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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+Cat_transfer_learning_MobileNetV2.keras filter=lfs diff=lfs merge=lfs -text

Cat_transfer_learning_MobileNetV2.keras

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c06dfbfe5de9013d8cce0dabffad096d00fcdab25e96ca035087c50c934f3575
+size 27577542

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+model_path = "C:\Users\salih\Project_imageclass_Hasicic_Salih\Project_Inference_Dogs\Cat_transfer_learning_MobileNetV2.keras"
+model = tf.keras.models.load_model(model_path)
+
+# Define the core prediction function
+def predict_dog(image):
+    # Preprocess image
+    print(type(image))
+    image = Image.fromarray(image.astype('uint8'))  # Convert numpy array to PIL image
+    image = image.resize((150, 150)) #resize the image to 28x28 and converts it to gray scale
+    image = np.array(image)
+    image = np.expand_dims(image, axis=0) # same as image[None, ...]
+    
+    # Predict
+    prediction = model.predict(image)
+    
+     # No need to apply sigmoid, as the output layer already uses softmax
+    # Convert the probabilities to rounded values
+    prediction = np.round(prediction, 3)
+
+    # Separate the probabilities for each class
+    p_americanshorthair = prediction[0][0]  # Probability for class 'articuno'
+    p_bengal = prediction[0][1]   # Probability for class 'moltres'
+    p_mainecoon = prediction[0][2]    # Probability for class 'zapdos'
+    p_ragdoll = prediction[0][3]    # Probability for class 'zapdos'
+    p_scottishfold = prediction[0][4]
+    p_sphinx = prediction[0][5]
+
+ 
+    return {'americanshorthair':  p_americanshorthair, 'bengal': p_bengal, 'mainecoon': p_mainecoon, 'ragdoll': p_ragdoll, 'scottishfold': p_scottishfold, 'sphinx': p_sphinx }
+
+# Create the Gradio interface
+input_image = gr.Image()
+iface = gr.Interface(
+    fn=predict_dog,
+    inputs=input_image,
+    outputs=gr.Label(),
+    examples=["images/americanshorthair_1.jpg", "images/americanshorthair_2.jpg", "images/americanshorthair_3.jpg", "images/bengal_1.jpg", "images/bengal_2.jpeg", "images/bengal_3.jpg", "images/mainecoon_1.jpg", "images/mainecoon_2.jpeg", "images/mainecoon_3.jpg", "images/ragdoll_1.jpg", "images/ragdoll_2.jpg", "images/ragdoll_3.jpeg", "images/scottishfold_1.jpeg", "images/scottishfold_2.jpg", "images/scottishfold_3.jpg", "images/sphinx_1.jpg", "images/sphinx_2.jpg", "images/sphinx_3.jpg"],  
+    description="TEST.")
+
+iface.launch()