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| import gradio as gr | |
| from transformers import pipeline | |
| from huggingface_hub import InferenceClient, login, snapshot_download | |
| from langchain_community.vectorstores import FAISS | |
| from langchain_huggingface import HuggingFaceEmbeddings | |
| import os | |
| import pandas as pd | |
| """ | |
| 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 | |
| """ | |
| HF_TOKEN=os.getenv('TOKEN') | |
| login(HF_TOKEN) | |
| #model = "meta-llama/Llama-3.2-1B-Instruct" | |
| #model = "google/mt5-small" | |
| model = "mistralai/Mistral-7B-Instruct-v0.3" | |
| client = InferenceClient(model) | |
| folder = snapshot_download(repo_id="umaiku/faiss_index", repo_type="dataset", local_dir=os.getcwd()) | |
| embeddings = HuggingFaceEmbeddings(model_name="intfloat/multilingual-e5-small") | |
| vector_db = FAISS.load_local("faiss_index_8k", embeddings, allow_dangerous_deserialization=True) | |
| df = pd.read_csv("faiss_index/bger_cedh_db 1954-2024.csv") | |
| system_prompt = """ You are an assistant in Swiss Jurisprudence law. | |
| Please answer the user in the same language that he used in his question using the following given context, not prior or other knowledge. | |
| If no relevant cases were retrieved or the issue has not been addressed within the context, just say "I can't find enough relevant information". | |
| Don't make up an answer or give irrelevant information not requested by the user and do not give any links that are not provided in the context. | |
| Otherwise, if relevant cases were found, start by summarizing these cases in the user's question's language and reference the sources, including the source, urls and dates. | |
| """ | |
| retriever = vector_db.as_retriever(search_type="similarity_score_threshold", search_kwargs={"score_threshold": score}) | |
| def respond( | |
| message, | |
| history: list[tuple[str, str]], | |
| system_message, | |
| max_tokens, | |
| temperature, | |
| top_p, | |
| score, | |
| ): | |
| documents = retriever.invoke(message) | |
| spacer = " \n" | |
| context = "" | |
| print(len(documents)) | |
| for doc in documents: | |
| case_text = df[df["case_url"] == doc.metadata["case_url"]].case_text.values[0] | |
| context += "Case number: " + doc.metadata["case_nb"] + spacer | |
| context += "Case source: " + ("Swiss Federal Court" if doc.metadata["case_ref"] == "ATF" else "European Court of Human Rights") + spacer | |
| context += "Case date: " + doc.metadata["case_date"] + spacer | |
| context += "Case url: " + doc.metadata["case_url"] + spacer | |
| context += "Case text: " + doc.page_content + spacer | |
| #context += "Case text: " + case_text[:8000] + spacer | |
| system_message += f""" | |
| The following case extracts have been found in either Swiss Federal Court or European Court of Human Rights cases and could fit the question: | |
| {context} | |
| Task: Always answer the user using the language used in his question | |
| """ | |
| messages = [{"role": "system", "content": system_message}] | |
| print(system_message) | |
| # message = f""" | |
| #The user is asking you the following question: {message} | |
| #The following case extracts have been found in either Swiss Federal Court or European Court of Human Rights cases and could fit the question: | |
| #{context} | |
| #Task: Always answer the user using the language used in his question: {message} | |
| # """ | |
| # print(message) | |
| # for val in history: | |
| # if val[0]: | |
| # messages.append({"role": "user", "content": val[0]}) | |
| # if val[1]: | |
| # messages.append({"role": "assistant", "content": val[1]}) | |
| messages.append({"role": "user", "content": message}) | |
| response = "" | |
| for message in client.chat_completion( | |
| messages, | |
| max_tokens=max_tokens, | |
| stream=True, | |
| temperature=temperature, | |
| top_p=top_p, | |
| ): | |
| token = message.choices[0].delta.content | |
| response += token | |
| yield response | |
| """ | |
| For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface | |
| """ | |
| demo = gr.ChatInterface( | |
| respond, | |
| additional_inputs=[ | |
| gr.Textbox(value=system_prompt, label="System message"), | |
| gr.Slider(minimum=1, maximum=24000, value=5000, step=1, label="Max new tokens"), | |
| gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), | |
| gr.Slider( | |
| minimum=0.1, | |
| maximum=1.0, | |
| value=0.95, | |
| step=0.05, | |
| label="Top-p (nucleus sampling)", | |
| ), | |
| gr.Slider(minimum=0, maximum=1, value=0.7, step=0.1, label="Score Threshold"), | |
| ], | |
| description="# 📜 ALexI: Artificial Legal Intelligence for Swiss Jurisprudence", | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch(debug=True) |