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import gradio as gr | |
import tensorflow as tf | |
import numpy as np | |
model = tf.keras.models.load_model("Sports_ball_prediction_v2.h5") | |
labels = ['american_football', 'baseball', 'basketball', 'billiard_ball', 'bowling_ball', 'cricket_ball', | |
'football', 'golf_ball', 'hockey_ball', 'hockey_puck', 'rugby_ball', 'shuttlecock', | |
'table_tennis_ball', 'tennis_ball', 'volleyball'] | |
def predict_image(inp): | |
if inp is not None: | |
img = tf.image.resize(inp, (224, 224)) | |
else: | |
print("Input is None. Unable to resize.") | |
img = tf.keras.preprocessing.image.img_to_array(img) | |
img = np.expand_dims(img, axis=0) | |
prediction = model.predict(img)[0] | |
return {labels[i]: float(prediction[i]) for i in range(len(labels))} | |
demo = gr.Interface(fn=predict_image, inputs='image',outputs=gr.Label(num_top_classes=3),title='Sports Ball Classification') | |
demo.launch(debug=True,share=True) |