Update app.py

app.py

@@ -13,15 +13,15 @@ import googlemaps
 import folium
 import torch
 
-# Disable GPU usage for TensorFlow and logs
+# Suppress TensorFlow GPU usage and warnings
 os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
 os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-nltk.download("punkt")
 
-# Initialize Stemmer
+# Download NLTK resources
+nltk.download("punkt")
 stemmer = LancasterStemmer()
 
-# Load Chatbot Training Data
+# Load chatbot training data
 with open("intents.json") as file:
     intents_data = json.load(file)
 
@@ -37,17 +37,16 @@ net = tflearn.regression(net)
 chatbot_model = tflearn.DNN(net)
 chatbot_model.load("MentalHealthChatBotmodel.tflearn")
 
-# Sentiment and Emotion Detection Models
+# Hugging Face sentiment and emotion models
 tokenizer_sentiment = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
 model_sentiment = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
-
 tokenizer_emotion = AutoTokenizer.from_pretrained("j-hartmann/emotion-english-distilroberta-base")
 model_emotion = AutoModelForSequenceClassification.from_pretrained("j-hartmann/emotion-english-distilroberta-base")
 
-# Google Maps API for Nearby Professionals
+# Google Maps API Client
 gmaps = googlemaps.Client(key=os.getenv('GOOGLE_API_KEY'))
 
-# Chatbot Helper
+# Bag of Words Helper Function for Chatbot
 def bag_of_words(s, words):
     bag = [0] * len(words)
     s_words = word_tokenize(s)
@@ -58,8 +57,9 @@ def bag_of_words(s, words):
                 bag[i] = 1
     return np.array(bag)
 
+# Chatbot Function
 def chatbot(message, history):
-    """Generate a chatbot response and update history."""
+    """Generate a chatbot response and append to the chat history."""
     history = history or []
     try:
         result = chatbot_model.predict([bag_of_words(message, words)])
@@ -74,8 +74,9 @@ def chatbot(message, history):
     history.append((message, response))
     return history, response
 
+# Sentiment Analysis
 def analyze_sentiment(user_input):
-    """Detect sentiment and return sentiment emoji."""
+    """Analyze sentiment from user input."""
     inputs = tokenizer_sentiment(user_input, return_tensors="pt")
     with torch.no_grad():
         outputs = model_sentiment(**inputs)
@@ -83,8 +84,9 @@ def analyze_sentiment(user_input):
     sentiment_map = ["Negative 😔", "Neutral 😐", "Positive 😊"]
     return sentiment_map[sentiment_class]
 
+# Emotion Detection
 def detect_emotion(user_input):
-    """Detect user emotion based on input."""
+    """Detect user emotion with an emoji."""
     pipe = pipeline("text-classification", model=model_emotion, tokenizer=tokenizer_emotion)
     result = pipe(user_input)
     emotion = result[0]['label'].lower()
@@ -96,82 +98,130 @@ def detect_emotion(user_input):
         "surprise": "😲 Surprise",
         "neutral": "😐 Neutral"
     }
-    return emotion_map.get(emotion, "Unknown 🤔")
+    return emotion_map.get(emotion, "Unknown Emotion 🤔")
 
+# Generate Suggestions for Emotion
 def generate_suggestions(emotion):
-    """Provide clickable suggestions for each emotion."""
+    """Return suggestions for the detected emotion."""
     suggestions = {
         "joy": [
-            ["Relaxation Techniques", '<a href="https://www.helpguide.org/mental-health/meditation/mindful-breathing-meditation" target="_blank">Visit</a>'],
-            ["Emotional Wellness Toolkit", '<a href="https://www.nih.gov" target="_blank">Visit</a>'],
-            ["Relaxation Video", '<a href="https://youtu.be/m1vaUGtyo-A" target="_blank">Watch</a>'],
+            ["Relaxation Techniques", '<a href="https://www.helpguide.org/mental-health/meditation" target="_blank">Visit</a>'],
+            ["Dealing with Stress", '<a href="https://www.helpguide.org/mental-health/anxiety" target="_blank">Visit</a>'],
+            ["Emotional Wellness Toolkit", '<a href="https://www.nih.gov/health-information/emotional-wellness-toolkit" target="_blank">Visit</a>'],
+            ["Relaxation Video", '<a href="https://youtu.be/m1vaUGtyo-A" target="_blank">Watch</a>']
         ],
         "anger": [
-            ["Stress Management", '<a href="https://www.health.harvard.edu" target="_blank">Visit</a>'],
-            ["Dealing with Anger", '<a href="https://www.helpguide.org" target="_blank">Visit</a>']
+            ["Emotional Toolkit", '<a href="https://www.nih.gov/health-information/emotional-wellness-toolkit" target="_blank">Visit</a>'],
+            ["Calming Activities", '<a href="https://youtu.be/MIc299Flibs" target="_blank">Watch</a>']
         ],
         "fear": [
-            ["Coping with Anxiety", '<a href="https://www.helpguide.org" target="_blank">Visit</a>'],
-            ["Mindfulness Video", '<a href="https://youtu.be/yGKKz185M5o" target="_blank">Watch</a>']
+            ["Coping with Anxiety", '<a href="https://www.helpguide.org/mental-health/anxiety" target="_blank">Visit</a>'],
+            ["Mindfulness Practices", '<a href="https://youtu.be/yGKKz185M5o" target="_blank">Watch</a>']
         ],
         "sadness": [
-            ["Overcoming Sadness", '<a href="https://youtu.be/-e-4Kx5px_I" target="_blank">Watch</a>']
+            ["Stress Management", '<a href="https://youtu.be/-e-4Kx5px_I" target="_blank">Watch</a>']
         ],
         "surprise": [
-            ["Stress Tips", '<a href="https://youtu.be/m1vaUGtyo-A" target="_blank">Watch</a>']
+            ["Managing Stress", '<a href="https://www.health.harvard.edu/" target="_blank">Visit</a>'],
+            ["Relaxation Help", '<a href="https://youtu.be/m1vaUGtyo-A" target="_blank">Watch</a>']
         ]
     }
-    return suggestions.get(emotion.lower(), [["No suggestions available", ""]])
+    return suggestions.get(emotion.lower(), [["No suggestions are available.", ""]])
 
+# Search for Nearby Professionals and Generate Map
 def get_health_professionals_and_map(location, query):
-    """Show nearby professionals and interactive map."""
-    geo_location = gmaps.geocode(location)
-    if geo_location:
-        lat, lng = geo_location[0]["geometry"]["location"].values()
-        map_ = folium.Map(location=(lat, lng), zoom_start=13)
-        professionals = []
-        places_result = gmaps.places_nearby(location=(lat, lng), radius=10000, keyword=query)["results"]
-        for place in places_result:
-            professionals.append(f"{place['name']} - {place.get('vicinity', '')}")
-            folium.Marker(
-                location=[place["geometry"]["location"]["lat"], place["geometry"]["location"]["lng"]],
-                popup=place["name"]
-            ).add_to(map_)
-        return professionals, map_._repr_html_()
-    return ["No professionals found nearby."], ""
-
-# Main Function
+    """Search nearby healthcare professionals and display a map."""
+    try:
+        if not location or not query:
+            return ["Please provide a valid location and query."], ""
+
+        geo_location = gmaps.geocode(location)
+        if geo_location:
+            lat, lng = geo_location[0]["geometry"]["location"].values()
+
+            places_result = gmaps.places_nearby(location=(lat, lng), radius=10000, keyword=query)["results"]
+
+            professionals = []
+            map_ = folium.Map(location=(lat, lng), zoom_start=13)
+            for place in places_result:
+                professionals.append(f"{place['name']} - {place.get('vicinity', 'No address available')}")
+                folium.Marker(
+                    location=[place["geometry"]["location"]["lat"], place["geometry"]["location"]["lng"]],
+                    popup=f"{place['name']}"
+                ).add_to(map_)
+            return professionals, map_._repr_html_()
+
+        return ["No professionals found for the given location."], ""
+    except googlemaps.exceptions.HTTPError as e:
+        return [f"Google Maps API Error: {str(e)}"], ""
+    except Exception as e:
+        return [f"An error occurred: {str(e)}"], ""
+
+# Main App Logic
 def app_function(user_input, location, query, history):
-    chatbot_history, _ = chatbot(user_input, history)
+    chatbot_history, response = chatbot(user_input, history)
     sentiment = analyze_sentiment(user_input)
     emotion = detect_emotion(user_input)
     suggestions = generate_suggestions(emotion)
     professionals, map_html = get_health_professionals_and_map(location, query)
     return chatbot_history, sentiment, emotion, suggestions, professionals, map_html
 
-# CSS for Orange Themed Submit Button
+# Custom CSS for Dark Theme and Gradient Buttons
 custom_css = """
-button { background: linear-gradient(45deg, #ff5722, #ff9800); color: white; }
-.gr-dataframe, .gr-html, .gr-chatbot { background: black; color: white; border: 1px solid #ff5722; }
+body {
+    background: linear-gradient(135deg, #000, #ff5722);
+    font-family: 'Roboto', sans-serif;
+    color: white;
+}
+button {
+    background: linear-gradient(45deg, #ff5722, #ff9800) !important;
+    border: 0;
+    border-radius: 8px;
+    padding: 12px 20px;
+    cursor: pointer;
+    color: white;
+    box-shadow: 0 4px 6px rgba(0, 0, 0, 0.3);
+}
+button:hover {
+    background: linear-gradient(45deg, #ff9800, #ff5722) !important;
+}
+textarea, input {
+    background: black !important;
+    color: white !important;
+    padding: 12px;
+    border: 1px solid #ff5722 !important;
+    border-radius: 8px;
+}
+.gr-dataframe {
+    background-color: black !important;
+    color: white !important;
+    overflow-y: scroll;
+    height: 300px;
+}
 """
 
-# Gradio Application
+# Gradio Interface
 with gr.Blocks(css=custom_css) as app:
-    gr.Markdown("### 🌟 Well-Being Companion")
-    user_input = gr.Textbox(label="Enter Your Message")
-    location_input = gr.Textbox(label="Your Location")
-    query_input = gr.Textbox(label="Search Query (e.g., therapist)")
-    chatbot_history = gr.Chatbot(label="Chatbot History")
-    sentiment_box = gr.Textbox(label="Sentiment Detected")
-    emotion_box = gr.Textbox(label="Emotion Detected")
-    suggestions_table = gr.DataFrame(headers=["Title", "Link"], label="Suggestion Based On Emotion")
-    map_output_box = gr.HTML(label="Interactive Map of Professionals")
-    professional_list_box = gr.Textbox(label="Professionals Nearby", lines=5)
-    submit_button = gr.Button("Submit")
+    gr.HTML("<h1 style='text-align: center;'>🌟 Well-Being Companion</h1>")
+    gr.HTML("<h3 style='text-align: center;'>Empowering Your Mental Health Journey 💚</h3>")
+
+    with gr.Row():
+        user_message = gr.Textbox(label="Your Message", placeholder="Type your message...")
+        location = gr.Textbox(label="Your Location", placeholder="Enter your location...")
+        query = gr.Textbox(label="Search Query", placeholder="e.g., therapist, doctor")
+
+    chatbot_history = gr.Chatbot(label="Chat History")
+    sentiment_output = gr.Textbox(label="Detected Sentiment")
+    emotion_output = gr.Textbox(label="Detected Emotion")
+    suggestions_output = gr.DataFrame(headers=["Title", "Link"], label="Suggestions")
+    map_html_output = gr.HTML(label="Map of Nearby Health Professionals")
+    professionals_output = gr.Textbox(label="Nearby Professionals", lines=5)
 
+    submit_button = gr.Button("Submit")
     submit_button.click(
-        app_function, 
-        inputs=[user_input, location_input, query_input, chatbot_history], 
-        outputs=[chatbot_history, sentiment_box, emotion_box, suggestions_table, professional_list_box, map_output_box]
+        app_function,
+        inputs=[user_message, location, query, chatbot_history],
+        outputs=[chatbot_history, sentiment_output, emotion_output, suggestions_output, professionals_output, map_html_output]
     )
+
 app.launch()