app.py

import streamlit as st
from transformers import pipeline
import torch
import time

# Enhanced Suggestion Database (Now includes resources)
suggestion_database = {
    "sadness": {
        "suggestions": ["Try a guided meditation", "Take a walk in nature", "Connect with a friend"],
        "articles": [
            {"title": "Overcoming Sadness", "url": "https://example.com/sadness1"},
            {"title": "Understanding Depression", "url": "https://example.com/sadness2"},
        ],
        "videos": [
            {"title": "Mindfulness for Sadness", "url": "https://www.youtube.com/watch?v=sadnessvideo1"},
            {"title": "Coping with Grief", "url": "https://www.youtube.com/watch?v=sadnessvideo2"},
        ],
    },
    "joy": {
        "suggestions": ["Practice gratitude", "Engage in a hobby", "Spend time with loved ones"],
        "articles": [
            {"title": "The Benefits of Joy", "url": "https://example.com/joy1"},
            {"title": "Maintaining Positive Emotions", "url": "https://example.com/joy2"},
        ],
        "videos": [
            {"title": "Boosting Your Happiness", "url": "https://www.youtube.com/watch?v=joyvideo1"},
            {"title": "Practicing Gratitude", "url": "https://www.youtube.com/watch?v=joyvideo2"},
        ],
    },
    "neutral": {
        "suggestions": ["Take a break", "Engage in a relaxing activity", "Spend time in nature"],
        "articles": [
            {"title": "Importance of Self-Care", "url": "https://example.com/selfcare1"},
            {"title": "Stress Management Techniques", "url": "https://example.com/stress1"},
        ],
        "videos": [
            {"title": "Relaxation Techniques", "url": "https://www.youtube.com/watch?v=relaxvideo1"},
            {"title": "Mindfulness Exercises", "url": "https://www.youtube.com/watch?v=mindfulnessvideo1"},
        ]
    }
}

# Function to fetch relevant resources
def get_relevant_resources(emotion):
    resources = suggestion_database.get(emotion, {})
    return resources.get("suggestions", []), resources.get("articles", []), resources.get("videos", [])

# Function to load the model with error handling and retries
@st.cache_resource
def load_model():
    try:
        st.write("Attempting to load the emotion analysis model...")
        # Using a smaller model for quick load times
        emotion_analyzer = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta", device=0 if torch.cuda.is_available() else -1)
        st.write("Model loaded successfully!")
        return emotion_analyzer
    except Exception as e:
        st.write(f"Error loading the model: {e}")
        return None

# Function to predict emotion for a single response
def predict_emotion_single(response, emotion_analyzer):
    if emotion_analyzer is None:
        st.error("Model not loaded. Please try reloading the app.")
        return {"Error": "Emotion analyzer model not initialized. Please check model loading."}

    try:
        result = emotion_analyzer([response])
        return {res["label"]: round(res["score"], 4) for res in result}
    except Exception as e:
        st.error(f"Prediction failed: {e}")
        return {"Error": f"Prediction failed: {e}"}

# Streamlit App Layout
st.title("Emotion Prediction App: Your Personal Wellness Assistant")

st.write("**How it works:**")
st.write("- Enter your thoughts or feelings.")
st.write("- Our AI analyzes your text to predict your emotional state.")
st.write("- Receive personalized suggestions to improve your well-being.")

# Define questions for the user
questions = [
    "How are you feeling today?",
    "Describe your mood in a few words.",
    "What was the most significant emotion you felt this week?"
]

# Initialize a dictionary to store responses
responses = {}

# Initialize the emotion analysis model with retries
emotion_analyzer = None
max_retries = 3
retry_delay = 5  # seconds

# Try loading the model with retries
for attempt in range(max_retries):
    emotion_analyzer = load_model()
    if emotion_analyzer:
        break
    if attempt < max_retries - 1:
        st.warning(f"Retrying model load... Attempt {attempt + 2}/{max_retries}")
        time.sleep(retry_delay)
    else:
        st.error("Model failed to load after multiple attempts. Please try again later.")

# Function to handle responses and emotion analysis
for i, question in enumerate(questions, start=1):
    user_response = st.text_input(f"Question {i}: {question}")
    if user_response:
        analysis = predict_emotion_single(user_response, emotion_analyzer)
        responses[question] = (user_response, analysis)
        st.write(f"**Your Response**: {user_response}")
        st.write(f"**Emotion Analysis**: {analysis}")

        # Based on the emotion, suggest activities, articles, and videos
        max_emotion = max(analysis, key=analysis.get) if analysis else "neutral"
        suggestions, articles, videos = get_relevant_resources(max_emotion)

        if suggestions:
            st.write(f"### 🧘 Suggested Activity: {suggestions[0]}")
        else:
            st.write("### 🧘 No suggestions available at the moment.")

        if articles:
            st.write(f"### 📚 Suggested Articles:")
            for article in articles:
                st.write(f"[{article['title']}]({article['url']})")
        else:
            st.write("### 📚 No articles available at the moment.")

        if videos:
            st.write(f"### 🎥 Suggested Videos:")
            for video in videos:
                st.write(f"[{video['title']}]({video['url']})")
        else:
            st.write("### 🎥 No videos available at the moment.")

# Provide button to clear input fields
if st.button("Clear Responses"):
    st.experimental_rerun()

# Display results once all responses are filled
if st.button("Submit Responses"):
    if responses:
        st.write("-- Emotion Analysis Results ---")
        for i, (question, (response, analysis)) in enumerate(responses.items(), start=1):
            st.write(f"**{question}**")
            st.write(f"Response: {response}")
            st.write(f"Emotion Analysis: {analysis}")