app.py

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

app = Flask(__name__)
client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))

# Load Hugging Face emotion model
emotion_analyzer = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base", top_k=None)

USER_DATA_FILE = "user_data.json"

# ---------------------- UTILITIES -------------------------
def load_user_data():
    if os.path.exists(USER_DATA_FILE):
        with open(USER_DATA_FILE, "r") as f:
            return json.load(f)
    return {"name": None, "age": None, "mood": None, "last_interaction": None, "missed_days": 0}

def save_user_data(data):
    with open(USER_DATA_FILE, "w") as f:
        json.dump(data, f, indent=4)

def detect_emotion(text):
    result = emotion_analyzer(text)
    top_emotion = sorted(result[0], key=lambda x: x["score"], reverse=True)[0]["label"]
    return top_emotion.lower()

def crisis_check(user_input, location="global"):
    crisis_keywords = ["kill myself", "end my life", "suicide", "die", "worthless"]
    if any(kw in user_input.lower() for kw in crisis_keywords):
        if location == "india":
            return ("I'm really sorry you’re feeling like this. You are not alone. "
                    "You can reach out to AASRA Helpline at 91-9820466726 or Snehi at 91-9582208181.")
        elif location == "us":
            return ("It sounds like you’re going through a really difficult time. "
                    "If you are in the U.S., please call or text 988 to connect with the Suicide and Crisis Lifeline.")
        else:
            return ("I’m so sorry you’re in pain right now. You are not alone. "
                    "Please reach out to a local suicide helpline or emergency number right away.")
    return None

def days_since_last_interaction(user_data):
    if not user_data.get("last_interaction"):
        return None
    last = datetime.fromisoformat(user_data["last_interaction"])
    return (datetime.now() - last).days

# ---------------------- PERSONALITY SYSTEM -------------------------
PERSONALITIES = {
    "calm": {
        "tone": "gentle, understanding, patient",
        "style": "Uses short, soft phrases and empathy-driven responses."
    },
    "friendly": {
        "tone": "warm, chatty, and supportive",
        "style": "Uses casual language and light humor to uplift users."
    },
    "deep": {
        "tone": "reflective, philosophical, soulful",
        "style": "Encourages self-reflection and growth."
    },
    "spiritual": {
        "tone": "peaceful, grounding, and nurturing",
        "style": "Focuses on mindfulness, acceptance, and compassion."
    }
}

def generate_personality_prompt(personality):
    p = PERSONALITIES.get(personality, PERSONALITIES["calm"])
    return f"You are an emotional support AI with a {p['tone']} tone. {p['style']} Respond to users with empathy and variation."

# ---------------------- RESPONSE LOGIC -------------------------
@app.route("/chat", methods=["POST"])
def chat():
    user_input = request.json.get("message", "")
    personality = request.json.get("personality", "calm")
    location = request.json.get("location", "global")

    user_data = load_user_data()

    # Crisis detection
    crisis_response = crisis_check(user_input, location)
    if crisis_response:
        return jsonify({"response": crisis_response, "emotion": "worried"})

    # Daily check-in system
    days_passed = days_since_last_interaction(user_data)
    if days_passed is not None:
        if days_passed >= 3:
            reminder = "We missed you these past few days. How have you been feeling lately?"
        elif days_passed == 1 and user_data.get("mood") in ["sad", "angry", "worried"]:
            reminder = f"Hey {user_data.get('name','friend')}, you seemed a bit down yesterday. How are you feeling today?"
        else:
            reminder = None
    else:
        reminder = None

    # Emotion detection
    emotion = detect_emotion(user_input)
    user_data["mood"] = emotion
    user_data["last_interaction"] = datetime.now().isoformat()
    save_user_data(user_data)

    # OpenAI response generation
    system_prompt = generate_personality_prompt(personality)
    prompt = f"""
    The user said: "{user_input}"
    Their name: {user_data.get('name')}
    Their age: {user_data.get('age')}
    Their recent mood: {user_data.get('mood')}
    Your goal: offer empathetic emotional support, avoid repetition, vary expressions naturally.
    """

    openai_reply = None
    try:
        response = client.chat.completions.create(
            model="gpt-4o-mini",
            messages=[
                {"role": "system", "content": system_prompt},
                {"role": "user", "content": prompt}
            ],
            temperature=0.9
        )
        openai_reply = response.choices[0].message.content.strip()
    except Exception as e:
        print("Error with OpenAI API:", e)
        openai_reply = "I'm here for you, even though I’m having a bit of trouble expressing myself right now."

    # Combine reminder if needed
    if reminder:
        final_reply = f"{reminder} {openai_reply}"
    else:
        final_reply = openai_reply

    return jsonify({"response": final_reply, "emotion": emotion})

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

@app.route("/<path:path>")
def static_files(path):
    return send_from_directory(".", path)

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