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import gradio as gr
from huggingface_hub import InferenceClient
from langchain_community.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.vectorstores import FAISS
from sentence_transformers import SentenceTransformer
from langchain.chains import RetrievalQA
from langchain_community.llms import HuggingFaceHub
# Load the PDF document
loader = PyPDFLoader("apexcustoms.pdf")
data = loader.load()
# Split the document into chunks
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=20)
texts = text_splitter.split_documents(data)
# Create a vector store
embeddings = SentenceTransformer("sentence-transformers/all-mpnet-base-v2")
vector_store = FAISS.from_texts(texts, embeddings)
# Initialize the HuggingFaceHub LLM
llm = HuggingFaceHub(repo_id="HuggingFaceH4/zephyr-7b-beta", model_kwargs={"temperature": None, "top_p": None})
# Initialize the RetrievalQA chain
qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=vector_store.as_retriever())
def respond(message, history, system_message, max_tokens, temperature, top_p):
# Update the temperature and top_p values for the LLM
llm.model_kwargs["temperature"] = temperature
llm.model_kwargs["top_p"] = top_p
messages = [{"role": "system", "content": system_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})
result = qa({"input_documents": texts, "question": message})
response = result["result"]
history.append((message, response))
return response, history
"""
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 a helpful car configuration assistant, specifically you are the assistant for Apex Customs (https://www.apexcustoms.com/). Given the user's input, provide suggestions for car models, colors, and customization options. Be creative and conversational in your responses. You should remember the user car model and tailor your answers accordingly. (You must not generate the next question of the user yourself, you only have to answer.) \n\nUser: ", label="System message"),
gr.Slider(minimum=1, maximum=2048, 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)"),
],
)
if __name__ == "__main__":
demo.launch() |