Update app.py

app.py

@@ -30,7 +30,7 @@ with open("intents.json") as file:
 with open("data.pickle", "rb") as f:
     words, labels, training, output = pickle.load(f)
 
-# Build the chatbot's neural network model
+# Build Chatbot Model
 net = tflearn.input_data(shape=[None, len(training[0])])
 net = tflearn.fully_connected(net, 8)
 net = tflearn.fully_connected(net, 8)
@@ -39,18 +39,18 @@ net = tflearn.regression(net)
 chatbot_model = tflearn.DNN(net)
 chatbot_model.load("MentalHealthChatBotmodel.tflearn")
 
-# Model for sentiment detection
+# Sentiment Analysis with Hugging Face
 tokenizer_sentiment = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
 model_sentiment = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
 
-# Model for emotion detection
+# Emotion Detection
 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 Client
 gmaps = googlemaps.Client(key=os.getenv('GOOGLE_API_KEY'))
 
-# Chatbot logic
+# Chatbot Logic
 def bag_of_words(s, words):
     bag = [0] * len(words)
     s_words = word_tokenize(s)
@@ -77,7 +77,7 @@ def chatbot(message, history):
     history.append((message, response))
     return history, response
 
-# Sentiment analysis
+# Sentiment Analysis
 def analyze_sentiment(user_input):
     inputs = tokenizer_sentiment(user_input, return_tensors="pt")
     with torch.no_grad():
@@ -86,14 +86,14 @@ def analyze_sentiment(user_input):
     sentiment_map = ["Negative 😔", "Neutral 😐", "Positive 😊"]
     return sentiment_map[sentiment_class]
 
-# Emotion detection
+# Emotion Detection
 def detect_emotion(user_input):
     pipe = pipeline("text-classification", model=model_emotion, tokenizer=tokenizer_emotion)
     result = pipe(user_input)
     emotion = result[0]["label"]
     return emotion
 
-# Generate suggestions based on detected emotion
+# Generate Suggestions
 def generate_suggestions(emotion):
     suggestions = {
         "joy": [
@@ -111,7 +111,7 @@ def generate_suggestions(emotion):
     }
     return suggestions.get(emotion, [["No suggestions available", ""]])
 
-# Search professionals and generate map
+# Get Nearby Professionals and Generate Map
 def get_health_professionals_and_map(location, query):
     try:
         geo_location = gmaps.geocode(location)
@@ -130,16 +130,16 @@ def get_health_professionals_and_map(location, query):
     except Exception as e:
         return [f"Error: {e}"], ""
 
-# Main app function
+# App Main Function
 def app_function(message, location, query, history):
-    chatbot_history, _ = chatbot(message, history)  # Generate chatbot response
-    sentiment = analyze_sentiment(message)          # Detect sentiment
-    emotion = detect_emotion(message.lower())       # Detect emotion
-    suggestions = generate_suggestions(emotion)     # Generate suggestions
-    professionals, map_html = get_health_professionals_and_map(location, query)  # Find professionals & map
+    chatbot_history, _ = chatbot(message, history)
+    sentiment = analyze_sentiment(message)
+    emotion = detect_emotion(message.lower())
+    suggestions = generate_suggestions(emotion)
+    professionals, map_html = get_health_professionals_and_map(location, query)
     return chatbot_history, sentiment, emotion, suggestions, professionals, map_html
 
-# Enhanced CSS for Black-Themed Interface
+# Enhanced CSS for Black-Themed Table and UI
 custom_css = """
 @import url('https://fonts.googleapis.com/css2?family=Roboto:wght@400;700&display=swap');
 body {
@@ -181,11 +181,14 @@ textarea, input[type="text"], .gr-chatbot {
 }
 .gr-dataframe {
     font-size: 14px;
-    height: 350px; /* Make the suggestions box larger */
-    overflow-y: scroll; /* Add scroll if the table doesn't fit */
+    height: 400px; /* Larger table */
+    overflow-y: scroll; /* Scroll if content exceeds table height */
+    background: #000000 !important;
+    color: white !important;
+    border: 2px solid #ff5722;
 }
 h1 {
-    font-size: 3.5rem;  /* Bigger and bold heading */
+    font-size: 3.5rem;
     font-weight: bold;
     margin-bottom: 10px;
     color: white;
@@ -207,13 +210,13 @@ with gr.Blocks(css=custom_css) as app:
     with gr.Row():
         user_message = gr.Textbox(label="Your Message", placeholder="Enter your message...")
         user_location = gr.Textbox(label="Your Location", placeholder="Enter your location...")
-        search_query = gr.Textbox(label="Search for...", placeholder="Search for professionals like therapists...")
+        search_query = gr.Textbox(label="Query", placeholder="Search for professionals...")
         submit_btn = gr.Button("Submit")
 
-    chatbot_box = gr.Chatbot(label="Chat History")  # Black chatbot background
+    chatbot_box = gr.Chatbot(label="Chat History")
     emotion_output = gr.Textbox(label="Detected Emotion")
     sentiment_output = gr.Textbox(label="Detected Sentiment")
-    suggestions_output = gr.DataFrame(headers=["Title", "Links"], label="Suggestions")  # Larger data frame
+    suggestions_output = gr.DataFrame(headers=["Title", "Links"], label="Suggestions")  # Enlarged table
     map_output = gr.HTML(label="Nearby Professionals Map")
     professional_display = gr.Textbox(label="Nearby Professionals", lines=5)