Create app.py

app.py

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+import cv2
+from keras.models import load_model
+import gradio as gr
+import numpy as np
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+
+model = load_model('/content/drive/MyDrive/Chicken_Gizzard_Updated_model.h5',compile=True)
+class_names={0:'Normal Appearance',
+             1:'The proventriculusof infected chickens showing several ecchymotic hemorrhages on the tip of the proventricular glandsat 3 dpi',
+             2:'edema with increased number of solitary and coalesced ecchymotic hemorrhages on theproventricular glands at 4 dpi',
+             3:'and numerous hemorrhagic spots coalesced to form brush paintappearance on the entire mucosa at 5 dpi'}
+
+def Predict_Gizzard(img):
+  img=cv2.imread(img)
+  img = img.reshape((1, img.shape[0], img.shape[1], img.shape[2]))
+
+  # Create the data generator with desired properties
+  datagen = ImageDataGenerator(
+    rotation_range=30,
+    width_shift_range=0.1,
+    height_shift_range=0.1,
+    shear_range=0.1,
+    zoom_range=0.1,
+    horizontal_flip=True,
+    fill_mode="nearest",
+  )
+  # Generate a batch of augmented images (contains only the single image)
+  augmented_images = datagen.flow(img, batch_size=1)
+  # Get the first (and only) augmented image from the batch
+  augmented_img = next(augmented_images)[0]
+  img=cv2.resize(augmented_img.astype(np.uint8),(224,224))
+  class_no=model.predict(img.reshape(1,224,224,3)).argmax()
+  name=class_names.get(class_no)
+  return name
+
+
+interface=gr.Interface(fn=Predict_Gizzard,inputs='image',outputs=[gr.components.Textbox(label='Your Result')],
+                      examples=[['Class A.PNG'],['Class B.PNG'],['Class C.PNG'],['Class D.PNG']])
+
+interface.launch(debug=True)
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