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import tensorflow |
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from tensorflow import keras |
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from keras.models import load_model |
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model1 = load_model("inception.h5") |
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img_width, img_height = 180, 180 |
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class_names = ['daisy', 'dandelion', 'roses', 'sunflowers', 'tulips'] |
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num_classes = len(class_names) |
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def predict_image(img): |
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img_4d = img.reshape(-1, img_width, img_height, 3) |
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prediction = model1.predict(img_4d)[0] |
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return {class_names[i]: float(prediction[i]) for i in range(num_classes)} |
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import gradio as gr |
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image = gr.inputs.Image(shape=(img_height, img_width)) |
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label = gr.outputs.Label(num_top_classes=num_classes) |
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gr.Interface(fn=predict_image, inputs=image, outputs=label, title="Flower Classification using InceptionV3", interpretation='default').launch(debug='True', share='True') |
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