Update app.py

app.py

@@ -48,7 +48,6 @@ gmaps = googlemaps.Client(key=os.getenv("GOOGLE_API_KEY"))
 
 # 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()]
@@ -59,7 +58,6 @@ def bag_of_words(s, words):
     return np.array(bag)
 
 def generate_chatbot_response(message, history):
-    """Generate chatbot response and maintain conversation history."""
     history = history or []
     try:
         result = chatbot_model.predict([bag_of_words(message, words)])
@@ -75,7 +73,6 @@ def generate_chatbot_response(message, history):
     return history, response
 
 def analyze_sentiment(user_input):
-    """Analyze sentiment and map to emojis."""
     inputs = tokenizer_sentiment(user_input, return_tensors="pt")
     with torch.no_grad():
         outputs = model_sentiment(**inputs)
@@ -84,7 +81,6 @@ def analyze_sentiment(user_input):
     return f"Sentiment: {sentiment_map[sentiment_class]}"
 
 def detect_emotion(user_input):
-    """Detect emotions based on input."""
     pipe = pipeline("text-classification", model=model_emotion, tokenizer=tokenizer_emotion)
     result = pipe(user_input)
     emotion = result[0]["label"].lower().strip()
@@ -96,10 +92,9 @@ def detect_emotion(user_input):
         "surprise": "Surprise 😲",
         "neutral": "Neutral 😐",
     }
-    return emotion_map.get(emotion, "Unknown 🤔"), emotion  # Text with matching key
+    return emotion_map.get(emotion, "Unknown 🤔"), emotion
 
 def generate_suggestions(emotion):
-    """Return relevant suggestions based on detected emotions."""
     emotion_key = emotion.lower()
     suggestions = {
         "joy": [
@@ -129,7 +124,6 @@ def generate_suggestions(emotion):
     return suggestions.get(emotion_key, [["No specific suggestions available.", ""]])
 
 def get_health_professionals_and_map(location, query):
-    """Search nearby healthcare professionals using Google Maps API."""
     try:
         if not location or not query:
             return ["Please provide both location and query."], ""
@@ -154,11 +148,11 @@ def get_health_professionals_and_map(location, query):
 
 # Main Application Logic
 def app_function(user_input, location, query, history):
-    chatbot_history, _ = generate_chatbot_response(user_input, history)  # Generate chatbot response
-    sentiment_result = analyze_sentiment(user_input)  # Sentiment detection
-    emotion_result, cleaned_emotion = detect_emotion(user_input)  # Emotion detection
-    suggestions = generate_suggestions(cleaned_emotion)  # Fetch suggestions for emotion
-    professionals, map_html = get_health_professionals_and_map(location, query)  # Nearby professionals with map
+    chatbot_history, _ = generate_chatbot_response(user_input, history)
+    sentiment_result = analyze_sentiment(user_input)
+    emotion_result, cleaned_emotion = detect_emotion(user_input)
+    suggestions = generate_suggestions(cleaned_emotion)
+    professionals, map_html = get_health_professionals_and_map(location, query)
     return chatbot_history, sentiment_result, emotion_result, suggestions, professionals, map_html
 
 # Gradio Interface
@@ -186,6 +180,14 @@ textarea, input {
     font-size: 1rem;
     padding: 10px;
     border-radius: 8px;
+    caret-color: white; /* Ensures white cursor when typing */
+}
+
+/* Style for focused state to ensure black background and white font */
+textarea:focus, input:focus {
+    background: black; /* Black background when focused or typing */
+    color: white; /* White text */
+    border: 2px solid orange; /* Reinforce border for focus */
 }
 
 button {
@@ -202,12 +204,6 @@ button {
 button:hover {
     box-shadow: 0px 4px 8px rgba(255, 165, 0, 0.5);
 }
-
-.chat-container .human {
-    background: linear-gradient(to right, #ff7f50, orange);
-    color: white;
-    padding: 8px;
-}
 """
 
 with gr.Blocks(css=custom_css) as app: