+    "context_id": "EMER002",
+    "context_type": "emergency_education",
+    "scenario": {
+      "fr": "Morsure de serpent",
+      "en": "Snake Bite"
+    },
+    "action_steps": {
+      "fr": "1. Restez calme et rassurez la victime. 2. Immobilisez le membre mordu (ne pas le bouger). 3. NE PAS faire de garrot, NE PAS inciser, NE PAS aspirer le venin. 4. Aller à l'hôpital le plus proche IMMÉDIATEMENT.",
+      "en": "1. Stay calm and reassure the victim. 2. Immobilize the bitten limb (do not move it). 3. DO NOT use a tourniquet, DO NOT cut, DO NOT suck the venom. 4. Go to the nearest hospital IMMEDIATELY."
+    },
+    "visual_cue": "🐍➡️🏥",
+    "risk_level": "Élevé"
+  },
+  {
+    "patient_id": "P011",
+    "diagnosis": {
+      "fr": "Tuberculose",
+      "en": "Tuberculosis (TB)",
+      "dua": "Sɔŋgɔ a Kɔsɛ",
+      "bss": "Ntɔɔ̀ nì Kɔ̀s",
+      "ewo": "Mvɔɔ ɔkɔs"
+    },
+    "medications": [
+      {
+        "name": {
+          "fr": "Rifampicine, Isoniazide, Pyrazinamide, Ethambutol",
+          "en": "Rifampicin, Isoniazid, Pyrazinamide, Ethambutol"
+        },
+        "dosage": "Prise quotidienne pendant 6 mois, selon protocole national",
+        "side_effects": {
+          "fr": "Coloration orange des urines, troubles digestifs, douleurs articulaires",
+          "en": "Orange discoloration of urine, digestive issues, joint pain"
+        }
+      }
+    ],
+    "care_instructions": {
+      "fr": "Suivre le traitement sans interruption est crucial. Couvrez-vous la bouche et le nez lorsque vous toussez. Aérez bien votre logement. Mangez équilibré.",
+      "en": "Following the treatment without interruption is crucial. Cover your mouth and nose when coughing. Ventilate your home well. Eat a balanced diet."
+    },
+    "common_questions": [
+      {
+        "question": {
+          "fr": "La tuberculose se guérit-elle ?",
+          "en": "Is tuberculosis curable?"
+        },
+        "answer": {
+          "fr": "Oui, la tuberculose se guérit complètement si le traitement de 6 mois est suivi correctement sans interruption.",
+          "en": "Yes, tuberculosis is completely curable if the 6-month treatment is followed correctly without interruption."
+        }
+      }
+    ]
+  },
+  {
+    "patient_id": "P012",
+    "diagnosis": {
+      "fr": "Gastrite",
+      "en": "Gastritis",
+      "dua": "Sɔŋgɔ a dibɔ́mɔ",
+      "bss": "Ntɔɔ̀ nì libùm",
+      "ewo": "Mvɔɔ ɔwom"
+    },
+    "medications": [
+      {
+        "name": {
+          "fr": "Oméprazole 20mg",
+          "en": "Omeprazole 20mg"
+        },
+        "dosage": "1 comprimé par jour avant le repas",
+        "side_effects": {
+          "fr": "Maux de tête, diarrhée",
+          "en": "Headache, diarrhea"
+        }
+      }
+    ],
+    "care_instructions": {
+      "fr": "Évitez les aliments épicés, acides, gras, ainsi que l'alcool et le café. Mangez de plus petits repas plus fréquemment. Ne vous allongez pas juste après avoir mangé.",
+      "en": "Avoid spicy, acidic, fatty foods, as well as alcohol and coffee. Eat smaller, more frequent meals. Do not lie down immediately after eating."
+    },
+    "common_questions": [
+      {
+        "question": {
+          "fr": "Qu'est-ce qui cause la gastrite ?",
+          "en": "What causes gastritis?"
+        },
+        "answer": {
+          "fr": "La gastrite peut être causée par une infection (Helicobacter pylori), le stress, l'alcool, ou la prise de certains médicaments anti-inflammatoires.",
+          "en": "Gastritis can be caused by an infection (Helicobacter pylori), stress, alcohol, or taking certain anti-inflammatory drugs."
+        }
+      }
+    ]
+  },
+  {
+    "patient_id": "P013",
+    "diagnosis": {
+      "fr": "Infection urinaire",
+      "en": "Urinary Tract Infection (UTI)",
+      "dua": "Sɔŋgɔ a misɔ́pí",
+      "bss": "Ntɔɔ̀ nì màsɔ̀p",
+      "ewo": "Mvɔɔ ɔsɔp"
+    },
+    "medications": [
+      {
+        "name": {
+          "fr": "Ciprofloxacine 500mg",
+          "en": "Ciprofloxacin 500mg"
+        },
+        "dosage": "1 comprimé 2 fois par jour pendant 7 jours",
+        "side_effects": {
+          "fr": "Nausées, sensibilité au soleil",
+          "en": "Nausea, sun sensitivity"
+        }
+      }
+    ],
+    "care_instructions": {
+      "fr": "Buvez beaucoup d'eau (au moins 2 litres par jour) pour aider à nettoyer les voies urinaires. Urinez fréquemment et ne vous retenez pas. Essuyez-vous d'avant en arrière après être allée aux toilettes.",
+      "en": "Drink plenty of water (at least 2 liters per day) to help flush the urinary tract. Urinate frequently and do not hold it in. Wipe from front to back after using the toilet."
+    },
+    "common_questions": [
+      {
+        "question": {
+          "fr": "Comment prévenir les infections urinaires ?",
+          "en": "How to prevent UTIs?"
+        },
+        "answer": {
+          "fr": "Pour prévenir les infections, buvez beaucoup d'eau, urinez après les rapports sexuels, et évitez les produits d'hygiène irritants.",
+          "en": "To prevent infections, drink plenty of water, urinate after intercourse, and avoid irritating hygiene products."
+        }
+      }
+    ]
+  }
+] 

requirements.txt

@@ -0,0 +1,34 @@
+fastapi
+uvicorn[standard]
+python-multipart
+pydantic
+pydantic-settings  
+pydantic[email]
+passlib[bcrypt]
+PyJWT
+PyPDF2
+httpx
+openai
+python-dotenv
+langchain-openai
+langchain-huggingface
+huggingface-hub
+cachetools
+loguru
+numpy
+pandas
+scikit-learn
+scipy
+sentence-transformers
+rapidfuzz
+librosa
+soundfile
+noisereduce
+langdetect
+requests
+serpapi
+googlemaps
+langchain
+langchain-community
+langchain-core
+langsmith

scripts/local_media_tests.py

@@ -0,0 +1,162 @@
+import os
+import time
+import json
+import requests
+from pathlib import Path
+
+API_URL = os.environ.get("API_URL", "http://127.0.0.1:8000")
+
+ROOT = Path(__file__).resolve().parents[1]
+IMG1 = ROOT / "image-test1.jpg"
+IMG2 = ROOT / "image-test2.jpg"
+AUDIO1 = ROOT / "test-audio1.wav"
+
+
+def wait_for_health(timeout=60):
+    url = f"{API_URL}/health"
+    start = time.time()
+    while time.time() - start < timeout:
+        try:
+            r = requests.get(url, timeout=3)
+            if r.status_code == 200:
+                return True
+        except Exception:
+            pass
+        time.sleep(1)
+    return False
+
+
+def pretty(obj):
+    try:
+        return json.dumps(obj, indent=2, ensure_ascii=False)
+    except Exception:
+        return str(obj)
+
+
+def post_json(path, payload):
+    url = f"{API_URL}{path}"
+    r = requests.post(url, json=payload, timeout=180)
+    try:
+        return r.status_code, r.json()
+    except Exception:
+        return r.status_code, {"text": r.text}
+
+
+def post_form(path, data, files):
+    url = f"{API_URL}{path}"
+    r = requests.post(url, data=data, files=files, timeout=300)
+    try:
+        return r.status_code, r.json()
+    except Exception:
+        return r.status_code, {"text": r.text}
+
+
+def test_unified_text():
+    payload = {
+        "message": "Patient with rash and fever for 2 days.",
+        "message_type": "text",
+    }
+    return post_json("/api/chat/unified", payload)
+
+
+def test_unified_image(path: Path):
+    payload = {
+        "message": str(path),  # local path supported by server (converted to data URL)
+        "message_type": "image",
+    }
+    return post_json("/api/chat/unified", payload)
+
+
+def test_unified_audio(path: Path):
+    payload = {
+        "message": str(path),  # local path supported by server
+        "message_type": "audio",
+    }
+    return post_json("/api/chat/unified", payload)
+
+
+def test_form(text: str | None = None, images: list[Path] | None = None, audios: list[Path] | None = None):
+    data = {}
+    if text is not None:
+        data["text"] = text
+    data["want_stats"] = "true"
+    files = []
+    for img in images or []:
+        mime = "image/jpeg" if img.suffix.lower() in {".jpg", ".jpeg"} else "image/png"
+        files.append(("images", (img.name, open(img, "rb"), mime)))
+    for au in audios or []:
+        files.append(("audios", (au.name, open(au, "rb"), "audio/wav")))
+    try:
+        return post_form("/api/ai/form", data, files)
+    finally:
+        # Close any file handles we opened
+        for _, (name, fh, _mime) in files:
+            try:
+                fh.close()
+            except Exception:
+                pass
+
+
+def main():
+    print(f"API_URL: {API_URL}")
+    # Validate media presence
+    missing = [p for p in [IMG1, IMG2, AUDIO1] if not p.exists()]
+    if missing:
+        print("Missing local files:", ", ".join(map(str, missing)))
+        return 2
+
+    print("Waiting for API /health ...")
+    if not wait_for_health(timeout=90):
+        print("Server not ready within timeout.")
+        return 3
+
+    results = []
+
+    print("\n=== Unified - TEXT only ===")
+    results.append(("unified_text",) + test_unified_text())
+
+    print("\n=== Unified - IMAGE only (image-test1.jpg) ===")
+    results.append(("unified_image_img1",) + test_unified_image(IMG1))
+
+    print("\n=== Unified - IMAGE only (image-test2.jpg) ===")
+    results.append(("unified_image_img2",) + test_unified_image(IMG2))
+
+    print("\n=== Unified - AUDIO only (test-audio1.wav) ===")
+    results.append(("unified_audio_audio1",) + test_unified_audio(AUDIO1))
+
+    print("\n=== Form - TEXT + IMAGE (img1) ===")
+    results.append(("form_text_img1",) + test_form(
+        text="Patient with rash and fever for 2 days.", images=[IMG1], audios=[]
+    ))
+
+    print("\n=== Form - TEXT + AUDIO (audio1) ===")
+    results.append(("form_text_audio1",) + test_form(
+        text="Patient with cough and sore throat.", images=[], audios=[AUDIO1]
+    ))
+
+    print("\n=== Form - TEXT + IMAGES (img1, img2) + AUDIO (audio1) ===")
+    results.append(("form_text_imgs_audio",) + test_form(
+        text="Patient with rash, fever and mild headache.", images=[IMG1, IMG2], audios=[AUDIO1]
+    ))
+
+    # Print a compact summary at the end
+    print("\n==== SUMMARY ====")
+    for label, status, payload in results:
+        status_str = "OK" if status == 200 else f"ERR({status})"
+        print(f"- {label}: {status_str}")
+
+    # Optionally dump detailed JSON for failures
+    failures = [(l, s, p) for (l, s, p) in results if s != 200]
+    if failures:
+        print("\n==== FAILURES (detailed) ====")
+        for label, status, payload in failures:
+            print(f"\n## {label} -> HTTP {status}")
+            print(pretty(payload))
+    else:
+        print("\nAll tests returned HTTP 200.")
+
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

scripts/run_unified_tests.py

@@ -0,0 +1,77 @@
+import os
+import sys
+import importlib.util
+import subprocess
+import pytest
+from colorama import init, Fore, Style
+
+# Initialize colorama for colored output
+init()
+
+# Ensure project root is on sys.path
+PROJECT_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), os.pardir))
+if PROJECT_ROOT not in sys.path:
+    sys.path.insert(0, PROJECT_ROOT)
+
+def check_dependencies():
+    """Check if all required dependencies are installed"""
+    required_packages = ['httpx', 'pytest', 'colorama', 'fastapi']
+    missing_packages = []
+    
+    for package in required_packages:
+        if importlib.util.find_spec(package) is None:
+            missing_packages.append(package)
+    
+    if missing_packages:
+        print(f"{Fore.RED}Missing required dependencies: {', '.join(missing_packages)}{Style.RESET_ALL}")
+        install = input(f"Would you like to install them now? (y/n): ").lower().strip() == 'y'
+        
+        if install:
+            print(f"{Fore.YELLOW}Installing missing packages...{Style.RESET_ALL}")
+            subprocess.check_call([sys.executable, "-m", "pip", "install"] + missing_packages)
+            print(f"{Fore.GREEN}Dependencies installed successfully!{Style.RESET_ALL}")
+            return True
+        else:
+            print(f"{Fore.RED}Cannot run tests without required dependencies.{Style.RESET_ALL}")
+            return False
+    return True
+
+def main():
+    # Check dependencies first
+    if not check_dependencies():
+        return 1
+        
+    print(f"{Fore.CYAN}===== Running Unified API Tests ====={Style.RESET_ALL}")
+    
+    # Define test patterns to run
+    test_patterns = [
+        "tests/test_api_endpoints.py::test_unified_chat_text",
+        "tests/test_live_ai.py::test_unified_text_live"
+    ]
+    
+    # Run each test and collect results
+    results = {}
+    for pattern in test_patterns:
+        print(f"\n{Fore.YELLOW}Running: {pattern}{Style.RESET_ALL}")
+        exit_code = pytest.main([pattern, "-v"])
+        results[pattern] = exit_code == 0
+    
+    # Print summary
+    print(f"\n{Fore.CYAN}===== Test Results Summary ====={Style.RESET_ALL}")
+    all_passed = True
+    for pattern, passed in results.items():
+        status = f"{Fore.GREEN}PASSED{Style.RESET_ALL}" if passed else f"{Fore.RED}FAILED{Style.RESET_ALL}"
+        print(f"{pattern}: {status}")
+        if not passed:
+            all_passed = False
+    
+    # Final status
+    if all_passed:
+        print(f"\n{Fore.GREEN}All unified tests passed!{Style.RESET_ALL}")
+    else:
+        print(f"\n{Fore.RED}Some unified tests failed. Check the output above for details.{Style.RESET_ALL}")
+    
+    return 0 if all_passed else 1
+
+if __name__ == "__main__":
+    sys.exit(main())

scripts/test_ai_form.py

@@ -0,0 +1,27 @@
+import requests
+import io
+
+API_URL = "http://localhost:8000/api/ai/form"
+
+text = "Patient with rash and fever for 2 days."
+
+# Fetch a small sample image
+img_url = "https://upload.wikimedia.org/wikipedia/commons/8/89/Portrait_Placeholder.png"
+img_bytes = requests.get(img_url, timeout=15).content
+
+files = [
+    ("images", ("sample.png", io.BytesIO(img_bytes), "image/png")),
+]
+
+data = {
+    "text": text,
+    "want_stats": "true",
+    "location": "Douala, Cameroon",
+}
+
+resp = requests.post(API_URL, data=data, files=files)
+print("Status:", resp.status_code)
+try:
+    print(resp.json())
+except Exception:
+    print(resp.text)

scripts/test_api_ai.py

@@ -0,0 +1,36 @@
+import requests
+import json
+
+API_URL = "http://localhost:8000/api/ai"
+
+# Test 1 : Texte seul
+payload_text = {
+    "text": "Patient présentant une éruption cutanée et de la fièvre depuis 2 jours.",
+    "want_stats": True
+}
+
+# Test 2 : Image seule
+payload_image = {
+    "image": "https://upload.wikimedia.org/wikipedia/commons/8/89/Portrait_Placeholder.png",
+    "want_stats": True
+}
+
+# Test 3 : Texte + Image
+payload_multimodal = {
+    "text": "Patient présentant une éruption cutanée et de la fièvre depuis 2 jours.",
+    "image": "https://upload.wikimedia.org/wikipedia/commons/8/89/Portrait_Placeholder.png",
+    "want_stats": True
+}
+
+for label, payload in [
+    ("Texte seul", payload_text),
+    ("Image seule", payload_image),
+    ("Texte + Image", payload_multimodal)
+]:
+    print(f"\n=== Test : {label} ===")
+    response = requests.post(API_URL, json=payload)
+    print("Status:", response.status_code)
+    try:
+        print(json.dumps(response.json(), indent=2, ensure_ascii=False))
+    except Exception:
+        print(response.text)

scripts/test_multimodal.py

@@ -0,0 +1,19 @@
+import requests
+import json
+
+API_URL = "http://localhost:8000/gateway"
+
+payload = {
+    "action": "analyze-multimodal",
+    "payload": {
+        "image_url": "https://upload.wikimedia.org/wikipedia/commons/8/89/Portrait_Placeholder.png",
+        "text": "Patient présentant une éruption cutanée et de la fièvre depuis 2 jours."
+    }
+}
+
+response = requests.post(API_URL, json=payload)
+print("Status:", response.status_code)
+try:
+    print(json.dumps(response.json(), indent=2, ensure_ascii=False))
+except Exception:
+    print(response.text)

scripts/try_unified.py

@@ -0,0 +1,73 @@
+import argparse
+import json
+import os
+import sys
+
+import requests
+
+
+def call_unified(api_url: str, message: str, message_type: str, language: str | None, history: list[str] | None = None) -> dict:
+    payload = {
+        "message": message,
+        "message_type": message_type,
+    }
+    if language:
+        payload["language"] = language
+    if history:
+        payload["history"] = history
+    r = requests.post(f"{api_url}/api/chat/unified", json=payload, timeout=120)
+    try:
+        data = r.json()
+    except Exception:
+        data = {"status_code": r.status_code, "text": r.text}
+    return {"status_code": r.status_code, "data": data}
+
+
+def pretty(obj: dict) -> str:
+    return json.dumps(obj, indent=2, ensure_ascii=False)
+
+
+def interactive_mode(api_url: str, language: str | None) -> int:
+    print("Interactive unified chat tester. Type 'quit' to exit.\n")
+    history: list[str] = []
+    while True:
+        t = input("Type (text/audio/image) [text]: ").strip().lower() or "text"
+        if t not in {"text", "audio", "image"}:
+            print("Invalid type. Use text/audio/image.")
+            continue
+        msg = input("Message (text or URL): ").strip()
+        if msg.lower() in {"quit", "exit"}:
+            return 0
+        res = call_unified(api_url, msg, t, language, history=history[-6:])
+        print(pretty(res))
+        if res.get("status_code") == 200 and isinstance(res.get("data"), dict):
+            # Append last user message to history for context
+            history.append(msg)
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description="Try the unified AI endpoint with text/image/audio")
+    parser.add_argument("--api", default=os.environ.get("API_URL", "http://127.0.0.1:8000"), help="Base API URL")
+    parser.add_argument("--message", help="Text or URL to test")
+    parser.add_argument("--type", dest="type_", default="text", choices=["text", "audio", "image"], help="Message type")
+    parser.add_argument("--language", help="Optional language for response (auto-detected if omitted)")
+    parser.add_argument("--history", nargs="*", help="Optional prior messages (space-separated)")
+    parser.add_argument("--interactive", action="store_true", help="Interactive prompt mode")
+    args = parser.parse_args()
+
+    if args.interactive:
+        return interactive_mode(args.api, args.language) or 0
+
+    if not args.message:
+        print("--message is required when not in --interactive mode", file=sys.stderr)
+        return 2
+
+    result = call_unified(args.api, args.message, args.type_, args.language, history=args.history)
+    print(pretty(result))
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
+
+

tests/conftest.py

@@ -0,0 +1,9 @@
+import os
+import sys
+
+# Ensure project root is on sys.path so 'app' and 'main' can be imported
+PROJECT_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), os.pardir))
+if PROJECT_ROOT not in sys.path:
+    sys.path.insert(0, PROJECT_ROOT)
+
+

tests/test_ai_services.py

@@ -0,0 +1,55 @@
+from unittest.mock import patch, MagicMock
+from app.ai_services import chat_completion, analyze_image, translate_text, transcribe_audio
+
+
+@patch("app.ai_services.openai_client")
+def test_chat_completion(mock_client):
+    mock = MagicMock()
+    mock.chat.completions.create.return_value = MagicMock(choices=[MagicMock(message=MagicMock(content="ok"))])
+    mock_client.return_value = mock
+    out = chat_completion([{"role": "user", "content": "hello"}], "en")
+    assert out == "ok"
+
+
+@patch("app.ai_services.openai_client")
+def test_analyze_image(mock_client):
+    mock = MagicMock()
+    mock.chat.completions.create.return_value = MagicMock(choices=[MagicMock(message=MagicMock(content="image ok"))])
+    mock_client.return_value = mock
+    out = analyze_image("./image-test2.jpg", None)
+    assert "image ok" in out
+
+
+@patch("app.ai_services.openai_client")
+def test_translate_text_fallback_openai(mock_client):
+    mock = MagicMock()
+    mock.chat.completions.create.return_value = MagicMock(choices=[MagicMock(message=MagicMock(content="bonjour"))])
+    mock_client.return_value = mock
+    out = translate_text("hello", "fr")
+    assert out == "bonjour"
+
+
+@patch("app.ai_services.openai_client")
+@patch("httpx.Client.get")
+def test_transcribe_audio(mock_get, mock_client):
+    # Mock audio download
+    mock_response = MagicMock()
+    mock_response.content = b"fake-bytes"
+    mock_response.raise_for_status.return_value = None
+    mock_get.return_value = mock_response
+
+    # Mock whisper
+    mock_openai = MagicMock()
+    mock_openai.audio.transcriptions.create.return_value = MagicMock(text="bonjour")
+    mock_client.return_value = mock_openai
+
+    out = transcribe_audio("./test-audio1.wav", language="fr")
+    assert out == "bonjour"
+
+from app.ai_services import build_system_prompt
+
+
+def test_system_prompt_contains_context():
+    p = build_system_prompt("fr")
+    assert "Context" in p or "Contex" in p or len(p) > 0
+

tests/test_api.py

@@ -0,0 +1,22 @@
+from fastapi.testclient import TestClient
+from main import app
+
+
+client = TestClient(app)
+
+
+def test_health():
+    r = client.get("/health")
+    assert r.status_code == 200
+
+
+def test_cameroon_data_endpoints_exist():
+    r1 = client.get("/api/cameroon-data/stats/overview")
+    assert r1.status_code in (200, 500)  # 500 if CSV not configured
+
+    r2 = client.get("/api/cameroon-data/patterns/seasonal")
+    assert r2.status_code in (200, 500)
+
+    r3 = client.post("/api/cameroon-data/search/similar-cases", json={"query_text": "fever", "top_k": 3})
+    assert r3.status_code in (200, 500)
+

tests/test_api_endpoints.py

@@ -0,0 +1,24 @@
+from fastapi.testclient import TestClient
+from main import app
+
+
+client = TestClient(app)
+
+
+def test_unified_chat_text():
+    r = client.post("/api/chat/unified", json={
+        "message": "J'ai de la fièvre et des frissons",
+        "message_type": "text",
+        "language": "fr"
+    })
+    assert r.status_code in (200, 500)
+    if r.status_code == 200:
+        data = r.json()
+        assert "response" in data and "context" in data
+
+
+def test_cameroon_overview():
+    r = client.get("/api/cameroon-data/stats/overview")
+    assert r.status_code in (200, 500)
+
+

tests/test_cameroon_data.py

@@ -0,0 +1,40 @@
+import os
+import pandas as pd
+from app.services.cameroon_data import CameroonMedicalData
+
+
+def test_service_handles_missing_csv(tmp_path):
+    svc = CameroonMedicalData(csv_path=str(tmp_path / "missing.csv"))
+    svc.clean()
+    assert svc.stats_overview()["total_rows"] == 0
+
+
+def test_basic_stats(tmp_path):
+    # Build a tiny CSV
+    df = pd.DataFrame([
+        {"summary_id": "1", "patient_id": "p1", "patient_age": 25, "patient_gender": "M", "diagnosis": "Paludisme",
+         "body_temp_c": 38.5, "blood_pressure_systolic": 120, "heart_rate": 90, "summary_text": "Fievre et frissons",
+         "date_recorded": "2024-01-10"},
+        {"summary_id": "2", "patient_id": "p2", "patient_age": 7, "patient_gender": "F", "diagnosis": "Typhoide",
+         "body_temp_c": 39.2, "blood_pressure_systolic": 110, "heart_rate": 95, "summary_text": "Fièvre, maux de ventre",
+         "date_recorded": "2024-02-15"}
+    ])
+    csv_path = tmp_path / "clinical_summaries.csv"
+    df.to_csv(csv_path, index=False)
+
+    svc = CameroonMedicalData(csv_path=str(csv_path))
+    svc.clean()
+    ov = svc.stats_overview()
+    assert ov["total_rows"] == 2
+    assert "paludisme" in ov["top_diagnoses"] or "typhoide" in ov["top_diagnoses"]
+
+    disease = svc.stats_disease("paludisme")
+    assert disease["disease"] == "paludisme"
+
+    seasonal = svc.seasonal_patterns()
+    assert isinstance(seasonal, dict)
+
+    age_gender = svc.age_gender_distribution()
+    assert "age_buckets" in age_gender and "gender_distribution" in age_gender
+
+

tests/test_helpers.py

@@ -0,0 +1,20 @@
+from app.utils.helpers import normalize_gender, clean_diagnosis, emergency_triage
+
+
+def test_normalize_gender():
+    assert normalize_gender("M") == "male"
+    assert normalize_gender("masculin") == "male"
+    assert normalize_gender("Femme") == "female"
+    assert normalize_gender("unknown") is None
+
+
+def test_clean_diagnosis():
+    assert clean_diagnosis(" Paludisme ") == "paludisme"
+    assert clean_diagnosis("") is None
+
+
+def test_emergency_triage():
+    assert emergency_triage("douleur poitrine") is True
+    assert emergency_triage("tout va bien") is False
+
+

tests/test_live_ai.py

@@ -0,0 +1,24 @@
+import os
+import pytest
+from fastapi.testclient import TestClient
+from main import app
+
+
+requires_live = pytest.mark.skipif(
+    not os.getenv("OPENAI_API_KEY"), reason="Skipping live AI tests: OPENAI_API_KEY not set"
+)
+
+
+@requires_live
+def test_unified_text_live():
+    client = TestClient(app)
+    r = client.post("/api/chat/unified", json={
+        "message": "J'ai de la fièvre et des frissons",
+        "message_type": "text",
+        "language": "fr"
+    })
+    assert r.status_code == 200
+    data = r.json()
+    assert "response" in data and "context" in data
+
+

tests/test_users_endpoints.py

@@ -0,0 +1,24 @@
+from fastapi.testclient import TestClient
+from main import app
+
+
+client = TestClient(app)
+
+
+def test_users_register_stubbed():
+    r = client.post("/api/users/register", json={
+        "email": "test@example.com",
+        "password": "secret",
+        "preferred_language": "fr"
+    })
+    assert r.status_code == 501
+
+
+def test_users_login_stubbed():
+    r = client.post("/api/users/login", data={
+        "username": "test@example.com",
+        "password": "secret"
+    })
+    assert r.status_code == 501
+
+