Update app.py

app.py

@@ -1,4 +1,5 @@
 import streamlit as st
+import os
 
 # Suggestion Database
 suggestion_database = {
@@ -37,60 +38,73 @@ suggestion_database = {
     },
 }
 
-# Function to fetch resources
+# 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", [])
+    return suggestion_database.get(emotion, {"suggestions": [], "articles": [], "videos": []})
 
-# 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,
-    }
+# 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
 
-# Streamlit Interface
-st.title("Personalized Emotional Wellness Recommendations")
+    try:
+        # Replace with actual model loading logic
+        model = "Dummy model loaded"
+        st.success("Model loaded successfully!")
+        return model
+    except Exception as e:
+        st.error(f"Error loading model: {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"
 
-# Questions
-st.write("### How are you feeling today?")
-emotion_input = st.text_input("Your response (e.g., happy, sad, neutral):", "").lower()
+# 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)
 
-st.write("### Describe your mood in a few words.")
-mood_input = st.text_input("Your response (e.g., calm, frustrated, joyful):", "").lower()
+    # 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()}")
 
-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()
+        # Fetch and Display Suggestions
+        resources = get_relevant_resources(emotion)
+        st.subheader("Suggestions for You:")
+        for suggestion in resources["suggestions"]:
+            st.write(f"- {suggestion}")
 
-# 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",
-}
+        st.subheader("Articles to Explore:")
+        for article in resources["articles"]:
+            st.write(f"- [{article['title']}]({article['url']})")
 
-# 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),
-    }
+        st.subheader("Videos to Watch:")
+        for video in resources["videos"]:
+            st.write(f"- [{video['title']}]({video['url']})")
 
-    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']})")
+# Run the App
+if __name__ == "__main__":
+    main()