app.py

import os
import json
import requests
from datetime import datetime
from flask import Flask, request, jsonify, send_from_directory
from transformers import pipeline
from openai import OpenAI

# Initialize Flask
app = Flask(__name__)

# Initialize OpenAI client
api_key = os.getenv("OPENAI_API_KEY")
if not api_key:
    print("Warning: OPENAI_API_KEY not set. Fallback responses will be used.")
client = OpenAI(api_key=api_key) if api_key else None

# Emotion analysis model
try:
    emotion_model = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base")
except Exception as e:
    print(f"Emotion model error: {e}. Using fallback.")
    emotion_model = None

# Load or create user data
USER_FILE = "user_data.json"
if not os.path.exists(USER_FILE):
    with open(USER_FILE, "w") as f:
        json.dump({"name": None, "age": None, "mood": None, "last_interaction": None, "missed_days": 0, "mode": "emotional_support", "conversation_history": [], "ip": None}, f)

def load_user():
    with open(USER_FILE, "r") as f:
        return json.load(f)

def save_user(data):
    with open(USER_FILE, "w") as f:
        json.dump(data, f)

# Helpline data
HELPLINES = {
    "US": "National Suicide Prevention Lifeline: 988",
    "UK": "Samaritans: 116 123",
    "IN": "AASRA: 91-9820466726",
    "CA": "Canada Suicide Prevention Service: 988",
    "AU": "Lifeline: 13 11 14",
    "DE": "Telefonseelsorge: 0800 111 0 111",
    "FR": "SOS Amitié: 09 72 39 40 50",
    "default": "Please contact a local crisis hotline or emergency services."
}

def get_country_from_ip(ip):
    try:
        response = requests.get(f"http://ipapi.co/{ip}/country/")
        if response.status_code == 200:
            return response.text.upper()
    except:
        pass
    return "default"

def detect_critical_situation(message):
    keywords = ["suicide", "kill myself", "end my life", "self harm", "hurt myself", "crisis", "emergency", "harm others", "panic attack", "overdose"]
    return any(keyword in message.lower() for keyword in keywords)

def get_fallback_reply(mode, emotion):
    if mode == "emotional_support":
        return "I hear you. You're not alone—let's talk more." if emotion == "sadness" else "That sounds tough. I'm here to support you."
    else:
        return "For knowledge, ask a specific question. I'm here to help."

@app.route("/chat", methods=["POST"])
def chat():
    try:
        data = request.get_json()
        user_message = data.get("message", "")
        mode = data.get("mode", "emotional_support")
        voice_tone = data.get("voice_tone", "neutral tone")
        user_ip = request.remote_addr
        user = load_user()

        now = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
        user["last_interaction"] = now
        user["mode"] = mode
        user["ip"] = user_ip
        user["conversation_history"].append({"role": "user", "content": user_message, "timestamp": now})

        emotion = "neutral"
        if emotion_model:
            try:
                emotion = emotion_model(user_message)[0]["label"]
            except Exception as e:
                print(f"Emotion analysis error: {e}")
        user["mood"] = emotion

        if detect_critical_situation(user_message):
            country = get_country_from_ip(user_ip)
            helpline = HELPLINES.get(country, HELPLINES["default"])
            reply = f"I'm concerned. Call {helpline} now. Talk to someone."
            user["conversation_history"].append({"role": "assistant", "content": reply, "timestamp": now})
            save_user(user)
            return jsonify({"reply": reply, "emotion": emotion})

        history = user["conversation_history"][-10:]
        system_prompt = f"You are a supportive friend. Respond briefly, accurately, and calmly. In emotional_support mode: empathize and motivate gently. In knowledge mode: give short, factual answers. Avoid repetition. Current mood: {emotion}. Mode: {mode}. Voice tone: {voice_tone}."
        messages = [{"role": "system", "content": system_prompt}] + history

        if client:
            try:
                response = client.chat.completions.create(
                    model="gpt-3.5-turbo",
                    messages=messages,
                    max_tokens=80,
                    temperature=0.5
                )
                reply = response.choices[0].message.content.strip()
            except Exception as e:
                print(f"OpenAI API error: {e}. Using fallback.")
                reply = get_fallback_reply(mode, emotion)
        else:
            reply = get_fallback_reply(mode, emotion)

        user["conversation_history"].append({"role": "assistant", "content": reply, "timestamp": now})
        save_user(user)

        return jsonify({"reply": reply, "emotion": emotion})
    except Exception as e:
        print(f"General error: {e}")
        return jsonify({"reply": "Something went wrong. Please check your setup and try again.", "emotion": "neutral"}), 500

@app.route("/")
def index():
    return send_from_directory(".", "index.html")

if __name__ == "__main__":
    app.run(host="0.0.0.0", port=7860)