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import gradio as gr |
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from huggingface_hub import InferenceClient, login, snapshot_download |
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from langchain_community.vectorstores import FAISS |
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from langchain_huggingface import HuggingFaceEmbeddings |
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import os |
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import pandas as pd |
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""" |
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For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co./docs/huggingface_hub/v0.22.2/en/guides/inference |
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""" |
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login(token=os.getenv('TOKEN')) |
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model = "google/mt5-small" |
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client = InferenceClient(model) |
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folder = snapshot_download(repo_id="umaiku/faiss_index", repo_type="dataset", local_dir=os.getcwd()) |
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embeddings = HuggingFaceEmbeddings(model_name="intfloat/multilingual-e5-small") |
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vector_db = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True) |
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df = pd.read_csv("faiss_index/bger_cedh_db 1954-2024.csv") |
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def respond( |
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message, |
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history: list[tuple[str, str]], |
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system_message, |
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max_tokens, |
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temperature, |
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top_p, |
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score, |
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): |
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messages = [{"role": "system", "content": system_message}] |
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print(system_message) |
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retriever = vector_db.as_retriever(search_type="similarity_score_threshold", search_kwargs={"score_threshold": score}) |
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documents = retriever.invoke(message) |
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spacer = " \n" |
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context = "" |
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for doc in documents[:3]: |
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case_text = df[df["case_url"] == doc.metadata["case_url"]].case_text.values[0] |
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context += "Case number: " + doc.metadata["case_nb"] + spacer |
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context += "Case date: " + doc.metadata["case_date"] + spacer |
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context += "Case url: " + doc.metadata["case_url"] + spacer |
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context += "Case text: " + case_text + spacer |
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message = f""" |
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A user is asking you the following question: {message} |
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Please answer the user in the same language that he used in his question. |
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Use the following context collected from various Swiss federal jurisprudence cases: |
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{context} |
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Please mention your sources in your answer, including the urls and dates. |
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Always answer the user using the language used in his question which was: {message} |
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""" |
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print(message) |
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messages.append({"role": "user", "content": message}) |
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response = "" |
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for message in client.chat_completion( |
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messages, |
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max_tokens=max_tokens, |
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stream=True, |
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temperature=temperature, |
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top_p=top_p, |
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): |
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token = message.choices[0].delta.content |
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response += token |
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yield response |
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""" |
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For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface |
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""" |
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demo = gr.ChatInterface( |
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respond, |
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additional_inputs=[ |
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gr.Textbox(value="You are an assistant in Swiss Jurisprudence cases.", label="System message"), |
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gr.Slider(minimum=1, maximum=24000, value=5000, step=1, label="Max new tokens"), |
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gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), |
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gr.Slider( |
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minimum=0.1, |
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maximum=1.0, |
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value=0.95, |
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step=0.05, |
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label="Top-p (nucleus sampling)", |
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), |
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gr.Slider(minimum=0, maximum=1, value=0.7, step=0.1, label="Score Threshold"), |
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], |
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description="# 📜 ALexI: Artificial Legal Intelligence for Swiss Jurisprudence", |
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) |
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if __name__ == "__main__": |
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demo.launch(debug=True) |
