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import gradio as gr |
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import joblib as joblib |
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model = joblib.load('random_forest_model_3labels2.joblib') |
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encoder = joblib.load('label_encoder2.joblib') |
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vectorizer = joblib.load('count_vectorizer2.joblib') |
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def predict(input_text): |
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vectorized_text = vectorizer.transform([input_text]) |
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prediction = model.predict(vectorized_text) |
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decoded_prediction = encoder.inverse_transform(prediction) |
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return decoded_prediction[0] |
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iface = gr.Interface(fn=predict, |
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inputs=gr.Textbox(lines=2, placeholder="Enter Text Here..."), |
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outputs="text", |
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description="Detects hate speech AGAINST GROUPS. Outputs 'Neutral or Ambiguous', 'Not Hate', or 'Offensive or Hate Speech'.") |
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iface.launch() |
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""" |
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import gradio as gr |
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def greet(name): |
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return "Hello " + name + "!!" |
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iface = gr.Interface(fn=greet, inputs="text", outputs="text") |
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iface.launch() |
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""" |
