Update app.py

app.py

@@ -1,4 +1,4 @@
-import streamlit as st
+import gradio as gr
 import numpy as np
 from PIL import Image
 from keras.models import load_model
@@ -47,26 +47,7 @@ def predict_ripeness(image):
     predicted_label = class_names_ripeness[predicted_class]
     return predicted_label
 
-def main():
-    st.title("Banana Analysis App")
-    st.write("Choose an option to analyze bananas")
+inputs = gr.inputs.File(label="Upload an image...")
+outputs = gr.outputs.Textbox(label="Prediction")
 
-    # Options for banana analysis
-    analysis_option = st.radio("Choose an option", ["Banana Disease Detection", "Banana Ripeness Detection"])
-
-    # File uploader
-    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
-
-    if uploaded_file is not None:
-        # Display the uploaded image
-        st.image(uploaded_file, caption='Uploaded Image', use_column_width=True)
-        if st.button("Analyze"):
-            if analysis_option == "Banana Disease Detection":
-                predicted_label = predict_disease(uploaded_file)
-                st.success(f"Predicted disease: {predicted_label}")
-            elif analysis_option == "Banana Ripeness Detection":
-                predicted_label = predict_ripeness(uploaded_file)
-                st.success(f"Predicted ripeness: {predicted_label}")
-
-if __name__ == '__main__':
-    main()
+gr.Interface(fn=predict_disease, inputs=inputs, outputs=outputs, title="Banana Disease Detection").launch()