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Chicken_Heart_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d783c67e3831d9f027248cdabec0463807c16871692e4cd6100a6e0d1341b012
+size 220570576

app.py

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+from keras.models import load_model
+import cv2
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+import gradio as gr
+import numpy as np
+
+heart_model=load_model('Chicken_Heart_model.h5',compile=True)
+class_name={0:'Dilation(eccentric)',1:'Hepatoma',2:'Hypertrophy(concentric)',3:'Hypertrophy(physiological)',4:'Infraction Damage',5:'Normal'}
+
+def Heart_Disease_prediction(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),(128,128))
+  class_no=heart_model.predict(img.reshape(1,128,128,3)).argmax()
+  name=class_name.get(class_no) 
+  return name 
+
+
+interface=gr.Interface(fn=Heart_Disease_prediction,inputs='image',outputs=[gr.components.Textbox(label='Disease Name')],
+                      examples=[['Image1.PNG'],['Image2.PNG'],['Image3.PNG'],['Image4.PNG'],
+                                ['Image5.PNG'],['Image6.PNG'],['Image7.PNG'],['Image8.PNG'],
+                                ['Image9.PNG'],['Image10.PNG'],['Image11.PNG']])
+
+interface.launch(debug=True)
+
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requirements.txt

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+tensorflow==2.12.0
+keras
+opencv-python