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import gradio as gr |
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import tensorflow as tf |
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import gdown |
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from PIL import Image |
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input_shape = (32, 32, 3) |
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resized_shape = (224, 224, 3) |
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num_classes = 10 |
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labels = { |
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0: "plane", |
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1: "car", |
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2: "bird", |
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3: "cat", |
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4: "deer", |
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5: "dog", |
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6: "frog", |
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7: "horse", |
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8: "ship", |
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9: "truck", |
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} |
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def download_model(): |
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url = "https://drive.google.com/uc?id=12700bE-pomYKoVQ214VrpBoJ7akXcTpL" |
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output = "modelV2Lmixed.keras" |
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gdown.download(url, output, quiet=False) |
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return output |
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model_file = download_model() |
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model = tf.keras.models.load_model(model_file) |
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def predict_class(image): |
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img = tf.cast(image, tf.float32) |
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img = tf.image.resize(img, [input_shape[0], input_shape[1]]) |
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img = tf.expand_dims(img, axis=0) |
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prediction = model.predict(img) |
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class_index = tf.argmax(prediction[0]).numpy() |
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predicted_class = labels[class_index] |
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return predicted_class |
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def classify_image(image): |
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predicted_class = predict_class(image) |
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output = f"<h2>Predicted Class:</h2><p>{predicted_class}</p>" |
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return output |
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inputs = gr.inputs.Image(label="Upload an image") |
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outputs = gr.outputs.HTML() |
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title = "<h1 style='text-align: center;'>Image Classifier</h1>" |
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description = "Upload an image and get the predicted class." |
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gr.Interface(fn=classify_image, inputs=inputs, outputs=outputs, title=title, description=description).launch() |
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