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import gradio as gr |
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from langchain.document_loaders import OnlinePDFLoader |
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from langchain.text_splitter import CharacterTextSplitter |
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from langchain.llms import HuggingFaceHub |
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from langchain.embeddings import HuggingFaceHubEmbeddings |
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from langchain.vectorstores import Chroma |
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from langchain.chains import RetrievalQA |
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def loading_pdf(): return 'Loading...' |
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def pdf_changes(pdf_doc, repo_id): |
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loader = OnlinePDFLoader(pdf_doc.name) |
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documents = loader.load() |
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text_splitter = CharacterTextSplitter(chunk_size=1024, chunk_overlap=0) |
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texts = text_splitter.split_documents(documents) |
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embeddings = HuggingFaceHubEmbeddings() |
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db = Chroma.from_documents(texts, embeddings) |
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retriever = db.as_retriever() |
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llm = HuggingFaceHub(repo_id=repo_id, model_kwargs={'temperature': 0.5, 'max_new_tokens': 2096}) |
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global qa |
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qa = RetrievalQA.from_chain_type(llm=llm, chain_type='stuff', retriever=retriever, return_source_documents=True) |
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return "Ready" |
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def add_text(history, text): |
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history = history + [(text, None)] |
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return history, '' |
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def bot(history): |
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response = infer(history[-1][0]) |
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history[-1][1] = response['result'] |
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return history |
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def infer(question): |
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query = question |
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result = qa({'query': query}) |
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return result |
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css=""" |
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#col-container {max-width: 700px; margin-left: auto; margin-right: auto;} |
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""" |
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title = """ |
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<h1>Chat with PDF</h1> |
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""" |
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with gr.Blocks(css=css, theme='NoCrypt/miku@1.2.1') as demo: |
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with gr.Column(elem_id='col-container'): |
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gr.HTML(title) |
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with gr.Column(): |
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pdf_doc = gr.File(label='Upload a PDF', file_types=['.pdf']) |
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repo_id = gr.Dropdown(label='LLM', |
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choices=[ |
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'mistralai/Mistral-7B-Instruct-v0.1', |
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'HuggingFaceH4/zephyr-7b-beta', |
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'meta-llama/Llama-2-7b-chat-hf', |
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'01-ai/Yi-6B-200K' |
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], |
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value='mistralai/Mistral-7B-Instruct-v0.1') |
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with gr.Row(): |
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langchain_status = gr.Textbox(label='Status', placeholder='', interactive=False) |
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load_pdf = gr.Button('Load PDF to LangChain') |
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chatbot = gr.Chatbot([], elem_id='chatbot') |
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question = gr.Textbox(label='Question', placeholder='Type your query') |
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submit_btn = gr.Button('Send') |
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repo_id.change(pdf_changes, inputs=[pdf_doc, repo_id], outputs=[langchain_status], queue=False) |
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load_pdf.click(pdf_changes, inputs=[pdf_doc, repo_id], outputs=[langchain_status], queue=False) |
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question.submit(add_text, [chatbot, question], [chatbot, question]).then(bot, chatbot, chatbot) |
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submit_btn.click(add_text, [chatbot, question], [chatbot, question]).then(bot, chatbot, chatbot) |
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demo.launch() |
