Update app.py

app.py

@@ -230,12 +230,43 @@ def app_function_chatbot(user_input, location, query, history):
     return chatbot_history, sentiment_result, emotion_result, suggestions, professionals, map_html
 
 # Disease Prediction Logic
+# def predict_disease(symptoms):
+#     """Predict disease based on input symptoms."""
+#     valid_symptoms = [s for s in symptoms if s is not None]
+#     if len(valid_symptoms) < 3:
+#         return "Please select at least 3 symptoms for a better prediction."
+#     input_test = np.zeros(len(X_train.columns))  # Create an array for feature input
+#     for symptom in symptoms:
+#         if symptom in X_train.columns:
+#             input_test[X_train.columns.get_loc(symptom)] = 1
+#     predictions = {}
+#     for model_name, info in trained_models.items():
+#         prediction = info['model'].predict([input_test])[0]
+#         predicted_disease = label_encoder_train.inverse_transform([prediction])[0]
+#         predictions[model_name] = predicted_disease
+
+#     # Create a Markdown table for displaying predictions
+#     markdown_output = ["### Predicted Diseases"]
+#     markdown_output.append("| Model | Predicted Disease |")
+#     markdown_output.append("|-------|------------------|")  # Table headers
+#     for model_name, disease in predictions.items():
+#         markdown_output.append(f"| {model_name} | {disease} |")
+    
+#     return "\n".join(markdown_output)
 def predict_disease(symptoms):
     """Predict disease based on input symptoms."""
+    # Filter out None values
+    valid_symptoms = [s for s in symptoms if s is not None]
+    
+    # Ensure at least 3 symptoms are selected
+    if len(valid_symptoms) < 3:
+        return "Please select at least 3 symptoms for a better prediction."
+    
     input_test = np.zeros(len(X_train.columns))  # Create an array for feature input
-    for symptom in symptoms:
+    for symptom in valid_symptoms:
         if symptom in X_train.columns:
             input_test[X_train.columns.get_loc(symptom)] = 1
+    
     predictions = {}
     for model_name, info in trained_models.items():
         prediction = info['model'].predict([input_test])[0]
@@ -251,11 +282,11 @@ def predict_disease(symptoms):
     
     return "\n".join(markdown_output)
 
+
 from gradio.components import HTML
 
 # Custom CSS for styling
-custom_css = """
-/* Importing Google Fonts */
+/* Custom CSS for styling */
 @import url('https://fonts.googleapis.com/css2?family=Roboto:wght@400;700&display=swap');
 
 /* General Body Styling */
@@ -272,7 +303,7 @@ h1, h2, h3, h4 {
 
 h1 {
     font-size: 2.5rem; /* Bigger header size */
-    background: linear-gradient(135deg, #3c6487, #355f7a); /* Gradient using your color */
+    background: linear-gradient(135deg, #3c6487, #355f7a);
     color: #ffffff;
     border-radius: 12px;
     padding: 15px;
@@ -281,23 +312,29 @@ h1 {
     margin-bottom: 20px; /* Spacing below the header */
 }
 
-/* Tab Button Styling */
-button[role="tab"] {
-    background-color: transparent; /* Make background transparent */
-    color: #3c6487; /* Text color matching the theme */
-    border: none; /* Remove default button border */
-    padding: 10px 15px; /* Padding for buttons */
-    font-size: 1.2rem; /* Font size for tab buttons */
+/* Button Styling */
+.gr-button {
+    background-color: #3c6487; /* Button background */
+    color: white;
+    border-radius: 8px;
+    padding: 10px 15px; /* Adjusted padding */
+    font-size: 16px; /* Font size for buttons */
+    border: none; /* No border */
     cursor: pointer; /* Pointer on hover */
-    text-decoration: underline;
+    box-shadow: 0 4px 6px rgba(0, 0, 0, 0.2); /* Shadow on button */
+    display: inline-block; /* Inline-block to wrap text */
+    position: relative; /* For pseudo-element positioning */
+    text-decoration: none; /* Remove default underline */
 }
 
-button[role="tab"]:hover {
-    color: #ae1c93; /* Change color on hover */
+/* Button hover states */
+.gr-button:hover {
+    background: linear-gradient(to right, #a0c4e1, #3c6487); /* Light blue gradient on hover */
+    transition: background 0.3s; /* Ease the background change */
 }
 
-/* Add a custom underline effect */
-button[role="tab"].selected::after {
+/* Add a blue underline effect */
+.gr-button::after {
     content: ""; /* Empty content for underline */
     display: block;
     width: 100%; /* Full width */
@@ -306,6 +343,12 @@ button[role="tab"].selected::after {
     position: absolute;
     bottom: -5px; /* Position it below the text */
     left: 0;
+    transform: scaleX(0); /* Initially scale to 0 (invisible) */
+    transition: transform 0.3s; /* Smooth transition for the underline */
+}
+
+.gr-button:hover::after {
+    transform: scaleX(1); /* Scale to full width on hover */
 }
 
 /* Input and Textarea Styling */
@@ -324,26 +367,6 @@ textarea:focus, input:focus {
     box-shadow: 0 0 5px rgba(174, 28, 147, 0.5); /* Shadow on focus */
 }
 
-/* Button Styling */
-.gr-button {
-    background-color: #3c6487; /* Button background */
-    color: white;
-    border-radius: 8px;
-    padding: 10px 20px; /* Adjusted padding */
-    font-size: 16px; /* Larger font size for buttons */
-    border: none; /* No border */
-    cursor: pointer; /* Pointer on hover */
-    box-shadow: 0 4px 6px rgba(0, 0, 0, 0.2); /* Shadow on button */
-}
-
-.gr-button:hover {
-    background-color: #ae1c93; /* Change button color on hover */
-}
-
-.gr-button:active {
-    background-color: #8f167b; /* Even darker shade on active */
-}
-
 /* DataFrame Container Styling */
 .df-container {
     background: white; /* Background for data frames */
@@ -383,7 +406,6 @@ textarea:focus, input:focus {
         margin-bottom: 10px; /* Spacing between inputs */
     }
 }
-"""
 
 # Gradio Application Interface
 with gr.Blocks(css=custom_css) as app:
@@ -412,15 +434,15 @@ with gr.Blocks(css=custom_css) as app:
         )
 
     with gr.Tab("Disease Prediction"):
-        symptom1 = gr.Dropdown(X_train.columns.tolist(), label="Select Symptom 1")
-        symptom2 = gr.Dropdown(X_train.columns.tolist(), label="Select Symptom 2")
-        symptom3 = gr.Dropdown(X_train.columns.tolist(), label="Select Symptom 3")
-        symptom4 = gr.Dropdown(X_train.columns.tolist(), label="Select Symptom 4")
-        symptom5 = gr.Dropdown(X_train.columns.tolist(), label="Select Symptom 5")
-        
+        symptom1 = gr.Dropdown(choices=[None] + X_train.columns.tolist(), label="Select Symptom 1", value=None)
+        symptom2 = gr.Dropdown(choices=[None] + X_train.columns.tolist(), label="Select Symptom 2", value=None)
+        symptom3 = gr.Dropdown(choices=[None] + X_train.columns.tolist(), label="Select Symptom 3", value=None)
+        symptom4 = gr.Dropdown(choices=[None] + X_train.columns.tolist(), label="Select Symptom 4", value=None)
+        symptom5 = gr.Dropdown(choices=[None] + X_train.columns.tolist(), label="Select Symptom 5", value=None)
+    
         submit_disease = gr.Button(value="Predict Disease", variant="primary", icon="fa-stethoscope")
-        
-        disease_prediction_result = gr.Markdown(label="Predicted Diseases")  # Use Markdown for predictions
+    
+        disease_prediction_result = gr.Markdown(label="Predicted Diseases")
 
         submit_disease.click(
             lambda symptom1, symptom2, symptom3, symptom4, symptom5: predict_disease(
@@ -429,5 +451,6 @@ with gr.Blocks(css=custom_css) as app:
             outputs=disease_prediction_result
         )
 
+
 # Launch the Gradio application
 app.launch()