app.py

import gradio as gr
import openai
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")

emotion_classifier = pipeline("text-classification",
                              model="j-hartmann/emotion-english-distilroberta-base")

USER_FILE = "user_data.json"
QUOTE_API = "https://api.quotable.io/random"

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:
            return json.load(f)
    return {}

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(
            model="gpt-4o-mini",
            messages=[{"role": "user", "content": prompt}],
            temperature=0.8
        )
        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"
            with open(out_path, "wb") as f:
                f.write(speech.read())
        else:
            out_path = None
        # Dynamically change background color
        app.theme = None
        return gr.update(value=text_reply), out_path

    send_btn.click(respond, [chatbox, name, age, mic], [output_text, audio_reply])

app.launch()