Update app.py

app.py

@@ -83,9 +83,26 @@ def chat(message, history, state):
         else:
             response = "I'm sorry, I didn't understand that. Could you please rephrase?"
 
+        # Add emoticons to the response
+        emoticon_dict = {
+            "joy": "😊",
+            "anger": "😡",
+            "fear": "😨",
+            "sadness": "😔",
+            "surprise": "😲",
+            "neutral": "😐"
+        }
+
+        # Add the emotion-related emoticon to the response
+        for tg in data["intents"]:
+            if tg['tag'] == tag:
+                emotion = tg.get('emotion', 'neutral')  # Default to neutral if no emotion is defined
+                response = f"{response} {emoticon_dict.get(emotion, '😐')}"
+                break
+        
         history.append((message, response))
 
-        # Update state to move to the next feature
+        # Transition to the next feature (sentiment analysis)
         state['step'] = 2  # Move to sentiment analysis
     except Exception as e:
         response = f"An error occurred: {str(e)}"
@@ -104,39 +121,80 @@ def analyze_sentiment(text, state):
     predicted_class = torch.argmax(outputs.logits, dim=1).item()
     sentiment = ["Negative", "Neutral", "Positive"][predicted_class]
     
-    # Update state to move to the next feature
+    # Add emoticon to sentiment
+    sentiment_emojis = {
+        "Negative": "😞",
+        "Neutral": "😐",
+        "Positive": "😊"
+    }
+    sentiment_with_emoji = f"{sentiment} {sentiment_emojis.get(sentiment, '😐')}"
+    
+    # Transition to emotion detection
     state['step'] = 3  # Move to emotion detection and suggestions
-    return sentiment, state
+    return sentiment_with_emoji, state
 
 # Load pre-trained model and tokenizer for emotion detection
 emotion_tokenizer = AutoTokenizer.from_pretrained("j-hartmann/emotion-english-distilroberta-base")
 emotion_model = AutoModelForSequenceClassification.from_pretrained("j-hartmann/emotion-english-distilroberta-base")
 
-# Function for emotion detection
+# Function for emotion detection and suggestions
 def detect_emotion(text, state):
     pipe = pipeline("text-classification", model=emotion_model, tokenizer=emotion_tokenizer)
     result = pipe(text)
     emotion = result[0]['label']
-
-    # Provide suggestions based on emotion
+    
+    # Provide suggestions based on detected emotion
     suggestions = provide_suggestions(emotion)
     
-    # Update state to move to the next feature
+    # Transition to wellness professional search
     state['step'] = 4  # Move to wellness professional search
     return emotion, suggestions, state
 
 # Suggestions based on detected emotion
 def provide_suggestions(emotion):
-    if emotion == 'joy':
-        return "You're feeling happy! Keep up the great mood!"
-    elif emotion == 'anger':
-        return "You're feeling angry. It's okay to feel this way. Let's try to calm down."
-    elif emotion == 'fear':
-        return "You're feeling fearful. Take deep breaths, everything will be okay."
-    elif emotion == 'sadness':
-        return "You're feeling sad. It's okay, things will get better. You're not alone."
-    else:
-        return "Sorry, no suggestions available for this emotion."
+    resources = {
+        'joy': {
+            'message': "You're feeling happy! Keep up the great mood! 😊",
+            'articles': [
+                "[Relaxation Techniques](https://www.helpguide.org/mental-health/meditation/mindful-breathing-meditation)",
+                "[Dealing with Stress](https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety)"
+            ],
+            'videos': "[Watch Relaxation Video](https://youtu.be/m1vaUGtyo-A)"
+        },
+        'anger': {
+            'message': "You're feeling angry. It's okay to feel this way. Let's try to calm down. 😡",
+            'articles': [
+                "[Emotional Wellness Toolkit](https://www.nih.gov/health-information/emotional-wellness-toolkit)",
+                "[Stress Management Tips](https://www.health.harvard.edu/health-a-to-z)"
+            ],
+            'videos': "[Watch Anger Management Video](https://youtu.be/MIc299Flibs)"
+        },
+        'fear': {
+            'message': "You're feeling fearful. Take a moment to breathe and relax. 😨",
+            'articles': [
+                "[Mindfulness Practices](https://www.helpguide.org/mental-health/meditation/mindful-breathing-meditation)",
+                "[Coping with Anxiety](https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety)"
+            ],
+            'videos': "[Watch Coping Video](https://youtu.be/yGKKz185M5o)"
+        },
+        'sadness': {
+            'message': "You're feeling sad. It's okay to take a break. 😔",
+            'articles': [
+                "[Emotional Wellness Toolkit](https://www.nih.gov/health-information/emotional-wellness-toolkit)",
+                "[Dealing with Anxiety](https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety)"
+            ],
+            'videos': "[Watch Sadness Relief Video](https://youtu.be/-e-4Kx5px_I)"
+        },
+        'surprise': {
+            'message': "You're feeling surprised. It's okay to feel neutral! 😲",
+            'articles': [
+                "[Managing Stress](https://www.health.harvard.edu/health-a-to-z)",
+                "[Coping Strategies](https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety)"
+            ],
+            'videos': "[Watch Stress Relief Video](https://youtu.be/m1vaUGtyo-A)"
+        }
+    }
+    return resources.get(emotion, {'message': "Stay calm. 🙂", 'articles': [], 'videos': []})
 
 # Function to find wellness professionals
 def find_wellness_professionals(location, state):
@@ -152,73 +210,84 @@ def find_wellness_professionals(location, state):
             "User Ratings Total", "Website", "Types", "Latitude", "Longitude",
             "Opening Hours", "Reviews", "Email"
         ])
-        return df, state
+        # Display results in Gradio interface
+        if not df.empty:
+            df_html = df.to_html(classes="table table-striped", index=False)
+            return f"Found wellness professionals in your area: \n{df_html}", state
+        else:
+            return "No wellness professionals found for your location. Try another search.", state
     else:
-        return pd.DataFrame(), state
+        return "Sorry, there was an issue fetching data. Please try again later.", state
 
-# The functions for scraping websites and fetching details
+# Function to fetch places data using Google Places API
 def get_all_places(query, location, radius, api_key):
-    all_results = []
-    next_page_token = None
-    while True:
-        data = get_places_data(query, location, radius, api_key, next_page_token)
-        if data:
-            results = data.get('results', [])
-            for place in results:
-                place_id = place.get("place_id")
-                name = place.get("name")
-                address = place.get("formatted_address")
-                rating = place.get("rating", "Not available")
-                business_status = place.get("business_status", "Not available")
-                user_ratings_total = place.get("user_ratings_total", "Not available")
-                website = place.get("website", "Not available")
-                types = ", ".join(place.get("types", []))
-                location = place.get("geometry", {}).get("location", {})
-                latitude = location.get("lat", "Not available")
-                longitude = location.get("lng", "Not available")
-                details = get_place_details(place_id, api_key)
-                phone_number = details.get("phone_number", "Not available")
-                email = details.get("email", "Not available")
-                all_results.append([name, address, phone_number, rating, business_status,
-                                    user_ratings_total, website, types, latitude, longitude,
-                                    details.get("opening_hours", "Not available"),
-                                    details.get("reviews", "Not available"), email])
-            next_page_token = data.get('next_page_token')
-            if not next_page_token:
-                break
-        time.sleep(2)
-    return all_results
+    url = f"https://maps.googleapis.com/maps/api/place/textsearch/json?query={query}&location={location}&radius={radius}&key={api_key}"
+    response = requests.get(url)
+    if response.status_code == 200:
+        results = response.json().get("results", [])
+        places = []
+        for place in results:
+            name = place.get("name")
+            address = place.get("formatted_address")
+            phone = place.get("formatted_phone_number", "Not available")
+            rating = place.get("rating", "Not rated")
+            business_status = place.get("business_status", "N/A")
+            user_ratings_total = place.get("user_ratings_total", "N/A")
+            website = place.get("website", "Not available")
+            types = place.get("types", [])
+            lat, lng = place.get("geometry", {}).get("location", {}).values()
+            opening_hours = place.get("opening_hours", {}).get("weekday_text", [])
+            reviews = place.get("reviews", [])
+            email = "Not available"  # Assume email is not included in the API response
+            
+            # Adding the place data to the list
+            places.append([name, address, phone, rating, business_status, user_ratings_total,
+                           website, types, lat, lng, opening_hours, reviews, email])
+        return places
+    else:
+        return []
 
-# Gradio interface
-with gr.Blocks() as demo:
-    gr.Markdown("# Wellbeing Support System")
+# Gradio interface setup
+def gradio_interface():
+    with gr.Blocks() as demo:
+        # Set title and description
+        gr.Markdown("<h1 style='text-align: center;'>Mental Health Support Chatbot 🤖</h1>")
+        gr.Markdown("<p style='text-align: center;'>Get emotional well-being suggestions and find wellness professionals nearby.</p>")
+        
+        # State to manage step transitions
+        state = gr.State({"step": 1})
+        
+        # Chat interface
+        with gr.Row():
+            chatbot = gr.Chatbot(label="Chatbot")
+            user_input = gr.Textbox(placeholder="Type your message here...", label="Your Message")
+            send_button = gr.Button("Send")
+        
+        # Output for emotion, sentiment, suggestions
+        with gr.Row():
+            sentiment_output = gr.Textbox(label="Sentiment Analysis")
+            emotion_output = gr.Textbox(label="Emotion Detection")
+            suggestions_output = gr.Textbox(label="Suggestions")
+        
+        # Input for location for wellness professionals
+        with gr.Row():
+            location_input = gr.Textbox(label="Your Location (City/Region)", placeholder="Enter your city...")
+            search_button = gr.Button("Search Wellness Professionals")
+        
+        # Button actions
+        send_button.click(chat, inputs=[user_input, chatbot, state], outputs=[chatbot, chatbot, state])
+        user_input.submit(chat, inputs=[user_input, chatbot, state], outputs=[chatbot, chatbot, state])
+        
+        send_button.click(analyze_sentiment, inputs=[user_input, state], outputs=[sentiment_output, state])
+        user_input.submit(analyze_sentiment, inputs=[user_input, state], outputs=[sentiment_output, state])
+        
+        send_button.click(detect_emotion, inputs=[user_input, state], outputs=[emotion_output, suggestions_output, state])
+        user_input.submit(detect_emotion, inputs=[user_input, state], outputs=[emotion_output, suggestions_output, state])
+        
+        search_button.click(find_wellness_professionals, inputs=[location_input, state], outputs=[suggestions_output, state])
     
-    state = gr.State({"step": 1})  # Track the flow step
-
-    with gr.Tab("Chatbot"):
-        chatbot = gr.Chatbot()
-        msg = gr.Textbox()
-        clear = gr.Button("Clear")
-        msg.submit(chat, inputs=[msg, chatbot, state], outputs=[chatbot, chatbot, state])
-        clear.click(lambda: None, None, chatbot)
-
-    with gr.Tab("Sentiment Analysis"):
-        sentiment_output = gr.Textbox(label="Sentiment:")
-        text_input = gr.Textbox(label="Enter text to analyze sentiment:")
-        analyze_button = gr.Button("Analyze Sentiment")
-        analyze_button.click(analyze_sentiment, inputs=[text_input, state], outputs=[sentiment_output, state])
-
-    with gr.Tab("Emotion Detection & Suggestions"):
-        emotion_input = gr.Textbox(label="How are you feeling today?", value="Enter your thoughts here...")
-        detect_button = gr.Button("Detect Emotion")
-        emotion_output = gr.Textbox(label="Detected Emotion:")
-        suggestions_output = gr.Textbox(label="Suggestions:")
-        detect_button.click(detect_emotion, inputs=[emotion_input, state], outputs=[emotion_output, suggestions_output, state])
-
-    with gr.Tab("Find Local Wellness Professionals"):
-        location_input = gr.Textbox(label="Enter your location:", value="Hawaii")
-        search_button = gr.Button("Search")
-        results_output = gr.Dataframe(label="Search Results")
-        search_button.click(find_wellness_professionals, inputs=[location_input, state], outputs=[results_output, state])
-
-demo.launch()
+    demo.launch()
+
+# Run the Gradio interface
+if __name__ == "__main__":
+    gradio_interface()