Create app.py

app.py

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+import tensorflow as tf
+import gradio as gr
+import cv2
+
+used_model = tf.keras.models.load_model('/model')
+new_classes = ['blight', 'common_rust', 'gray_leaf_spot','healthy']
+
+def classify_image(img_dt):
+  img_dt = cv2.resize(img_dt,(256,256))
+  img_dt = img_dt.reshape((-1,256,256,3)) 
+  prediction = used_model.predict(img_dt).flatten()
+  confidences = {new_classes[i]: float(prediction[i]) for i in range (4) }
+  return confidences
+
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        signal = gr.Markdown(''' #Welcome to Maize Classifier, This model can identify if a leaf is 
+        **HEALTHY**, has **COMMON RUST**, **BLIGHT** or **GRAY LEAF SPOT**''')
+        inp = gr.image()
+        out = gr.Label()
+        inp.upload(fn= classify_image, inputs = inp, outputs = out, show_progrss = True)