Update app.py

app.py

@@ -73,14 +73,10 @@ disease_dict = {
 
 # Function to prepare data
 def prepare_data(df):
-    # Split the dataset into features and target
-    X = df.iloc[:, :-1]  # All columns except the last one (features)
-    y = df.iloc[:, -1]   # The last column (target)
-
-    # Encode the target variable
+    X = df.iloc[:, :-1]  # Features
+    y = df.iloc[:, -1]   # Target
     label_encoder = LabelEncoder()
     y_encoded = label_encoder.fit_transform(y)
-    
     return X, y_encoded, label_encoder
 
 # Preparing training and testing data
@@ -244,7 +240,15 @@ def predict_disease(symptoms):
         prediction = info['model'].predict([input_test])[0]
         predicted_disease = label_encoder_train.inverse_transform([prediction])[0]
         predictions[model_name] = predicted_disease
-    return predictions
+
+    # 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)
 
 # Gradio Application Interface
 with gr.Blocks() as app:
@@ -280,7 +284,7 @@ with gr.Blocks() as app:
         symptom5 = gr.Dropdown(X_train.columns.tolist(), label="Select Symptom 5")
         
         submit_disease = gr.Button(value="Predict Disease", variant="primary")
-        disease_prediction_result = gr.Textbox(label="Predicted Diseases")
+        disease_prediction_result = gr.Markdown(label="Predicted Diseases")  # Use Markdown for predictions
 
         submit_disease.click(
             lambda symptom1, symptom2, symptom3, symptom4, symptom5: predict_disease(