Update app.py

app.py

@@ -13,6 +13,7 @@ import googlemaps
 import folium
 import os
 import base64
+import torch  # Added missing import for torch
 
 # Disable GPU usage for TensorFlow
 os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
@@ -71,6 +72,7 @@ def chatbot(message, history):
         else:
             response = "I'm sorry, I didn't understand that. Could you please rephrase?"
     except Exception as e:
+        print(f"Error in chatbot: {e}")  # For debugging
         response = f"An error occurred: {str(e)}"
     # Convert the new message and response to the 'messages' format
     history.append({"role": "user", "content": message})
@@ -98,7 +100,6 @@ def detect_emotion(user_input):
     result = pipe(user_input)
     emotion = result[0]['label']
     return f"Emotion Detected: {emotion}"
-
 # Initialize Google Maps API client securely
 gmaps = googlemaps.Client(key=os.getenv('GOOGLE_API_KEY'))
 
@@ -158,169 +159,81 @@ def generate_suggestions(emotion):
             {"Title": "Coping with Anxiety", "Subject": "Anxiety Management", "Link": '<a href="https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety" target="_blank">Tips for Dealing with Anxiety</a>'},
             {"Title": "Emotional Wellness Toolkit", "Subject": "Wellness", "Link": '<a href="https://www.nih.gov/health-information/emotional-wellness-toolkit" target="_blank">Emotional Wellness Toolkit</a>'},
             {"Title": "Relaxation Video", "Subject": "Video", "Link": '<a href="https://youtu.be/yGKKz185M5o" target="_blank">Watch Video</a>'}
-        ],
-        'sadness': [
-            {"Title": "Emotional Wellness Toolkit", "Subject": "Wellness", "Link": '<a href="https://www.nih.gov/health-information/emotional-wellness-toolkit" target="_blank">Emotional Wellness Toolkit</a>'},
-            {"Title": "Dealing with Anxiety", "Subject": "Anxiety Management", "Link": '<a href="https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety" target="_blank">Tips for Dealing with Anxiety</a>'},
-            {"Title": "Relaxation Video", "Subject": "Video", "Link": '<a href="https://youtu.be/-e-4Kx5px_I" target="_blank">Watch Video</a>'}
-        ],
-        'surprise': [
-            {"Title": "Managing Stress", "Subject": "Stress Management", "Link": '<a href="https://www.health.harvard.edu/health-a-to-z" target="_blank">Harvard Health: Stress Management</a>'},
-            {"Title": "Coping Strategies", "Subject": "Coping", "Link": '<a href="https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety" target="_blank">Coping with Anxiety</a>'},
-            {"Title": "Relaxation Video", "Subject": "Video", "Link": '<a href="https://youtu.be/m1vaUGtyo-A" target="_blank">Watch Video</a>'}
         ]
     }
-    return suggestions.get(emotion, [])
-
-# Define the Gradio interface
-def gradio_app(message, location, health_query, submit_button, history, state):
-    if submit_button:
-        # Chatbot interaction
-        history, _ = chatbot(message, history)
-        
-        # Sentiment analysis
-        sentiment_response = analyze_sentiment(message)
-        
-        # Emotion detection
-        emotion_response = detect_emotion(message)
-        
-        # Health professional search and map display
-        route_info, map_html = get_health_professionals_and_map(location, health_query)
-        
-        # Generate suggestions based on the detected emotion
-        suggestions = generate_suggestions(emotion_response.split(': ')[1])
-        
-        # Create a DataFrame for displaying suggestions
-        suggestions_df = pd.DataFrame(suggestions)
-        
-        # Add emoticons based on emotion
-        emoticon = {
-            'joy': '😊',
-            'anger': '😡',
-            'fear': '😨',
-            'sadness': '😢',
-            'surprise': '😲'
-        }
-        
-        # Add info graphics based on emotion
-        info_graphics = {
-            'joy': 'joy.png',
-            'anger': 'anger.png',
-            'fear': 'fear.png',
-            'sadness': 'sadness.png',
-            'surprise': 'surprise.png'
-        }
-        
-        # Convert image to base64 for embedding in HTML
-        if info_graphics.get(emotion_response.split(': ')[1]):
-            with open(info_graphics[emotion_response.split(': ')[1]], "rb") as image_file:
-                encoded_string = base64.b64encode(image_file.read()).decode()
-            info_graphic_html = f'<img src="data:image/png;base64,{encoded_string}" alt="{emotion_response.split(": ")[1]}">'
-        else:
-            info_graphic_html = ''
-        
-        return (
-            history,
-            sentiment_response,
-            f"{emotion_response} {emoticon.get(emotion_response.split(': ')[1], '')}",
-            route_info,
-            map_html,
-            gr.DataFrame(suggestions_df, headers=["Title", "Subject", "Link"]),
-            info_graphic_html,
-            state
-        )
-    else:
-        return history, "", "", "", "", gr.DataFrame([], headers=["Title", "Subject", "Link"]), "", state
-
-# Gradio UI components
-message_input = gr.Textbox(lines=1, label="Message", placeholder="Type your message here...")
-location_input = gr.Textbox(value="Honolulu, HI", label="Current Location", placeholder="Enter your current location...")
-health_query_input = gr.Textbox(value="doctor", label="Health Professional Query (e.g., doctor, health professional, well-being professional)", placeholder="Search for health professionals...")
-submit_button = gr.Button("Submit")
-
-# Updated chat history component with 'messages' type
-chat_history = gr.Chatbot(label="Well-Being Chat History", type='messages')
-
-# Outputs
-sentiment_output = gr.Textbox(label="Sentiment Analysis Result")
-emotion_output = gr.Textbox(label="Emotion Detection Result")
-route_info_output = gr.Textbox(label="Health Professionals Information")
-map_output = gr.HTML(label="Map with Health Professionals")
-suggestions_output = gr.DataFrame(label="Well-Being Suggestions", headers=["Title", "Subject", "Link"])
-info_graphic_output = gr.HTML(label="Info Graphic")
-
-# Custom CSS for styling
-custom_css = """
-<style>
-body {
-    background-color: #f0f8ff;
-    color: #333;
-    font-family: Arial, sans-serif;
-}
-h1, h2, h3, h4, h5, h6 {
-    color: #0056b3;
-}
-.gradio-app {
-    max-width: 800px;
-    margin: 0 auto;
-    padding: 20px;
-    background-color: #fff;
-    border-radius: 10px;
-    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
-}
-.gradio-input, .gradio-output {
-    margin-bottom: 15px;
-}
-.gradio-button {
-    background-color: #0056b3;
-    color: #fff;
-    border: none;
-    padding: 10px 20px;
-    border-radius: 5px;
-    cursor: pointer;
-}
-.gradio-button:hover {
-    background-color: #004080;
-}
-.gradio-dataframe {
-    border: 1px solid #ddd;
-    border-radius: 5px;
-    overflow: hidden;
-}
-.gradio-dataframe th, .gradio-dataframe td {
-    padding: 10px;
-    text-align: left;
-}
-.gradio-dataframe th {
-    background-color: #0056b3;
-    color: #fff;
-}
-.gradio-dataframe a {
-    color: #0056b3;
-    text-decoration: none;
-}
-.gradio-dataframe a:hover {
-    text-decoration: underline;
-}
-.info-graphic img {
-    max-width: 100%;
-    height: auto;
-}
-</style>
+    
+    emotion_suggestions = suggestions.get(emotion, [])
+    return pd.DataFrame(emotion_suggestions)
+# Custom CSS styling for Gradio interface
+css = """
+    body {
+        font-family: 'Arial', sans-serif;
+    }
+    .gradio-container {
+        background-color: #f7f7f7;
+    }
+    .gradio-input, .gradio-output {
+        padding: 10px;
+        border-radius: 5px;
+        background-color: #fff;
+        border: 1px solid #ccc;
+    }
+    .gradio-container .gradio-button {
+        background-color: #4CAF50;
+        color: white;
+        border-radius: 5px;
+    }
+    .gradio-container .gradio-button:hover {
+        background-color: #45a049;
+    }
+    .gradio-output {
+        font-size: 16px;
+        color: #333;
+    }
+    .gradio-container h3 {
+        color: #333;
+    }
+    .gradio-output .output {
+        border-top: 2px solid #ddd;
+    }
 """
 
-# Create Gradio interface
-demo = gr.Interface(
+# Gradio interface components
+def gradio_app(message, current_location, health_professional_query, history):
+    # Detect sentiment and emotion
+    sentiment = analyze_sentiment(message)
+    emotion = detect_emotion(message)
+    
+    # Generate suggestions based on emotion
+    suggestions_df = generate_suggestions(emotion)
+    
+    # Get health professionals details and map
+    route_info, map_html = get_health_professionals_and_map(current_location, health_professional_query)
+    
+    return sentiment, emotion, suggestions_df, route_info, map_html, history
+
+# Gradio interface setup
+iface = gr.Interface(
     fn=gradio_app,
-    inputs=[message_input, location_input, health_query_input, submit_button, gr.State(), gr.State()],
-    outputs=[chat_history, sentiment_output, emotion_output, route_info_output, map_output, suggestions_output, info_graphic_output, gr.State()],
+    inputs=[
+        gr.inputs.Textbox(lines=2, placeholder="Enter your message..."),
+        gr.inputs.Textbox(lines=2, placeholder="Enter your current location..."),
+        gr.inputs.Textbox(lines=2, placeholder="Enter health professional query..."),
+        gr.State(value=[])
+    ],
+    outputs=[
+        gr.outputs.Textbox(label="Sentiment Analysis"),
+        gr.outputs.Textbox(label="Detected Emotion"),
+        gr.outputs.Dataframe(label="Suggestions"),
+        gr.outputs.Textbox(label="Nearby Health Professionals"),
+        gr.outputs.HTML(label="Map of Health Professionals"),
+        gr.State(value=[])
+    ],
+    live=True,
     allow_flagging="never",
-    live=False,
-    title="Well-Being App: Support, Sentiment, Emotion Detection & Health Professional Search",
-    css=custom_css
+    theme="huggingface",
+    css=css  # Apply custom CSS styling
 )
 
-# Launch the Gradio interface
-if __name__ == "__main__":
-    demo.launch()
-    
+# Launch Gradio interface
+iface.launch(share=True)