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import gradio as gr
from huggingface_hub import InferenceClient, login, snapshot_download
from langchain_community.vectorstores import FAISS
from langchain_huggingface import HuggingFaceEmbeddings
import os
"""
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
"""
login(token=os.getenv('TOKEN'))
client = InferenceClient("meta-llama/Llama-3.2-1B-Instruct")
#client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.3")
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)
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
score,
):
messages = [{"role": "system", "content": system_message}]
retriever = vector_db.as_retriever(search_type="similarity_score_threshold", search_kwargs={"score_threshold": score})
documents = retriever.invoke(message)
"""
if document == []:
message = message + "\nNo cases were found about this subject"
else:
message = message + "\nUse the following jurisprudence case to answer " + documents[0].page_content + "\n Give the following url " + documents[0].metadata["case_url"]
"""
spacer = " \n "
context = ""
for doc in documents:
context += "Case number: " + doc.metadata["case_nb"] + "\n"
context += "Case date: " + doc.metadata["case_date"] + "\n"
context += "Case url: " + doc.metadata["case_url"] + "\n"
context += "Case chunk: " + doc.page_content + "\n"
message = f"""
The user is asking for information about the following: {message}.
Answer him in his own language using the information from the following Swiss federal jurisprudence cases:
{context}
Please mention your sources in your answer.
If you don't know just mention the sources.
"""
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=512, 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) |