app.py

import os
from fastapi import FastAPI
from pydantic import BaseModel
from langdetect import detect

from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline, GenerationConfig
from langchain.vectorstores import Qdrant
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.chains import RetrievalQA
from langchain.llms import HuggingFacePipeline
from qdrant_client import QdrantClient

# Get environment variables
QDRANT_API_KEY = os.getenv("QDRANT_API_KEY")
QDRANT_URL = os.getenv("QDRANT_URL")
COLLECTION_NAME = "arabic_rag_collection"

# Load model and tokenizer
model_name = "FreedomIntelligence/Apollo-7B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer.pad_token = tokenizer.eos_token

# Generation settings
generation_config = GenerationConfig(
    max_new_tokens=150,
    temperature=0.2,
    top_k=20,
    do_sample=True,
    top_p=0.7,
    repetition_penalty=1.3,
)

# Text generation pipeline
llm_pipeline = pipeline(
    model=model,
    tokenizer=tokenizer,
    task="text-generation",
    generation_config=generation_config,
    device=model.device.index if model.device.type == "cuda" else -1
)
llm = HuggingFacePipeline(pipeline=llm_pipeline)

# Connect to Qdrant + embedding
embedding = HuggingFaceEmbeddings(model_name="Omartificial-Intelligence-Space/GATE-AraBert-v1")
qdrant_client = QdrantClient(url=QDRANT_URL, api_key=QDRANT_API_KEY)

vector_store = Qdrant(
    client=qdrant_client,
    collection_name=COLLECTION_NAME,
    embeddings=embedding
)

retriever = vector_store.as_retriever(search_kwargs={"k": 3})

# Set up RAG QA chain
qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    retriever=retriever,
    chain_type="stuff"
)

# FastAPI setup
app = FastAPI(title="Apollo RAG Medical Chatbot")


class Query(BaseModel):
    question: str = Field(..., example="ما هي اسباب تساقط الشعر ؟", min_length=3)

class TimeoutCallback(BaseCallbackHandler):
    def __init__(self, timeout_seconds: int = 60):
        self.timeout_seconds = timeout_seconds
        self.start_time = None

    async def on_llm_start(self, *args, **kwargs):
        self.start_time = asyncio.get_event_loop().time()

    async def on_llm_new_token(self, *args, **kwargs):
        if asyncio.get_event_loop().time() - self.start_time > self.timeout_seconds:
            raise TimeoutError("LLM processing timeout")

# Prompt template
def generate_prompt(question: str) -> str:
    lang = detect(question)
    if lang == "ar":
        return f"""أجب على السؤال الطبي التالي بلغة عربية فصحى، بإجابة دقيقة ومفصلة. إذا لم تجد معلومات كافية في السياق، استخدم معرفتك الطبية السابقة. 
 وتأكد من ان:
- عدم تكرار أي نقطة أو عبارة أو كلمة
- وضوح وسلاسة كل نقطة
- تجنب الحشو والعبارات الزائدة
السؤال: {question}
الإجابة:"""
    else:
        return f"""Answer the following medical question in clear English with a detailed, non-redundant response. Do not repeat ideas or restate the question. If the context lacks information, rely on prior medical knowledge.
Question: {question}
Answer:"""

# Input schema
# class ChatRequest(BaseModel):
#     message: str

# # Output endpoint
# @app.post("/chat")
# def chat_rag(req: ChatRequest):
#     prompt = generate_prompt(req.message)
#     response = qa_chain.run(prompt)
#     return {"response": response}


# === ROUTES === #
@app.get("/")
async def root():
    return {"message": "Medical QA API is running!"}

@app.post("/ask")
async def ask(query: Query):
    try:
        logger.debug(f"Received question: {query.question}")
        prompt = generate_prompt(query.question)
        timeout_callback = TimeoutCallback(timeout_seconds=60)


        loop = asyncio.get_event_loop()
        
        answer = await asyncio.wait_for(
            # qa_chain.run(prompt, callbacks=[timeout_callback]),
            loop.run_in_executor(None, qa_chain.run, prompt),
            timeout=360
        )

        if not answer:
            raise ValueError("Empty answer returned from model")

        if 'Answer:' in answer:
            response_text = answer.split('Answer:')[-1].strip()
        elif 'الإجابة:' in answer:
            response_text = answer.split('الإجابة:')[-1].strip()
        else:
            response_text = answer.strip()

        
        return {
            "status": "success",
            "response": response_text,
            "language": detect(query.question)
        }

    except TimeoutError as te:
        logger.error("Request timed out", exc_info=True)
        raise HTTPException(
            status_code=status.HTTP_504_GATEWAY_TIMEOUT,
            detail={"status": "error", "message": "Request timed out", "error": str(te)}
        )

    except Exception as e:
        logger.error(f"Unexpected error: {e}", exc_info=True)
        raise HTTPException(
            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
            detail={"status": "error", "message": "Internal server error", "error": str(e)}
        )

# === ENTRYPOINT === #
if __name__ == "__main__":
    def handle_exit(signum, frame):
        print("Shutting down gracefully...")
        exit(0)

    signal.signal(signal.SIGINT, handle_exit)
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)