Update app.py

app.py

@@ -3,7 +3,6 @@ import pandas as pd
 import numpy as np
 from sklearn.preprocessing import MultiLabelBinarizer
 from keras.models import load_model
-import time
 
 # Load the trained model
 model = load_model('model.h5')
@@ -29,25 +28,18 @@ def predict_disease(symptoms):
     user_symptoms = [symptoms.split(',')]
     user_X = mlb.transform(user_symptoms)
 
-    # Show loading animation
-    prediction.update("Loading...")
-    time.sleep(1)
-
     # Make the prediction
-    prediction_array = model.predict(np.expand_dims(user_X, axis=2))[0]
-    predicted_disease = disease_list[np.argmax(prediction_array)]
+    prediction = model.predict(np.expand_dims(user_X, axis=2))
+    predicted_disease = disease_list[np.argmax(prediction)]
     predicted_antibiotics = data.loc[data['Disease'] == predicted_disease, 'Antibiotics'].values[0]
 
-    # Display prediction and accuracy
-    accuracy_percent = f"{accuracy*100:.2f}%"
-    result = f"Disease: {predicted_disease}\nAntibiotics: {predicted_antibiotics}\n\nModel accuracy on testing set: {accuracy_percent}"
-    prediction.update(result)
+    # Return the prediction and accuracy
+    return f"Disease: {predicted_disease}\nAntibiotics: {predicted_antibiotics}", f"Model accuracy on testing set: {accuracy:.3f}"
 
 # Define the Gradio interface
 inputs = gr.inputs.Textbox(label="Symptoms")
-outputs = gr.outputs.Textbox(label="Prediction")
-prediction = gr.outputs.Textbox("Loading...")
-gradio_interface = gr.Interface(predict_disease, inputs, outputs, title="Disease Prediction App", live=True, outputs=[outputs, prediction])
+outputs = [gr.outputs.Textbox(label="Prediction"), gr.outputs.Textbox(label="Accuracy")]
+gradio_interface = gr.Interface(predict_disease, inputs, outputs, title="Disease Prediction App")
 
 # Launch the Gradio interface
 gradio_interface.launch()