import gradio as gr from requests import head from transformer_vectorizer import TransformerVectorizer from sklearn.feature_extraction.text import TfidfVectorizer from concrete.ml.deployment import FHEModelClient import numpy import os from pathlib import Path import requests import json import base64 import subprocess import shutil import time # This repository's directory REPO_DIR = Path(__file__).parent subprocess.Popen(["uvicorn", "server:app"], cwd=REPO_DIR) # Wait 5 sec for the server to start time.sleep(5) # Encrypted data limit for the browser to display # (encrypted data is too large to display in the browser) ENCRYPTED_DATA_BROWSER_LIMIT = 500 N_USER_KEY_STORED = 20 model_names=['financial_rating','legal_rating'] FHE_MODEL_PATH = "deployment/financial_rating" FHE_LEGAL_PATH = "deployment/legal_rating" #FHE_MODEL_PATH="deployment/legal_rating" print("Loading the transformer model...") # Initialize the transformer vectorizer transformer_vectorizer = TransformerVectorizer() vectorizer = TfidfVectorizer() def clean_tmp_directory(): # Allow 20 user keys to be stored. # Once that limitation is reached, deleted the oldest. path_sub_directories = sorted([f for f in Path(".fhe_keys/").iterdir() if f.is_dir()], key=os.path.getmtime) user_ids = [] if len(path_sub_directories) > N_USER_KEY_STORED: n_files_to_delete = len(path_sub_directories) - N_USER_KEY_STORED for p in path_sub_directories[:n_files_to_delete]: user_ids.append(p.name) shutil.rmtree(p) list_files_tmp = Path("tmp/").iterdir() # Delete all files related to user_id for file in list_files_tmp: for user_id in user_ids: if file.name.endswith(f"{user_id}.npy"): file.unlink() model_nams=[] def keygen(selected_tasks): # Clean tmp directory if needed clean_tmp_directory() print("Initializing FHEModelClient...") if not selected_tasks: return "choose task first" # 修改提示信息为英文 if "legal_rating" in selected_tasks: model_names.append('legal_rating') if "financial_rating" in selected_tasks: model_names.append('financial_rating') # Let's create a user_id user_id = numpy.random.randint(0, 2**32) fhe_api = FHEModelClient(FHE_MODEL_PATH, f".fhe_keys/{user_id}") fhe_api.load() # Generate a fresh key fhe_api.generate_private_and_evaluation_keys(force=True) evaluation_key = fhe_api.get_serialized_evaluation_keys() # Save evaluation_key in a file, since too large to pass through regular Gradio # buttons, https://github.com/gradio-app/gradio/issues/1877 numpy.save(f"tmp/tmp_evaluation_key_{user_id}.npy", evaluation_key) return [list(evaluation_key)[:ENCRYPTED_DATA_BROWSER_LIMIT], user_id] def encode_quantize_encrypt(text, user_id): if not user_id: raise gr.Error("You need to generate FHE keys first.") fhe_api = FHEModelClient(FHE_MODEL_PATH, f".fhe_keys/{user_id}") fhe_api.load() encodings = transformer_vectorizer.transform([text]) quantized_encodings = fhe_api.model.quantize_input(encodings).astype(numpy.uint8) encrypted_quantized_encoding = fhe_api.quantize_encrypt_serialize(encodings) # Save encrypted_quantized_encoding in a file, since too large to pass through regular Gradio # buttons, https://github.com/gradio-app/gradio/issues/1877 numpy.save(f"tmp/tmp_encrypted_quantized_encoding_{user_id}.npy", encrypted_quantized_encoding) # Compute size encrypted_quantized_encoding_shorten = list(encrypted_quantized_encoding)[:ENCRYPTED_DATA_BROWSER_LIMIT] encrypted_quantized_encoding_shorten_hex = ''.join(f'{i:02x}' for i in encrypted_quantized_encoding_shorten) return ( encodings[0], quantized_encodings[0], encrypted_quantized_encoding_shorten_hex, ) def run_fhe(user_id): encoded_data_path = Path(f"tmp/tmp_encrypted_quantized_encoding_{user_id}.npy") if not user_id: raise gr.Error("You need to generate FHE keys first.") if not encoded_data_path.is_file(): raise gr.Error("No encrypted data was found. Encrypt the data before trying to predict.") # Read encrypted_quantized_encoding from the file encrypted_quantized_encoding = numpy.load(encoded_data_path) # Read evaluation_key from the file evaluation_key = numpy.load(f"tmp/tmp_evaluation_key_{user_id}.npy") # Use base64 to encode the encodings and evaluation key encrypted_quantized_encoding = base64.b64encode(encrypted_quantized_encoding).decode() encoded_evaluation_key = base64.b64encode(evaluation_key).decode() query = {} query["evaluation_key"] = encoded_evaluation_key query["encrypted_encoding"] = encrypted_quantized_encoding headers = {"Content-type": "application/json"} response = requests.post( "http://localhost:8000/predict_sentiment", data=json.dumps(query), headers=headers ) encrypted_prediction = base64.b64decode(response.json()["encrypted_prediction"]) # Save encrypted_prediction in a file, since too large to pass through regular Gradio # buttons, https://github.com/gradio-app/gradio/issues/1877 numpy.save(f"tmp/tmp_encrypted_prediction_{user_id}.npy", encrypted_prediction) encrypted_prediction_shorten = list(encrypted_prediction)[:ENCRYPTED_DATA_BROWSER_LIMIT] encrypted_prediction_shorten_hex = ''.join(f'{i:02x}' for i in encrypted_prediction_shorten) return encrypted_prediction_shorten_hex def decrypt_prediction(user_id): encoded_data_path = Path(f"tmp/tmp_encrypted_prediction_{user_id}.npy") if not user_id: raise gr.Error("You need to generate FHE keys first.") if not encoded_data_path.is_file(): raise gr.Error("No encrypted prediction was found. Run the prediction over the encrypted data first.") # Read encrypted_prediction from the file encrypted_prediction = numpy.load(encoded_data_path).tobytes() fhe_api = FHEModelClient(FHE_MODEL_PATH, f".fhe_keys/{user_id}") fhe_api.load() # We need to retrieve the private key that matches the client specs (see issue #18) fhe_api.generate_private_and_evaluation_keys(force=False) predictions = fhe_api.deserialize_decrypt_dequantize(encrypted_prediction) print(predictions) return { "low_relative": predictions[0][0], "medium_relative": predictions[0][1], "high_relative": predictions[0][2], } demo = gr.Blocks() print("Starting the demo...") with demo: gr.Markdown( """

Contract Analysis

""" ) gr.Markdown( """

""" ) gr.Markdown("## Notes") gr.Markdown( """ - The private key is used to encrypt and decrypt the data and shall never be shared. - The evaluation key is a public key that the server needs to process encrypted data. """ ) gr.Markdown("# Step 0: Select Tasks") task_checkbox = gr.CheckboxGroup( choices=["legal_rating", "financial_rating"], label="select_tasks" ) gr.Markdown("# Step 1: Generate the keys") b_gen_key_and_install = gr.Button("Generate all the keys and send public part to server") evaluation_key = gr.Textbox( label="Evaluation key (truncated):", max_lines=4, interactive=False, ) user_id = gr.Textbox( label="", max_lines=4, interactive=False, visible=False ) gr.Markdown("# Step 2: Provide a contract or clause") gr.Markdown("## Client side") gr.Markdown( "Enter a contract or clause you want to analysis)." ) text = gr.Textbox(label="Enter some words:", value="The Employee is entitled to two weeks of paid vacation annually, to be scheduled at the mutual convenience of the Employee and Employer.") gr.Markdown("# Step 3: Encode the message with the private key") b_encode_quantize_text = gr.Button( "Encode, quantize and encrypt the text with vectorizer, and send to server" ) with gr.Row(): encoding = gr.Textbox( label="Representation:", max_lines=4, interactive=False, ) quantized_encoding = gr.Textbox( label="Quantized representation:", max_lines=4, interactive=False ) encrypted_quantized_encoding = gr.Textbox( label="Encrypted quantized representation (truncated):", max_lines=4, interactive=False, ) gr.Markdown("# Step 4: Run the FHE evaluation") gr.Markdown("## Server side") gr.Markdown( "The encrypted value is received by the server. Thanks to the evaluation key and to FHE, the server can compute the (encrypted) prediction directly over encrypted values. Once the computation is finished, the server returns the encrypted prediction to the client." ) b_run_fhe = gr.Button("Run FHE execution there") encrypted_prediction = gr.Textbox( label="Encrypted prediction (truncated):", max_lines=4, interactive=False, ) gr.Markdown("# Step 5: Decrypt the class") gr.Markdown("## Client side") gr.Markdown( "The encrypted sentiment is sent back to client, who can finally decrypt it with its private key. Only the client is aware of the original tweet and the prediction." ) b_decrypt_prediction = gr.Button("Decrypt prediction") labels_sentiment = gr.Label(label="level:") # Button for key generation b_gen_key_and_install.click(keygen, inputs=[task_checkbox], outputs=[evaluation_key, user_id]) # Button to quantize and encrypt b_encode_quantize_text.click( encode_quantize_encrypt, inputs=[text, user_id], outputs=[ encoding, quantized_encoding, encrypted_quantized_encoding, ], ) # Button to send the encodings to the server using post at (localhost:8000/predict_sentiment) b_run_fhe.click(run_fhe, inputs=[user_id], outputs=[encrypted_prediction]) # Button to decrypt the prediction on the client b_decrypt_prediction.click(decrypt_prediction, inputs=[user_id], outputs=[labels_sentiment]) gr.Markdown( "The app was built with [Concrete-ML](https://github.com/zama-ai/concrete-ml), a Privacy-Preserving Machine Learning (PPML) open-source set of tools by [Zama](https://zama.ai/). Try it yourself and don't forget to star on Github ⭐." ) demo.launch(share=False)