Create app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing.image import load_img, img_to_array
+import numpy as np
+from PIL import Image
+
+model = load_model('/content/drive/MyDrive/Colab Notebooks/model_extended.h5')
+
+def predict_image(image):
+    img_array = img_to_array(image)
+    img_array = img_array.reshape((1, 256, 256, 3))
+    img_array = img_array / 255.0
+    predictions = model.predict(img_array)
+    predicted_class_index = predictions.argmax()
+    class_labels = ['bacterial_leaf_blight', 'bacterial_leaf_streak', 'bacterial_panicle_blight','blast','brown_spot','dead_heart','downy_mildew','hispa','normal','tungro' ]  # Replace with your actual class labels
+    predicted_class_label = class_labels[predicted_class_index]
+    return predicted_class_label
+
+my_app = gr.Blocks()
+with my_app:
+  gr.Markdown("<center><h1>Paddy Pest Disease Classification Application UI with Gradio</h1></center>")
+  with gr.Row():
+    with gr.Column():
+        img_source = gr.Image(label="Please select source Image", shape=(256, 256))
+        source_image_loader = gr.Button("Load Image")
+    with gr.Column():
+        output = gr.Textbox(label="Image Info")
+  source_image_loader.click(predict_image,img_source,output)
+
+my_app.launch(debug=True)