Create app.py

app.py

@@ -0,0 +1,41 @@
+import gradio as gr
+import tensorflow as tf
+# import numpy as np
+
+model=tf.keras.models.load_model("final_model_v_full.keras")
+class_names =['Potato___Early_blight','Potato___healthy','Potato___Late_blight']
+
+    
+
+def predict(img):
+    img_array = img.reshape(-1,256,256,3)
+#   img_array = tf.keras.preprocessing.image.img_to_array(img)
+    #img_array = tf.expand_dims(img_array, 0)
+    
+    predictions = model.predict(img_array)
+    predicted_class = class_names[np.argmax(predictions[0])]
+    confidence = round(100 * (np.max(predictions[0])), 2)
+    return {class_names[i]: float(predictions[0][i]) for i in range(3)}
+
+
+article = "<h3>How to Use:</h3> " \
+          "<ul><li>Click on the Upload button to upload an image,you can also drag the image to the upload box.</li> " \
+          "<li>Choose a Image from your computer</li>" \
+          "<li>Click on the 'Submit' button. <strong>Voila!</strong>. " \
+          "and labels will be displayed on screen.</li></ul>"
+
+
+# with gr.Blocks() as demo:
+demo = gr.Interface(fn=predict,
+                    inputs=[gr.Image(label="Upload an image",show_share_button=True,interactive=True,show_download_button=True)],
+                    outputs=[gr.Label(num_top_classes=3,label="Predictions")],
+                    title="Potato Disease Classification",
+                    description="",
+                    examples=['sample_images\potato_early_blight.JPG','sample_images\potato_healty.JPG','sample_images\potato_late_blight.JPG'],
+                    allow_flagging="never",
+                    article=article,
+                    theme=gr.themes.Soft(),
+                    )              
+
+demo.launch(debug=True,share=True)
+