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| import gradio as gr | |
| import pandas as pd | |
| import json | |
| from langchain.document_loaders import DataFrameLoader | |
| from langchain.text_splitter import RecursiveCharacterTextSplitter | |
| from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings | |
| from langchain.vectorstores import Chroma | |
| from langchain.chains import RetrievalQA | |
| from langchain import HuggingFacePipeline | |
| from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline | |
| from trafilatura import fetch_url, extract | |
| from trafilatura.spider import focused_crawler | |
| from trafilatura.settings import use_config | |
| def loading_website(): | |
| return "Loading..." | |
| def url_changes(url, pages_to_visit, urls_to_scrape, repo_id): | |
| to_visit, links = focused_crawler(url, max_seen_urls=pages_to_visit, max_known_urls=urls_to_scrape) | |
| print(f"{len(links)} to be crawled") | |
| config = use_config() | |
| config.set("DEFAULT", "EXTRACTION_TIMEOUT", "0") | |
| results_df = pd.DataFrame() | |
| for url in links: | |
| downloaded = fetch_url(url) | |
| if downloaded: | |
| result = extract(downloaded, output_format='json', config=config) | |
| result = json.loads(result) | |
| results_df = pd.concat([results_df, pd.DataFrame.from_records([result])]) | |
| results_df.to_csv("./data.csv") | |
| df = pd.read_csv("./data.csv") | |
| loader = DataFrameLoader(df, page_content_column="text") | |
| documents = loader.load() | |
| print(f"{len(documents)} documents loaded") | |
| text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200) | |
| texts = text_splitter.split_documents(documents) | |
| print(f"documents splitted into {len(texts)} chunks") | |
| embeddings = SentenceTransformerEmbeddings(model_name="jhgan/ko-sroberta-multitask") | |
| persist_directory = './vector_db' | |
| db = Chroma.from_documents(texts, embeddings, persist_directory=persist_directory) | |
| retriever = db.as_retriever() | |
| MODEL = 'beomi/KoAlpaca-Polyglot-5.8B' | |
| model = AutoModelForCausalLM.from_pretrained( | |
| MODEL, | |
| torch_dtype="auto", | |
| ) | |
| model.eval() | |
| pipe = pipeline( | |
| 'text-generation', | |
| model=model, | |
| tokenizer=MODEL, | |
| max_length=512, | |
| temperature=0, | |
| top_p=0.95, | |
| repetition_penalty=1.15 | |
| ) | |
| llm = HuggingFacePipeline(pipeline=pipe) | |
| global qa | |
| qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever, return_source_documents=True) | |
| return "Ready" | |
| def add_text(history, text): | |
| history = history + [(text, None)] | |
| return history, "" | |
| def bot(history): | |
| response = infer(history[-1][0]) | |
| history[-1][1] = response['result'] | |
| return history | |
| def infer(question): | |
| query = question | |
| result = qa({"query": query}) | |
| return result | |
| css=""" | |
| #col-container {max-width: 700px; margin-left: auto; margin-right: auto;} | |
| """ | |
| title = """ | |
| <div style="text-align: center;max-width: 700px;"> | |
| <h1>Chat with your website</h1> | |
| <p style="text-align: center;">Enter target URL, click the "Load website to LangChain" button</p> | |
| </div> | |
| """ | |
| with gr.Blocks(css=css) as demo: | |
| with gr.Column(elem_id="col-container"): | |
| gr.HTML(title) | |
| with gr.Column(): | |
| target_url = gr.Textbox(label="Load URL", placeholder="Enter target URL here. EX: https://www.penta.co.kr/") | |
| #pdf_doc = gr.File(label="Load URL", file_types=['.pdf'], type="file") | |
| repo_id = gr.Dropdown(label="LLM", choices=["google/flan-ul2", "OpenAssistant/oasst-sft-1-pythia-12b", "beomi/KoAlpaca-Polyglot-12.8B"], value="google/flan-ul2") | |
| with gr.Row(): | |
| langchain_status = gr.Textbox(label="Status", placeholder="", interactive=False) | |
| load_pdf = gr.Button("Load website to langchain") | |
| chatbot = gr.Chatbot([], elem_id="chatbot").style(height=350) | |
| question = gr.Textbox(label="Question", placeholder="Type your question and hit Enter ") | |
| submit_btn = gr.Button("Send message") | |
| #load_pdf.click(loading_pdf, None, langchain_status, queue=False) | |
| repo_id.change(url_changes, inputs=[target_url, gr.Number(value=5, visible=False), gr.Number(value=50, visible=False), repo_id], outputs=[langchain_status], queue=False) | |
| load_pdf.click(url_changes, inputs=[target_url, gr.Number(value=5, visible=False), gr.Number(value=50, visible=False), repo_id], outputs=[langchain_status], queue=False) | |
| question.submit(add_text, [chatbot, question], [chatbot, question]).then( | |
| bot, chatbot, chatbot | |
| ) | |
| submit_btn.click(add_text, [chatbot, question], [chatbot, question]).then( | |
| bot, chatbot, chatbot | |
| ) | |
| demo.launch() |