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Dogs_vs_Cat.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0cf39de911ef42ffafd82f8696162007d91b3f04b3eee6372a0ed1d5d08358c6
+size 24025984

app.py

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+import gradio as gr
+import tensorflow as tf
+import tf_keras as keras
+import gradio as gr
+import keras_cv
+
+model = keras_cv.models.ImageClassifier.from_preset(
+    "efficientnetv2_b0_imagenet",
+    num_classes=2,
+)
+
+model.compile(loss="sparse_categorical_crossentropy",
+              optimizer=keras.optimizers.Adam(),
+              metrics=["accuracy"])
+
+model.load_weights("model.h5")
+
+title = "Food Vision 101"
+
+
+description = f"""
+Dogs VS Cats is a deep learning model that classifies dogs and cats into using a TensorFlow model with 70% accuracy. 
+The model utilizes transfer learning and fine-tuning techniques to achieve high performance
+For the source code, you can visit the `Dogs_vs_Cats.ipynb` file.
+"""
+
+def perform_prediction(image, model=model,):
+    predictions = model(image)
+    
+    predicted_class_index = tf.argmax(predictions[0]).numpy()
+    
+    return ['cats','dogs'][predicted_class_index]
+
+demo = gr.Interface(fn=perform_prediction,
+                    inputs="image",
+                    outputs=gr.Label(num_top_classes=3, label="Predictions"),
+                    title=title,
+                    description=description)
+demo.launch()

requirements.txt

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+tensorflow
+efficientnet
+gradio
+tf_keras
+keras_cv