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import gradio as gr
import os
from langchain.document_loaders import WebBaseLoader
from langchain_community.vectorstores import FAISS
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chains import ConversationalRetrievalChain
from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferMemory
from langchain_community.llms import HuggingFaceEndpoint
from sentence_transformers import SentenceTransformer
from langchain_ollama import ChatOllama
import re
import torch
def preprocessing_text(document: list) -> list:
document[0].page_content = re.sub(r"\n{2,}", "\n\n", document[0].page_content)
return document
def loading_the_webpage(url: str) -> list:
loader = WebBaseLoader(url)
document = preprocessing_text(loader.load())
return document
def chunking(document: list) -> list:
text_splitter = RecursiveCharacterTextSplitter(chunk_size= 1024,
chunk_overlap= 128,
separators= ["\n\n", "\n", " ", ""])
return text_splitter.split_documents(documents= document)
def create_vector_db(chunked_documents):
embeddings = SentenceTransformer("nomic-ai/nomic-embed-text-v1", trust_remote_code=True)
vector_db = FAISS.from_documents(chunked_documents, embeddings)
return vector_db
# Initialize langchain LLM chain
def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
llm = ChatOllama(model= "mistral",
temperature= temperature,
top_k= top_k,
num_predict= max_tokens)
memory = ConversationBufferMemory(
memory_key="chat_history",
output_key='answer',
return_messages=True
)
retriever=vector_db.as_retriever()
qa_chain = ConversationalRetrievalChain.from_llm(
llm,
retriever=retriever,
chain_type="stuff",
memory=memory,
return_source_documents=True,
verbose=False,
)
return qa_chain
def process_url_and_query(url: str, query: str):
# Load and process the webpage
documents = loading_the_webpage(url)
documents = preprocessing_text(documents)
# Chunk the documents
chunked_documents = chunking(documents)
# Create a vector database from chunked documents
vector_db = create_vector_db(chunked_documents)
# Initialize the LLM chain
qa_chain = initialize_llmchain(llm_model="mistral", temperature=0.7, max_tokens=150, top_k=5, vector_db=vector_db)
# Get the answer for the user's query
answer = qa_chain({"question": query})
return answer['answer']
with gr.Blocks() as demo:
gr.Markdown("# Webpage Querying App")
url_input = gr.Textbox(label="Enter URL")
query_input = gr.Textbox(label="Enter your query")
submit_button = gr.Button("Submit")
output_textbox = gr.Textbox(label="Response", interactive=False)
submit_button.click(process_url_and_query, inputs=[url_input, query_input], outputs=output_textbox)
# Launch the app
demo.launch()
# import gradio as gr
# from huggingface_hub import InferenceClient
# """
# 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
# """
# client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
# def respond(
# message,
# history: list[tuple[str, str]],
# system_message,
# max_tokens,
# temperature,
# 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})
# 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 a friendly Chatbot.", 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()
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