Update app.py

app.py

@@ -3,6 +3,7 @@ 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')
@@ -28,20 +29,25 @@ 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 = model.predict(np.expand_dims(user_X, axis=2))
-    predicted_disease = disease_list[np.argmax(prediction)]
+    prediction_array = model.predict(np.expand_dims(user_X, axis=2))[0]
+    predicted_disease = disease_list[np.argmax(prediction_array)]
     predicted_antibiotics = data.loc[data['Disease'] == predicted_disease, 'Antibiotics'].values[0]
 
-    # Return the prediction and accuracy
-    return predicted_disease, predicted_antibiotics, accuracy
+    # 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)
 
 # Define the Gradio interface
 inputs = gr.inputs.Textbox(label="Symptoms")
-disease_output = gr.outputs.Label(label="Disease")
-antibiotics_output = gr.outputs.Label(label="Antibiotics")
-accuracy_output = gr.outputs.Slider(minimum=0, maximum=100, label="Model accuracy on testing set")
-gradio_interface = gr.Interface(fn=predict_disease, inputs=inputs, outputs=[disease_output, antibiotics_output, accuracy_output], title="Disease Prediction App")
+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])
 
-# Launch the Gradio interface¿
+# Launch the Gradio interface
 gradio_interface.launch()