Update app.py

app.py

@@ -13,22 +13,22 @@ import googlemaps
 import folium
 import torch
 
-# Suppress TensorFlow GPU usage and warnings
-os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+# Suppress TensorFlow's GPU usage and warnings
+os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
 
-# Download NLTK resources
+# Download necessary NLTK resources
 nltk.download("punkt")
 stemmer = LancasterStemmer()
 
-# Load chatbot training data
+# Load intents and chatbot training data
 with open("intents.json") as file:
     intents_data = json.load(file)
 
 with open("data.pickle", "rb") as f:
     words, labels, training, output = pickle.load(f)
 
-# Build Chatbot Model
+# Build the Chatbot Model
 net = tflearn.input_data(shape=[None, len(training[0])])
 net = tflearn.fully_connected(net, 8)
 net = tflearn.fully_connected(net, 8)
@@ -37,17 +37,19 @@ net = tflearn.regression(net)
 chatbot_model = tflearn.DNN(net)
 chatbot_model.load("MentalHealthChatBotmodel.tflearn")
 
-# Hugging Face sentiment and emotion 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
-gmaps = googlemaps.Client(key=os.getenv('GOOGLE_API_KEY'))
+gmaps = googlemaps.Client(key=os.getenv("GOOGLE_API_KEY"))
 
-# Bag of Words Helper Function for Chatbot
+# Helper Functions
 def bag_of_words(s, words):
+    """Convert user input to bag-of-words vector."""
     bag = [0] * len(words)
     s_words = word_tokenize(s)
     s_words = [stemmer.stem(word.lower()) for word in s_words if word.isalnum()]
@@ -57,9 +59,8 @@ def bag_of_words(s, words):
                 bag[i] = 1
     return np.array(bag)
 
-# Chatbot Function
 def chatbot(message, history):
-    """Generate a chatbot response and append to the chat history."""
+    """Generate chatbot response and append to chat history."""
     history = history or []
     try:
         result = chatbot_model.predict([bag_of_words(message, words)])
@@ -70,11 +71,10 @@ def chatbot(message, history):
                 response = random.choice(intent["responses"])
                 break
     except Exception as e:
-        response = f"Error: {str(e)}"
+        response = f"Error: {e}"
     history.append((message, response))
     return history, response
 
-# Sentiment Analysis
 def analyze_sentiment(user_input):
     """Analyze sentiment from user input."""
     inputs = tokenizer_sentiment(user_input, return_tensors="pt")
@@ -84,53 +84,50 @@ 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 with an emoji."""
+    """Detect user emotion with emoji representation."""
     pipe = pipeline("text-classification", model=model_emotion, tokenizer=tokenizer_emotion)
     result = pipe(user_input)
-    emotion = result[0]['label'].lower()
+    emotion = result[0]["label"].lower()
     emotion_map = {
         "joy": "😊 Joy",
         "anger": "😠 Anger",
         "sadness": "😢 Sadness",
         "fear": "😨 Fear",
         "surprise": "😲 Surprise",
-        "neutral": "😐 Neutral"
+        "neutral": "😐 Neutral",
     }
-    return emotion_map.get(emotion, "Unknown Emotion 🤔")
+    return emotion_map.get(emotion, "Unknown 🤔")
 
-# Generate Suggestions for Emotion
 def generate_suggestions(emotion):
-    """Return suggestions for the detected emotion."""
+    """Provide suggestions based on the detected emotion."""
     suggestions = {
         "joy": [
             ["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>']
+            ["Relaxation Video", '<a href="https://youtu.be/m1vaUGtyo-A" target="_blank">Watch</a>'],
         ],
         "anger": [
-            ["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>']
+            ["Stress Management Tips", '<a href="https://www.health.harvard.edu" target="_blank">Visit</a>'],
+            ["Relaxation Video", '<a href="https://youtu.be/MIc299Flibs" target="_blank">Watch</a>'],
         ],
         "fear": [
             ["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>']
+            ["Mindfulness Techniques", '<a href="https://youtu.be/yGKKz185M5o" target="_blank">Watch</a>'],
         ],
         "sadness": [
-            ["Stress Management", '<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/" target="_blank">Visit</a>'],
-            ["Relaxation Help", '<a href="https://youtu.be/m1vaUGtyo-A" target="_blank">Watch</a>']
-        ]
+            ["Managing Surprises", '<a href="https://www.health.harvard.edu" target="_blank">Visit</a>'],
+            ["Calm Relaxation", '<a href="https://youtu.be/m1vaUGtyo-A" target="_blank">Watch</a>'],
+        ],
     }
     return suggestions.get(emotion.lower(), [["No suggestions are available.", ""]])
 
-# Search for Nearby Professionals and Generate Map
 def get_health_professionals_and_map(location, query):
-    """Search nearby healthcare professionals and display a map."""
+    """Search for nearby healthcare professionals and generate a map."""
     try:
         if not location or not query:
             return ["Please provide a valid location and query."], ""
@@ -138,7 +135,6 @@ def get_health_professionals_and_map(location, 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 = []
@@ -152,16 +148,14 @@ def get_health_professionals_and_map(location, query):
             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, response = chatbot(user_input, history)
-    sentiment = analyze_sentiment(user_input)
-    emotion = detect_emotion(user_input)
+# Main Application Logic
+def app_function(user_message, location, query, history):
+    chatbot_history, _ = chatbot(user_message, history)
+    sentiment = analyze_sentiment(user_message)
+    emotion = detect_emotion(user_message)
     suggestions = generate_suggestions(emotion)
     professionals, map_html = get_health_professionals_and_map(location, query)
     return chatbot_history, sentiment, emotion, suggestions, professionals, map_html
@@ -169,17 +163,18 @@ def app_function(user_input, location, query, history):
 # Custom CSS for Dark Theme and Gradient Buttons
 custom_css = """
 body {
-    background: linear-gradient(135deg, #000, #ff5722);
+    background: linear-gradient(135deg, #000000, #ff5722);
     font-family: 'Roboto', sans-serif;
     color: white;
 }
 button {
     background: linear-gradient(45deg, #ff5722, #ff9800) !important;
-    border: 0;
+    border: none;
     border-radius: 8px;
     padding: 12px 20px;
     cursor: pointer;
     color: white;
+    font-size: 16px;
     box-shadow: 0 4px 6px rgba(0, 0, 0, 0.3);
 }
 button:hover {
@@ -197,31 +192,33 @@ textarea, input {
     color: white !important;
     overflow-y: scroll;
     height: 300px;
+    border: 1px solid #ff5722;
 }
 """
 
 # Gradio Interface
 with gr.Blocks(css=custom_css) as app:
-    gr.HTML("<h1 style='text-align: center;'>🌟 Well-Being Companion</h1>")
-    gr.HTML("<h3 style='text-align: center;'>Empowering Your Mental Health Journey 💚</h3>")
+    gr.Markdown("<h1 style='text-align: center;'>🌟 Well-Being Companion</h1>")
+    gr.Markdown("<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...")
+        user_message = gr.Textbox(label="Your Message", placeholder="Enter your message...")
         location = gr.Textbox(label="Your Location", placeholder="Enter your location...")
-        query = gr.Textbox(label="Search Query", placeholder="e.g., therapist, doctor")
+        query = gr.Textbox(label="Health Query", placeholder="Search for health professionals...")
 
     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)
+    suggestions_table = gr.DataFrame(headers=["Suggestion", "Link"], label="Suggestions")
+    professionals_output = gr.Textbox(label="Nearby Health Professionals", lines=5)
+    map_output = gr.HTML(label="Map")
 
     submit_button = gr.Button("Submit")
+
     submit_button.click(
         app_function,
         inputs=[user_message, location, query, chatbot_history],
-        outputs=[chatbot_history, sentiment_output, emotion_output, suggestions_output, professionals_output, map_html_output]
+        outputs=[chatbot_history, sentiment_output, emotion_output, suggestions_table, professionals_output, map_output]
     )
 
 app.launch()