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import gradio as gr |
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from tensorflow.keras.models import load_model |
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from PIL import Image |
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import numpy as np |
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import zipfile |
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import tempfile |
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import os |
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def classify_images(uploaded_file, images): |
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with tempfile.TemporaryDirectory() as temp_dir: |
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with zipfile.ZipFile(uploaded_file, 'r') as zip_ref: |
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zip_ref.extractall(temp_dir) |
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model_path = os.path.join(temp_dir, 'keras_model.h5') |
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labels_path = os.path.join(temp_dir, 'labels.txt') |
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loaded_model = load_model(model_path) |
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with open(labels_path, 'r') as file: |
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class_names = file.read().splitlines() |
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predictions = [] |
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for img in images: |
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with Image.open(img) as pil_image: |
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image = pil_image.resize((224, 224)) |
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image = np.array(image) |
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image = image / 255.0 |
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image = np.expand_dims(image, axis=0) |
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prediction = loaded_model.predict(image) |
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predicted_class = class_names[np.argmax(prediction)] |
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predictions.append(predicted_class) |
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return predictions |
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iface = gr.Interface( |
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fn=classify_images, |
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inputs=[ |
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gr.File(label="Upload do Modelo (.h5 or .zip (with .h5))"), |
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gr.Files(label="Upload of Images") |
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], |
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outputs="text", |
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title="GTM-Keras-h5-Predictor", |
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description="In Google Teachable Machine, after training, under 'Export Model', go to 'Tensorflow', click on 'Keras' and then 'Download my model' (wait a moment). The zip will contain the Keras .h5 model.", |
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examples=[["converted_keras.zip", ["example1.jpg", "example2.jpg"]]] |
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) |
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if __name__ == "__main__": |
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iface.launch(debug=True) |
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