Upload gradio.py
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gradio.py
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import gradio as gr
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from transformers import pipeline
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classifier = pipeline("text-classification", model="tugot17/my-awesome-model", tokenizer=tokenizer)
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def predict(text):
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output = {}
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for result in classifier(text, top_k=None):
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output[result["label"]] = result["score"]
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return output
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iface = gr.Interface(
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fn=predict,
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inputs='text',
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outputs=gr.Label(num_top_classes=2),
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examples=[["SIX chances to win CASH! From 100 to 20,000 pounds txt> CSH11 and send to 87575. Cost 150p/day, 6days, 16+ TsandCs apply Reply HL 4 info"]]
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)
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iface.launch()
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