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from fastapi import FastAPI |
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from type.request.predict import PredictRequest |
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from type.response.predict import PredictResponse |
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from hugging_face import model, dataset |
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from transformer import transformer |
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from pipeline import pipeline |
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hate_speech_model = model.load_hugging_face_model('model_rf.pkl') |
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hate_speech_dataset = dataset.load_dataset('data_clean.csv') |
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tfidf = transformer.create_tfidf(hate_speech_dataset, 'Tweet', 'U') |
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app = FastAPI() |
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@app.get("/") |
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def root(): |
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return {"message": "All system running well :)"} |
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@app.get("/healthz") |
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def healthz(): |
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return {"message": "All system running well :)"} |
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@app.post("/predict") |
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def predict(request: PredictRequest): |
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preprocessed_text = pipeline.preprocessing(request.predict_text) |
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predict_text = [preprocessed_text] |
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predict_text = tfidf.transform(predict_text) |
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prediction = hate_speech_model.predict(predict_text) |
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return PredictResponse( |
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predict_text = request.predict_text, |
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is_hate_speech = prediction[0] == 1 |
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) |
