app.py

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", embeddings, allow_dangerous_deserialization=True)

df = pd.read_csv("faiss_index/bger_cedh_db 1954-2024.csv")

def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
    score,
):
    messages = [{"role": "system", "content": system_message}]

    print(system_message)
    
    retriever = vector_db.as_retriever(search_type="similarity_score_threshold", search_kwargs={"score_threshold": 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 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
    
    
    message = f"""
A user is asking you the following question: {message}
Please answer the user in the same language that he used in his question using the following given context not prior knowledge.
Context:
The following case extracts from various Swiss Federal Court and European Court of Human Rights cases have been found to fit the question :
{context}
Task:
Start by summarizing these case in the user's question's language and reference the sources, including the urls and dates.
Instructions:
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="You are an assistant in Swiss Jurisprudence cases.", 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)