Update app.py

app.py

@@ -1,69 +1,10 @@
 import streamlit as st
 import os
 
-# Set Page Configuration
-st.set_page_config(
-    page_title="Emotion Prediction App",
-    page_icon="🌟",
-    layout="wide",
-)
-
-# Custom CSS for Background and Styling
-def add_custom_styles():
-    st.markdown("""
-        <style>
-            /* Background Styling */
-            .stApp {
-                background: linear-gradient(to right, #f2f7ff, #d9faff);
-                color: #333333;
-                font-family: Arial, sans-serif;
-            }
-
-            /* Header Styling */
-            .main-title {
-                color: #1e88e5;
-                text-align: center;
-                font-size: 3rem;
-                margin-top: -20px;
-                margin-bottom: 30px;
-                font-weight: bold;
-            }
-
-            /* Section Headers */
-            .section-header {
-                color: #0d47a1;
-                font-size: 1.8rem;
-                margin-top: 20px;
-                margin-bottom: 10px;
-                border-bottom: 2px solid #1e88e5;
-            }
-
-            /* Suggestions Styling */
-            .suggestions {
-                font-size: 1.1rem;
-                line-height: 1.6;
-            }
-
-            /* Footer Styling */
-            .footer {
-                text-align: center;
-                margin-top: 50px;
-                font-size: 0.9rem;
-                color: #555;
-            }
-        </style>
-    """, unsafe_allow_html=True)
-
-add_custom_styles()
-
-# Debugging Logger
-def debug_log(message):
-    st.sidebar.text(f"DEBUG: {message}")
-
 # Suggestion Database
 suggestion_database = {
     "sadness": {
-        "suggestions": ["Try guided meditation", "Take a walk in nature", "Connect with a friend"],
+        "suggestions": ["Try a guided meditation", "Take a walk in nature", "Connect with a friend"],
         "articles": [
             {"title": "Overcoming Sadness", "url": "https://example.com/sadness1"},
             {"title": "Understanding Depression", "url": "https://example.com/sadness2"},
@@ -97,62 +38,73 @@ suggestion_database = {
     },
 }
 
-# Function to Fetch Relevant Suggestions
+# Function to fetch relevant resources
 def get_relevant_resources(emotion):
-    return suggestion_database.get(emotion, suggestion_database["neutral"])
+    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
 
-# Placeholder for Model Loading
-def load_emotion_model(model_path):
     try:
-        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"
+        # Replace with actual model loading logic
+        model = "Dummy model loaded"
+        st.success("Model loaded successfully!")
+        return model
     except Exception as e:
-        debug_log(str(e))
+        st.error(f"Error loading model: {e}")
         return None
 
-# Main Application Header
-st.markdown('<div class="main-title">Emotion Prediction & Suggestions 🌈</div>', unsafe_allow_html=True)
-st.markdown("""
-This app is designed to help you discover activities, articles, and videos tailored to your emotional state.  
-Simply describe how you're feeling, and let us provide uplifting and insightful suggestions.  
-""")
-
-# Sidebar for Model Path Input
-st.sidebar.header("Emotion Model Configuration")
-model_path = st.sidebar.text_input("Enter the Path to the Emotion Prediction Model", "path/to/model")
-
-# Load Emotion Prediction Model
-emotion_model = load_emotion_model(model_path)
-if emotion_model is None:
-    st.sidebar.error("Model failed to load. Please check the path or contact support.")
-
-# Emotion Analysis Section
-st.markdown('<div class="section-header">How Are You Feeling Today?</div>', unsafe_allow_html=True)
-user_response = st.text_input("Describe your current emotion (e.g., happy, sad, neutral):", "neutral")
-
-# Display Suggestions
-if user_response:
-    resources = get_relevant_resources(user_response.lower())
-    st.markdown('<div class="section-header">Here Are Some Suggestions for You:</div>', unsafe_allow_html=True)
-
-    # Suggestions
-    st.markdown("#### Activities:")
-    st.markdown('<div class="suggestions">', unsafe_allow_html=True)
-    for suggestion in resources["suggestions"]:
-        st.write(f"- {suggestion}")
-    st.markdown('</div>', unsafe_allow_html=True)
-
-    # Articles
-    st.markdown("#### Articles:")
-    for article in resources["articles"]:
-        st.write(f"- [{article['title']}]({article['url']})")
-
-    # Videos
-    st.markdown("#### Videos:")
-    for video in resources["videos"]:
-        st.write(f"- [{video['title']}]({video['url']})")
-
-# Footer
-st.markdown('<div class="footer">Crafted with ❤️ by Your Emotion App Team</div>', unsafe_allow_html=True)
+# 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()}")
+
+        # Fetch and Display Suggestions
+        resources = get_relevant_resources(emotion)
+        st.subheader("Suggestions for You:")
+        for suggestion in resources["suggestions"]:
+            st.write(f"- {suggestion}")
+
+        st.subheader("Articles to Explore:")
+        for article in resources["articles"]:
+            st.write(f"- [{article['title']}]({article['url']})")
+
+        st.subheader("Videos to Watch:")
+        for video in resources["videos"]:
+            st.write(f"- [{video['title']}]({video['url']})")
+
+# Run the App
+if __name__ == "__main__":
+    main()