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from transformers import pipeline |
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import gradio as gr |
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modelName = "Melanoma-Cancer-Image-Classification" |
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hfUser = "Hemg" |
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def prediction_function(inputFile): |
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modelPath = hfUser + "/" + modelName |
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classifier = pipeline("image-classification", model=modelPath) |
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try: |
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result = classifier(inputFile) |
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predictions = dict() |
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labels = [] |
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for eachLabel in result: |
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predictions[eachLabel["label"]] = eachLabel["score"] |
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labels.append(eachLabel["label"]) |
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result = predictions |
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if "out of context image" in result: |
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raise ValueError("Out of context image provided") |
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except Exception as e: |
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result = "no data provided!!" |
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return result |
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def create_demo(): |
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demo = gr.Interface( |
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fn=prediction_function, |
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inputs=gr.Image(type="pil"), |
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outputs=gr.Label(num_top_classes=2), |
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) |
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demo.launch(auth=("admin", "Gr@ce")) |
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create_demo() |
