Spaces:
Running
Running
File size: 5,017 Bytes
49e74da 9dc56a0 9195e0a 9dc56a0 9195e0a 9dc56a0 9195e0a 9dc56a0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 |
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()
|