Update app.py

app.py

@@ -73,11 +73,10 @@ def chatbot_response(message, history):
     history.append((message, response))
     return history, response
 
-# Hugging Face transformers model for emotion detection
+# Emotion detection transformer model
 emotion_tokenizer = AutoTokenizer.from_pretrained("j-hartmann/emotion-english-distilroberta-base")
 emotion_model = AutoModelForSequenceClassification.from_pretrained("j-hartmann/emotion-english-distilroberta-base")
 
-# Detect emotion
 def detect_emotion(user_input):
     pipe = pipeline("text-classification", model=emotion_model, tokenizer=emotion_tokenizer)
     try:
@@ -95,7 +94,7 @@ def detect_emotion(user_input):
     except Exception as e:
         return f"Error detecting emotion: {str(e)} 💥"
 
-# Sentiment analysis using Hugging Face
+# Sentiment analysis model
 sentiment_tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
 sentiment_model = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
 
@@ -115,95 +114,117 @@ def analyze_sentiment(user_input):
 def generate_suggestions(emotion):
     suggestions = {
         "😊 Joy": [
-            {"Title": "Mindful Meditation 🧘‍♂️", "Link": "https://www.helpguide.org/meditation"},
-            {"Title": "Explore a new skill 🚀", "Link": "https://www.skillshare.com/"},
+            {"Title": "Mindful Meditation 🧘", "Link": "https://www.helpguide.org/meditation"},
+            {"Title": "Learn a new skill ✨", "Link": "https://www.skillshare.com/"},
         ],
         "😢 Sadness": [
-            {"Title": "Improve mental resilience ✨", "Link": "https://www.psychologytoday.com/"},
-            {"Title": "Reach out to a therapist 💬", "Link": "https://www.betterhelp.com/"},
+            {"Title": "Talk to a professional 💬", "Link": "https://www.betterhelp.com/"},
+            {"Title": "Mental health toolkit 🛠️", "Link": "https://www.psychologytoday.com/"},
         ],
         "😠 Anger": [
-            {"Title": "Anger Management Guide 🔥", "Link": "https://www.mentalhealth.org.uk/"},
-            {"Title": "Calming Exercises 🌿", "Link": "https://www.calm.com/"},
+            {"Title": "Anger Management Tips 🔥", "Link": "https://www.mentalhealth.org.uk"},
+            {"Title": "Stress Relieving Exercises 🌿", "Link": "https://www.calm.com/"},
         ],
     }
-    return suggestions.get(emotion, [{"Title": "General Wellness Resources 🌈", "Link": "https://www.wellness.com/"}])
-
-# Main App Function
-def well_being_app(user_input, history):
-    """Main function for chatbot, emotion detection, sentiment analysis, and suggestions."""
+    return suggestions.get(emotion, [{"Title": "Wellness Resources 🌈", "Link": "https://www.helpguide.org/wellness"}])
+
+# Dummy Function for Location Query Simulation (replace this with actual map/search integration)
+def search_nearby_professionals(location, query):
+    """Simulate searching for nearby professionals and returning results."""
+    return [
+        {"Name": "Wellness Center One", "Address": "123 Wellness Way"},
+        {"Name": "Mental Health Clinic", "Address": "456 Recovery Road"},
+        {"Name": "Therapists Hub", "Address": "789 Peace Avenue"},
+    ] if location and query else []
+
+def well_being_app(user_input, location, query, history):
+    """Main function for chatbot, emotion detection, sentiment, suggestions, and location query."""
     # Chatbot response
     history, chatbot_reply = chatbot_response(user_input, history)
 
-    # Emotion detection
+    # Emotion Detection
     emotion = detect_emotion(user_input)
 
-    # Sentiment analysis
+    # Sentiment Analysis
     sentiment = analyze_sentiment(user_input)
 
-    # Generating suggestions
+    # Suggestions
     detected_emotion = emotion.split(": ")[-1]
     suggestions = generate_suggestions(detected_emotion)
     suggestions_df = pd.DataFrame(suggestions)
 
-    return history, sentiment, emotion, suggestions_df
+    # Nearby Professionals (Location Query)
+    professionals = search_nearby_professionals(location, query)
+
+    return history, sentiment, emotion, suggestions_df, professionals
 
-# Custom CSS for Beautification
+# Custom CSS for beautification
 custom_css = """
 body {
     background: linear-gradient(135deg, #28a745, #218838);
-    font-family: 'Arial', sans-serif;
+    font-family: Arial, sans-serif;
     color: black;
 }
-#component-0 span {
-    color: white;
-}
 button {
-    background-color: #20c997;
+    background-color: #1abc9c;
     color: white;
-    padding: 12px 20px;
+    padding: 10px 20px;
     font-size: 16px;
-    border-radius: 12px;
+    border-radius: 8px;
     cursor: pointer;
 }
 button:hover {
-    background-color: #17a2b8;
+    background-color: #16a085;
 }
-input[type="text"],
-textarea {
+textarea, input[type="text"] {
     background: #ffffff;
     color: #000000;
-    border: solid 1px #ced4da;
-    padding: 10px;
     font-size: 14px;
-    border-radius: 6px;
+    border: 1px solid #ced4da;
+    padding: 10px;
+    border-radius: 5px;
 }
 """
 
 # Gradio UI
 with gr.Blocks(css=custom_css) as interface:
     gr.Markdown("# 🌱 **Well-being Companion**")
-    gr.Markdown("### Empowering your well-being journey with AI 💚")
+    gr.Markdown("### Empowering Your Mental Health Journey with AI 💚")
 
+    # Input Section
     with gr.Row():
-        user_input = gr.Textbox(lines=2, placeholder="How can I support you today?", label="Your Input")
-    
+        gr.Textbox(label="Your Message", lines=2, placeholder="How can I support you today?", elem_id="message_input")
+        gr.Textbox(label="Location", placeholder="Enter your location (e.g., New York City)")
+        gr.Textbox(label="Search Query", placeholder="Professionals nearby? (e.g., doctors, therapists)")
+        submit_button = gr.Button("Submit")
+
+    # Chatbot Section
+    with gr.Row():
+        chatbot_title = "### Chatbot Response"
+        chatbot_output = gr.Chatbot(label=None)
+
+    # Sentiment and Emotion Section
     with gr.Row():
-        submit_button = gr.Button("Submit", elem_id="submit")
-    
+        gr.Markdown("### Sentiment Analysis")
+        sentiment_output = gr.Textbox(label=None)
+        gr.Markdown("### Detected Emotion")
+        emotion_output = gr.Textbox(label=None)
+
+    # Suggestions Section
     with gr.Row():
-        chatbot_out = gr.Chatbot(label="Chat History")
-        sentiment_out = gr.Textbox(label="Sentiment Analysis")
-        emotion_out = gr.Textbox(label="Detected Emotion")
-        
+        gr.Markdown("### Suggestions")
+        suggestions_output = gr.DataFrame(headers=["Title", "Link"], interactive=False, max_height=300)
+
+    # Location Search Results Section
     with gr.Row():
-        suggestions_out = gr.DataFrame(label="Suggestions", headers=["Title", "Link"])
-    
+        gr.Markdown("### Nearby Professionals")
+        location_output = gr.DataFrame(headers=["Name", "Address"], interactive=False, max_height=300)
+
     submit_button.click(
         well_being_app,
-        inputs=[user_input, chatbot_out],
-        outputs=[chatbot_out, sentiment_out, emotion_out, suggestions_out],
+        inputs=["message_input", "Location", "Search Query", chatbot_output],
+        outputs=[chatbot_output, sentiment_output, emotion_output, suggestions_output, location_output],
     )
 
-# Launch App
+# Launch the app
 interface.launch()