Update app.py

app.py

@@ -13,17 +13,15 @@ import googlemaps
 import folium
 import torch
 
-# Disable GPU usage for TensorFlow and suppress logs
+# Disable GPU usage for TensorFlow and logs
 os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
 os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
-# Ensure necessary NLTK resources are downloaded
 nltk.download("punkt")
 
-# Initialize Lancaster Stemmer
+# Initialize Stemmer
 stemmer = LancasterStemmer()
 
-# Load intents.json and chatbot training data
+# Load Chatbot Training Data
 with open("intents.json") as file:
     intents_data = json.load(file)
 
@@ -39,19 +37,18 @@ net = tflearn.regression(net)
 chatbot_model = tflearn.DNN(net)
 chatbot_model.load("MentalHealthChatBotmodel.tflearn")
 
-# Hugging Face sentiment and emotion detection models
+# Sentiment and Emotion Detection 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 client
+# Google Maps API for Nearby Professionals
 gmaps = googlemaps.Client(key=os.getenv('GOOGLE_API_KEY'))
 
-# Helper Functions
+# Chatbot Helper
 def bag_of_words(s, words):
-    """Convert user input into bag-of-words vector for use in chatbot model."""
     bag = [0] * len(words)
     s_words = word_tokenize(s)
     s_words = [stemmer.stem(word.lower()) for word in s_words if word.isalnum()]
@@ -62,7 +59,7 @@ def bag_of_words(s, words):
     return np.array(bag)
 
 def chatbot(message, history):
-    """Generate chatbot response and append to history."""
+    """Generate a chatbot response and update history."""
     history = history or []
     try:
         result = chatbot_model.predict([bag_of_words(message, words)])
@@ -78,7 +75,7 @@ def chatbot(message, history):
     return history, response
 
 def analyze_sentiment(user_input):
-    """Analyze sentiment with emojis."""
+    """Detect sentiment and return sentiment emoji."""
     inputs = tokenizer_sentiment(user_input, return_tensors="pt")
     with torch.no_grad():
         outputs = model_sentiment(**inputs)
@@ -87,10 +84,10 @@ def analyze_sentiment(user_input):
     return sentiment_map[sentiment_class]
 
 def detect_emotion(user_input):
-    """Detect user emotion with emojis."""
+    """Detect user emotion based on input."""
     pipe = pipeline("text-classification", model=model_emotion, tokenizer=tokenizer_emotion)
     result = pipe(user_input)
-    emotion = result[0]['label']
+    emotion = result[0]['label'].lower()
     emotion_map = {
         "joy": "😊 Joy",
         "anger": "😠 Anger",
@@ -99,64 +96,51 @@ def detect_emotion(user_input):
         "surprise": "😲 Surprise",
         "neutral": "😐 Neutral"
     }
-    return emotion_map.get(emotion, "Unknown Emotion 🤔")
+    return emotion_map.get(emotion, "Unknown 🤔")
 
 def generate_suggestions(emotion):
-    """Return suggestions based on detected emotion."""
+    """Provide clickable suggestions for each emotion."""
     suggestions = {
         "joy": [
             ["Relaxation Techniques", '<a href="https://www.helpguide.org/mental-health/meditation/mindful-breathing-meditation" target="_blank">Visit</a>'],
-            ["Dealing with Stress", '<a href="https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-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>']
+            ["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>'],
         ],
         "anger": [
-            ["Emotional Wellness Toolkit", '<a href="https://www.nih.gov/health-information/emotional-wellness-toolkit" target="_blank">Visit</a>'],
-            ["Stress Management Tips", '<a href="https://www.health.harvard.edu/health-a-to-z" target="_blank">Visit</a>'],
-            ["Dealing with Anger", '<a href="https://www.helpguide.org/mental-health/anger-management.htm" target="_blank">Visit</a>'],
-            ["Relaxation Video", '<a href="https://youtu.be/MIc299Flibs" target="_blank">Watch</a>']
+            ["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>']
         ],
         "fear": [
-            ["Mindfulness Practices", '<a href="https://www.helpguide.org/mental-health/meditation/mindful-breathing-meditation" target="_blank">Visit</a>'],
-            ["Coping with Anxiety", '<a href="https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-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/yGKKz185M5o" target="_blank">Watch</a>']
+            ["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>']
         ],
         "sadness": [
-            ["Emotional Wellness Toolkit", '<a href="https://www.nih.gov/health-information/emotional-wellness-toolkit" target="_blank">Visit</a>'],
-            ["Dealing with Anxiety", '<a href="https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety" target="_blank">Visit</a>'],
-            ["Relaxation Video", '<a href="https://youtu.be/-e-4Kx5px_I" target="_blank">Watch</a>']
+            ["Overcoming Sadness", '<a href="https://youtu.be/-e-4Kx5px_I" target="_blank">Watch</a>']
         ],
         "surprise": [
-            ["Managing Stress", '<a href="https://www.health.harvard.edu/health-a-to-z" target="_blank">Visit</a>'],
-            ["Coping Strategies", '<a href="https://www.helpguide.org/mental-health/anxiety/tips-for-dealing-with-anxiety" target="_blank">Visit</a>'],
-            ["Relaxation Video", '<a href="https://youtu.be/m1vaUGtyo-A" target="_blank">Watch</a>']
-        ],
+            ["Stress Tips", '<a href="https://youtu.be/m1vaUGtyo-A" target="_blank">Watch</a>']
+        ]
     }
     return suggestions.get(emotion.lower(), [["No suggestions available", ""]])
 
 def get_health_professionals_and_map(location, query):
-    """Search for nearby professionals and generate interactive map."""
-    try:
-        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)
-            places_result = gmaps.places_nearby(location=(lat, lng), radius=10000, keyword=query)["results"]
-
-            professionals = []
-            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."], ""
-    except Exception as e:
-        return [f"Error: {str(e)}"], ""
-
-# Application Logic
+    """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
 def app_function(user_input, location, query, history):
     chatbot_history, _ = chatbot(user_input, history)
     sentiment = analyze_sentiment(user_input)
@@ -165,64 +149,29 @@ def app_function(user_input, location, query, history):
     professionals, map_html = get_health_professionals_and_map(location, query)
     return chatbot_history, sentiment, emotion, suggestions, professionals, map_html
 
-# Custom CSS for Styling Submit Button and UI
+# CSS for Orange Themed Submit Button
 custom_css = """
-body { 
-    background: linear-gradient(135deg, #000000, #ff5722); 
-    color: white; 
-    font-family: 'Roboto', sans-serif; 
-}
-button { 
-    background: linear-gradient(45deg, #ff5722, #ff9800) !important; 
-    color: white !important; 
-    border: none; 
-    border-radius: 8px; 
-    padding: 12px 20px; 
-    cursor: pointer; 
-    font-size: 16px; 
-    box-shadow: 0 4px 6px rgba(0, 0, 0, 0.3); 
-}
-button:hover { 
-    background: linear-gradient(45deg, #ff9800, #ff5722) !important; 
-}
-textarea, input { 
-    background: #000000 !important; 
-    color: white !important; 
-    border: 1px solid #ff5722 !important; 
-    padding: 12px; 
-    font-size: 14px; 
-    border-radius: 8px; 
-}
-.gr-chatbot, .gr-textbox, .gr-html, .gr-dataframe { 
-    background-color: #000 !important; 
-    border: 1px solid #ff5722 !important; 
-    color: white !important; 
-}
+button { background: linear-gradient(45deg, #ff5722, #ff9800); color: white; }
+.gr-dataframe, .gr-html, .gr-chatbot { background: black; color: white; border: 1px solid #ff5722; }
 """
 
 # Gradio Application
 with gr.Blocks(css=custom_css) as app:
-    gr.Markdown("<h1 style='text-align: center;'>🌟 Well-Being Companion</h1>")
-    gr.Markdown("<h3 style='text-align: center;'>Empowering Your Well-Being 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_output = gr.Chatbot(label="Chat History")
-    sentiment_output = gr.Textbox(label="Detected Sentiment")
-    emotion_output = gr.Textbox(label="Detected Emotion")
-    suggestion_table = gr.DataFrame(headers=["Suggestion Title", "Link"], label="Well-Being Suggestions")
-    professionals_output = gr.Textbox(label="Health Professionals Nearby", lines=5)
-    map_html = gr.HTML(label="Interactive Map")
-
-    submit_btn = gr.Button("Submit")
-
-    submit_btn.click(
-        app_function,
-        inputs=[user_message, location, query, chatbot_output],
-        outputs=[chatbot_output, sentiment_output, emotion_output, suggestion_table, professionals_output, map_html]
+    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")
+
+    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.launch()