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import gradio as gr |
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from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification |
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import torch |
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MODEL_NAME = "Devishetty100/redmoon-gibberishdetective" |
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try: |
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classifier = pipeline( |
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"text-classification", |
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model=MODEL_NAME |
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) |
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except: |
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try: |
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tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased") |
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model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME) |
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classifier = pipeline( |
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"text-classification", |
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model=model, |
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tokenizer=tokenizer, |
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device=0 if torch.cuda.is_available() else -1 |
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) |
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except Exception as e: |
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raise gr.Error(f"Failed to load model: {str(e)}\n\nPlease ensure all model files exist in the repository.") |
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def predict(text): |
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try: |
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if not text.strip(): |
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return {"Error": "Please enter some text"} |
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result = classifier(text)[0] |
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return { |
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"Prediction": result['label'], |
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"Confidence": f"{result['score']:.2%}" |
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} |
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except Exception as e: |
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return {"Error": str(e)} |
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with gr.Blocks(theme=gr.themes.Soft()) as demo: |
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gr.Markdown(""" |
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# 🔍 Gibberish Detective |
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*Using model: {MODEL_NAME}* |
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""") |
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with gr.Row(): |
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input_text = gr.Textbox(label="Input Text", placeholder="Enter text here...", lines=3) |
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output_json = gr.JSON(label="Results") |
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gr.Examples( |
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examples=[ |
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["This is proper English"], |
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["Xkjsd hfkjshdf 9834 kjsdhf!"], |
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["Hello world"], |
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["Hfjsd kjsadf lkjsdf 1234!"] |
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], |
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inputs=input_text |
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) |
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input_text.submit(predict, inputs=input_text, outputs=output_json) |
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gr.Button("Analyze").click(predict, inputs=input_text, outputs=output_json) |
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if __name__ == "__main__": |
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demo.launch() |
