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import gradio as gr
from huggingface_hub import InferenceClient, login
import random
from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint, HuggingFacePipeline
from langchain.schema import AIMessage, HumanMessage
from langchain.tools import Tool
import os
import datasets
from langchain.docstore.document import Document
from retriever import extract_text
login(token=os.environ["HUGGINGFACEHUB_API_TOKEN"])
# Load the dataset
guest_dataset = datasets.load_dataset("agents-course/unit3-invitees", split="train")
# Convert dataset entries into Document objects
docs = [
Document(
page_content="\n".join([
f"Name: {guest['name']}",
f"Relation: {guest['relation']}",
f"Description: {guest['description']}",
f"Email: {guest['email']}"
]),
metadata={"name": guest["name"]}
)
for guest in guest_dataset
]
bm25_retriever = BM25Retriever.from_documents(docs)
llm = HuggingFaceEndpoint(
repo_id="HuggingFaceH4/zephyr-7b-beta",
task="text-generation",
max_new_tokens=512,
do_sample=False,
repetition_penalty=1.03,
)
model = ChatHuggingFace(llm=llm)
def predict(message, history):
history_langchain_format = []
for msg in history:
if msg['role'] == "user":
history_langchain_format.append(HumanMessage(content=msg['content']))
elif msg['role'] == "assistant":
history_langchain_format.append(AIMessage(content=msg['content']))
history_langchain_format.append(HumanMessage(content=message))
gpt_response = model.invoke(history_langchain_format)
return gpt_response.content
demo = gr.ChatInterface(
predict,
type="messages"
)
demo.launch() |