Update app.py

app.py

@@ -1,9 +1,6 @@
 import streamlit as st
-from transformers import pipeline
-import torch
-import time
 
-# Enhanced Suggestion Database (Now includes resources)
+# Suggestion Database
 suggestion_database = {
     "sadness": {
         "suggestions": ["Try a guided meditation", "Take a walk in nature", "Connect with a friend"],
@@ -36,118 +33,64 @@ suggestion_database = {
         "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
+# Function to fetch 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...")
-        # Check if CUDA (GPU) is available, if not use CPU
-        device = 0 if torch.cuda.is_available() else -1
-        emotion_analyzer = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta", device=device)
-        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}")
+# Suggestion Logic
+def suggest_activity(emotion_analysis):
+    max_emotion = max(emotion_analysis, key=emotion_analysis.get) if emotion_analysis else "neutral"
+    suggestions, articles, videos = get_relevant_resources(max_emotion)
+    return {
+        "emotion": max_emotion,
+        "suggestions": suggestions,
+        "articles": articles,
+        "videos": videos,
+    }
 
-        # 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)
+# Streamlit Interface
+st.title("Personalized Emotional Wellness Recommendations")
 
-        if suggestions:
-            st.write(f"### 🧘 Suggested Activity: {suggestions[0]}")
-        else:
-            st.write("### 🧘 No suggestions available at the moment.")
+# Questions
+st.write("### How are you feeling today?")
+emotion_input = st.text_input("Your response (e.g., happy, sad, neutral):", "").lower()
 
-        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.")
+st.write("### Describe your mood in a few words.")
+mood_input = st.text_input("Your response (e.g., calm, frustrated, joyful):", "").lower()
 
-        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.")
+st.write("### What was the most significant emotion you felt this week?")
+emotion_week_input = st.text_input("Your response (e.g., sadness, joy, anxiety):", "").lower()
 
-# Provide button to clear input fields
-if st.button("Clear Responses"):
-    st.experimental_rerun()
+# Simulated Emotion Analysis (Here, based on user input; replace with real analysis later)
+emotion_analysis = {
+    "sadness": emotion_input == "sad" or emotion_week_input == "sadness",
+    "joy": emotion_input == "happy" or emotion_week_input == "joy",
+    "neutral": emotion_input == "neutral" or emotion_week_input == "calm",
+}
 
-# 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}")
+# Analyze and Provide Suggestions
+if st.button("Get Suggestions"):
+    analysis_results = {
+        "sadness": emotion_analysis.get("sadness", 0),
+        "joy": emotion_analysis.get("joy", 0),
+        "neutral": emotion_analysis.get("neutral", 0),
+    }
 
+    suggestions = suggest_activity(analysis_results)
+    st.write(f"### Detected Emotion: {suggestions['emotion'].capitalize()}")
+    st.write("### Suggestions for You:")
+    for suggestion in suggestions["suggestions"]:
+        st.write(f"- {suggestion}")
+    
+    st.write("### Articles to Explore:")
+    for article in suggestions["articles"]:
+        st.markdown(f"[{article['title']}]({article['url']})")
+    
+    st.write("### Videos to Watch:")
+    for video in suggestions["videos"]:
+        st.markdown(f"[{video['title']}]({video['url']})")