Update app.py

app.py

@@ -40,7 +40,7 @@ suggestion_database = {
     }
 }
 
-# Function to fetch relevant resources (placeholder - needs actual API calls or database)
+# 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", [])
@@ -50,8 +50,9 @@ def get_relevant_resources(emotion):
 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)
+        # 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:
@@ -63,7 +64,7 @@ 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}
@@ -72,8 +73,12 @@ def predict_emotion_single(response, emotion_analyzer):
         return {"Error": f"Prediction failed: {e}"}
 
 # Streamlit App Layout
-st.title("Behavior Prediction App")
-st.write("Enter your thoughts or feelings, and let the app predict your emotional states.")
+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 = [
@@ -109,25 +114,25 @@ for i, question in enumerate(questions, start=1):
         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:")
+            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:")
+            st.write(f"### 🎥 Suggested Videos: ")
             for video in videos:
                 st.write(f"[{video['title']}]({video['url']})")
         else: