Update app.py

app.py

@@ -1,165 +1,136 @@
-import gradio as gr
-import openai
-import os
+ import os
 import json
 import random
 import requests
-from transformers import pipeline
-from datetime import datetime
-
-# --- Setup ---
-openai.api_key = os.getenv("OPENAI_API_KEY")
+from datetime import datetime, timedelta
+from flask import Flask, request, jsonify
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+from openai import OpenAI
+
+app = Flask(__name__)
+
+# Initialize OpenAI client
+client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
+
+# Load emotion model
+tokenizer = AutoTokenizer.from_pretrained("j-hartmann/emotion-english-distilroberta-base")
+model = AutoModelForSequenceClassification.from_pretrained("j-hartmann/emotion-english-distilroberta-base")
+emotion_labels = [
+    "anger",
+    "disgust",
+    "fear",
+    "joy",
+    "neutral",
+    "sadness",
+    "surprise"
+]
 
-emotion_classifier = pipeline("text-classification",
-                              model="j-hartmann/emotion-english-distilroberta-base")
+# Motivational / spiritual quotes (non-religious and respectful)
+motivational_quotes = [
+    "Believe you can and you’re halfway there.",
+    "Every day is a new beginning; take a deep breath and start again.",
+    "The only way out is through. You’ve got this.",
+    "Storms don’t last forever — keep moving forward.",
+    "Even the smallest step in the right direction is progress.",
+    "You are stronger than you think and more capable than you realize.",
+    "Let your hope, not your hurt, shape your future.",
+    "Peace begins the moment you choose to stop fighting yourself.",
+    "Light will always find its way through the cracks.",
+    "Take things one heartbeat at a time — you are doing enough."
+]
 
-USER_FILE = "user_data.json"
-QUOTE_API = "https://api.quotable.io/random"
+# Path for storing user data
+USER_DATA_FILE = "user_data.json"
 
-LOCAL_QUOTES = [
-    "Every sunrise brings new hope. Keep shining.",
-    "You are capable of more than you realize.",
-    "Each step, no matter how small, is progress.",
-    "Difficult roads often lead to beautiful destinations.",
-    "The best time for new beginnings is now.",
-    "Your inner light never fades—let it guide you."
-]
 
-# --- Helper functions ---
 def load_user_data():
-    if os.path.exists(USER_FILE):
-        with open(USER_FILE, "r") as f:
+    if os.path.exists(USER_DATA_FILE):
+        with open(USER_DATA_FILE, "r") as f:
             return json.load(f)
-    return {}
+    return {"name": "", "age": "", "last_mood": "", "last_interaction": str(datetime.utcnow()), "missed_days": 0}
 
-def save_user_data(data):
-    with open(USER_FILE, "w") as f:
-        json.dump(data, f, indent=2)
 
-def fetch_quote():
-    try:
-        r = requests.get(QUOTE_API, timeout=5)
-        if r.status_code == 200:
-            q = r.json()
-            return f"“{q['content']}” — {q['author']}"
-    except Exception:
-        pass
-    return random.choice(LOCAL_QUOTES)
-
-def classify_emotion(text):
-    try:
-        result = emotion_classifier(text)
-        return result[0]["label"].lower()
-    except Exception:
-        return "neutral"
-
-def get_color_for_emotion(e):
-    palette = {
-        "happy": "#fff59d",
-        "joy": "#fff59d",
-        "sad": "#90caf9",
-        "angry": "#ff8a65",
-        "calm": "#a5d6a7",
-        "motivated": "#ffcc80",
-        "neutral": "#eeeeee",
-    }
-    return palette.get(e.lower(), "#f5f5f5")
-
-# --- Core logic ---
-def chat_with_bot(message, name, age, audio=None):
-    if not message and audio:
-        # if user spoke instead of typed
-        import speech_recognition as sr
-        recognizer = sr.Recognizer()
-        with sr.AudioFile(audio) as src:
-            audio_data = recognizer.record(src)
-        try:
-            message = recognizer.recognize_google(audio_data)
-        except Exception:
-            message = "..."
-
-    if not message:
-        return "Please say or type something.", None, None
-
-    user_id = "default"
-    users = load_user_data()
-    user = users.get(user_id, {"name": name, "age": age, "recent_mood": "neutral"})
-    emotion = classify_emotion(message)
-
-    # Motivational triggers
-    if "motivate" in message.lower() or "guidance" in message.lower():
-        quote = fetch_quote()
-        user["recent_mood"] = "motivated"
-        users[user_id] = user
-        save_user_data(users)
-        return quote, None, get_color_for_emotion("motivated")
-
-    # Normal empathetic reply
-    prompt = f"""
-You are a warm, empathetic emotional support companion.
-The user's name is {name}, age {age}, currently feeling {emotion}.
-Reply with kindness, encouragement, and positivity.
-Avoid therapy or diagnosis.
-
-User: {message}
-Assistant:
-"""
-    try:
-        response = openai.ChatCompletion.create(
+def save_user_data(data):
+    with open(USER_DATA_FILE, "w") as f:
+        json.dump(data, f)
+
+
+@app.route("/chat", methods=["POST"])
+def respond():
+    data = request.get_json()
+    message = data.get("message", "")
+    user_name = data.get("name", "friend")
+    user_age = data.get("age", "unknown")
+
+    user_data = load_user_data()
+
+    # Emotion detection
+    inputs = tokenizer(message, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**inputs)
+    emotion_label = emotion_labels[int(torch.argmax(outputs.logits))]
+
+    # Motivational or guidance request
+    motivational_keywords = ["give me guidance", "i need motivation", "motivate me", "inspire me"]
+    if any(kw in message.lower() for kw in motivational_keywords):
+        quote = random.choice(motivational_quotes)
+        ai_reply = f"Here’s something to lift your spirit, {user_name}: {quote}"
+    else:
+        # Empathetic chat completion
+        system_prompt = f"You are a warm, kind, and empathetic emotional support assistant for {user_name}, age {user_age}. Be comforting and understanding, but not a therapist."
+        response = client.chat.completions.create(
             model="gpt-4o-mini",
-            messages=[{"role": "user", "content": prompt}],
-            temperature=0.8
+            messages=[
+                {"role": "system", "content": system_prompt},
+                {"role": "user", "content": message},
+            ],
         )
-        reply = response.choices[0].message["content"].strip()
-    except Exception:
-        reply = "I'm here for you. Tell me how you’re feeling today."
-
-    # Save user state
-    user["recent_mood"] = emotion
-    user["last_active"] = datetime.utcnow().strftime("%Y-%m-%d")
-    users[user_id] = user
-    save_user_data(users)
-
-    return reply, reply, get_color_for_emotion(emotion)
-
-# --- Gradio UI ---
-with gr.Blocks(title="Empathetic Voice Chatbot 🌼") as app:
-    gr.Markdown(
-        "## 🌼 Empathetic Voice Chatbot\n"
-        "Speak or type your feelings — your friendly listener will respond with warmth and understanding."
-    )
-
-    with gr.Row():
-        name = gr.Textbox(label="Your Name", value="Alex")
-        age = gr.Number(label="Your Age", value=25)
-
-    chatbox = gr.Textbox(label="Type your message", placeholder="Say something or click record below...")
-    mic = gr.Audio(sources=["microphone"], type="filepath", label="🎤 Speak here")
-    output_text = gr.Textbox(label="Assistant's Response")
-    audio_reply = gr.Audio(label="🔊 Spoken Reply")
-
-    send_btn = gr.Button("Send / Talk 💬")
-
-    def respond(msg, n, a, aud):
-        text_reply, tts_text, color = chat_with_bot(msg, n, a, aud)
-        if tts_text:
-            speech = openai.audio.speech.with_streaming_response.create(
-                model="gpt-4o-mini-tts",
-                voice="alloy",
-                input=tts_text
-            )
-            out_path = "reply.mp3"
-            import requests
-
-speech = requests.get(speech_url)
-with open("speech.mp3", "wb") as f:
-    f.write(speech.content)
+        ai_reply = response.choices[0].message.content.strip()
+
+    # Daily check-in system
+    last_time = datetime.fromisoformat(user_data.get("last_interaction", str(datetime.utcnow())))
+    now = datetime.utcnow()
+    days_diff = (now - last_time).days
+
+    if days_diff >= 1:
+        user_data["missed_days"] += days_diff
+    else:
+        user_data["missed_days"] = 0
+
+    user_data.update({
+        "name": user_name,
+        "age": user_age,
+        "last_mood": emotion_label,
+        "last_interaction": str(now)
+    })
+    save_user_data(user_data)
+
+    if user_data["missed_days"] >= 3:
+        ai_reply = f"We missed you, {user_name}! Hope you’re okay. Remember, I’m always here to listen 💛"
+
+    # Optional text-to-speech (non-blocking)
+    try:
+        speech_url = "https://api.openai.com/v1/audio/speech"
+        headers = {"Authorization": f"Bearer {os.getenv('OPENAI_API_KEY')}"}
+        payload = {"model": "gpt-4o-mini-tts", "voice": "alloy", "input": ai_reply}
+        speech = requests.post(speech_url, headers=headers, json=payload)
+
+        if speech.status_code == 200:
+            with open("speech.mp3", "wb") as f:
+                f.write(speech.content)
         else:
-            out_path = None
-        # Dynamically change background color
-        app.theme = None
-        return gr.update(value=text_reply), out_path
+            print("Speech generation failed:", speech.text)
+    except Exception as e:
+        print("Speech block error:", e)
+
+    return jsonify({"response": ai_reply, "emotion": emotion_label})
+
+
+@app.route("/")
+def index():
+    return jsonify({"message": "Emotional Support Chatbot API is running!"})
 
-    send_btn.click(respond, [chatbox, name, age, mic], [output_text, audio_reply])
 
-app.launch()
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=7860)