Update app.py

app.py

@@ -1,7 +1,7 @@
 import streamlit as st
 from transformers import pipeline
 
-# Emotion classifier
+# Emotion classifier (use a pre-trained model from Hugging Face)
 emotion_analyzer = pipeline("text-classification", model="distilbert-base-uncased")
 
 # Enhanced Suggestion Database with resources
@@ -67,8 +67,8 @@ def main():
     question_2 = st.text_input("What's something that is currently on your mind?")
     question_3 = st.text_input("Do you feel overwhelmed or calm right now?")
 
+    # Step 2: Analyze sentiment of responses (only proceed if all questions are answered)
     if question_1 and question_2 and question_3:
-        # Step 2: Analyze sentiment of responses
         text_to_analyze = f"{question_1} {question_2} {question_3}"
         analysis_result = emotion_analyzer(text_to_analyze)
         emotion = analysis_result[0]['label']  # Get the emotion from the analysis result
@@ -85,6 +85,8 @@ def main():
         
         # Step 3: Suggest activities, articles, and videos
         resources = suggest_activity({emotion: 1})
+        
+        # Display suggestions, articles, and videos
         st.write("Suggestions:")
         for suggestion in resources["suggestions"]:
             st.write(f"- {suggestion}")
@@ -96,7 +98,8 @@ def main():
         st.write("Videos:")
         for video in resources["videos"]:
             st.write(f"- [{video['title']}]({video['url']})")
+    else:
+        st.write("Please answer all three questions to receive suggestions.")
 
 if __name__ == "__main__":
     main()
-