Update app.py

app.py

@@ -1,6 +1,10 @@
 import streamlit as st
 import os
 
+# Debugging Logger
+def debug_log(message):
+    st.text(f"DEBUG: {message}")
+
 # Suggestion Database
 suggestion_database = {
     "sadness": {
@@ -38,73 +42,50 @@ suggestion_database = {
     },
 }
 
-# Function to fetch relevant resources
+# Function to Fetch Suggestions
 def get_relevant_resources(emotion):
-    return suggestion_database.get(emotion, {"suggestions": [], "articles": [], "videos": []})
-
-# Debugging Model Initialization
-def load_emotion_model(model_path="path/to/model"):
-    if not os.path.exists(model_path):
-        st.error(f"Model file not found at {model_path}")
-        return None
+    return suggestion_database.get(emotion, suggestion_database["neutral"])
 
+# Placeholder for Model Loading
+def load_emotion_model(model_path):
     try:
-        # Replace with actual model loading logic
-        model = "Dummy model loaded"
-        st.success("Model loaded successfully!")
-        return model
+        # Placeholder logic: Replace this with actual model loading code
+        if not os.path.exists(model_path):
+            raise FileNotFoundError(f"Model file not found at {model_path}")
+        debug_log("Model loaded successfully!")
+        return "Emotion Model Placeholder"
     except Exception as e:
-        st.error(f"Error loading model: {e}")
+        debug_log(str(e))
         return None
 
-# Analyze User Input and Provide Suggestions
-def analyze_emotion(user_input, model):
-    if model is None:
-        return "neutral"  # Default to neutral if model failed to load
-    try:
-        # Dummy emotion analysis (replace with model prediction logic)
-        if "sad" in user_input.lower():
-            return "sadness"
-        elif "happy" in user_input.lower() or "joy" in user_input.lower():
-            return "joy"
-        else:
-            return "neutral"
-    except Exception as e:
-        st.error(f"Error during emotion analysis: {e}")
-        return "neutral"
-
-# Streamlit Interface
-def main():
-    st.title("Emotion-Based Suggestions")
-    
-    # Load Model
-    st.sidebar.title("Model Loader")
-    model_path = st.sidebar.text_input("Model Path", "path/to/model")
-    model = load_emotion_model(model_path)
-
-    # User Input
-    st.header("How are you feeling today?")
-    user_input = st.text_input("Describe your mood in a few words:")
-    
-    if user_input:
-        # Analyze Emotion
-        emotion = analyze_emotion(user_input, model)
-        st.subheader(f"Detected Emotion: {emotion.capitalize()}")
+# Streamlit UI
+st.title("Emotion-Based Suggestions")
 
-        # Fetch and Display Suggestions
-        resources = get_relevant_resources(emotion)
-        st.subheader("Suggestions for You:")
-        for suggestion in resources["suggestions"]:
-            st.write(f"- {suggestion}")
+# Sidebar for Model Path
+model_path = st.sidebar.text_input("Enter Model Path", "path/to/model")
 
-        st.subheader("Articles to Explore:")
-        for article in resources["articles"]:
-            st.write(f"- [{article['title']}]({article['url']})")
+# Load Model
+emotion_model = load_emotion_model(model_path)
+if emotion_model is None:
+    st.error("Model failed to load. Please check the path and try again.")
 
-        st.subheader("Videos to Watch:")
-        for video in resources["videos"]:
-            st.write(f"- [{video['title']}]({video['url']})")
+# Emotion Analysis Inputs
+st.header("How are you feeling today?")
+user_response = st.text_input("Describe your current emotion (e.g., happy, sad, neutral):", "neutral")
 
-# Run the App
-if __name__ == "__main__":
-    main()
+# Get Suggestions
+if user_response:
+    resources = get_relevant_resources(user_response.lower())
+    st.subheader("Here are some suggestions for you:")
+    
+    st.write("**Activities:**")
+    for suggestion in resources["suggestions"]:
+        st.write(f"- {suggestion}")
+    
+    st.write("**Articles:**")
+    for article in resources["articles"]:
+        st.write(f"- [{article['title']}]({article['url']})")
+    
+    st.write("**Videos:**")
+    for video in resources["videos"]:
+        st.write(f"- [{video['title']}]({video['url']})")