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Update app.py
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app.py
CHANGED
@@ -34,7 +34,7 @@ model_names=['financial_rating','legal_rating']
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FHE_MODEL_PATH = "deployment/financial_rating"
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FHE_LEGAL_PATH = "deployment/legal_rating"
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#FHE_LEGAL_PATH="deployment/legal_rating"
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print("Loading the transformer model...")
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@@ -169,19 +169,9 @@ def keygen(selected_tasks):
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def encode_quantize_encrypt(text, user_id):
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if not user_id:
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raise gr.Error("You need to generate FHE keys first.")
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if encodings.shape[1] < 1736:
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# 在后面填充零
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padding = np.zeros((1, 1736 - encodings.shape[1]))
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encodings = np.hstack((encodings, padding))
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elif encodings.shape[1] > 1736:
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# 截取前1736列
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encodings = encodings[:, :1736]
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else:
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fhe_api = FHEModelClient(FHE_MODEL_PATH, f".fhe_keys/{user_id}")
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encodings = transformer_vectorizer.transform([text])
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fhe_api.load()
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quantized_encodings = fhe_api.model.quantize_input(encodings).astype(numpy.uint8)
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FHE_MODEL_PATH = "deployment/financial_rating"
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#FHE_LEGAL_PATH = "deployment/legal_rating"
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#FHE_LEGAL_PATH="deployment/legal_rating"
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print("Loading the transformer model...")
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def encode_quantize_encrypt(text, user_id):
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if not user_id:
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raise gr.Error("You need to generate FHE keys first.")
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fhe_api = FHEModelClient(FHE_MODEL_PATH, f".fhe_keys/{user_id}")
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encodings = transformer_vectorizer.transform([text])
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fhe_api.load()
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quantized_encodings = fhe_api.model.quantize_input(encodings).astype(numpy.uint8)
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