| import gradio as gr | |
| from langchain.text_splitter import RecursiveCharacterTextSplitter | |
| from langchain.llms import HuggingFaceHub | |
| from langchain.embeddings import HuggingFaceEmbeddings | |
| from langchain.vectorstores import Chroma | |
| from langchain.chains import RetrievalQA | |
| from langchain.document_loaders import PyMuPDFLoader | |
| def load_doc(pdf_doc): | |
| loader = PyMuPDFLoader(pdf_doc.name) | |
| documents = loader.load() | |
| embedding = HuggingFaceEmbeddings() | |
| text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200) | |
| text = text_splitter.split_documents(documents) | |
| db = Chroma.from_documents(text, embedding) | |
| llm = HuggingFaceHub(repo_id="OpenAssistant/oasst-sft-1-pythia-12b", model_kwargs={"temperature": 1.0, "max_length": 256}) | |
| global chain | |
| chain = RetrievalQA.from_chain_type(llm=llm,chain_type="stuff",retriever=db.as_retriever()) | |
| return 'Document has successfully been loaded' | |
| def answer_query(query): | |
| question = query | |
| return chain.run(question) | |
| html = """ | |
| <div style="text-align:center; max width: 700px;"> | |
| <h1>ChatPDF</h1> | |
| <p> Upload a PDF File, then click on Load PDF File <br> | |
| Once the document has been loaded you can begin chatting with the PDF =) | |
| </div>""" | |
| css = """container{max-width:700px; margin-left:auto; margin-right:auto,padding:20px}""" | |
| with gr.Blocks(css=css,theme=gr.themes.Monochrome()) as demo: | |
| gr.HTML(html) | |
| with gr.Column(): | |
| gr.Markdown('ChatPDF') | |
| pdf_doc = gr.File(label="Load a pdf",file_types=['.pdf','.docx'],type='filepath') | |
| with gr.Row(): | |
| load_pdf = gr.Button('Load pdf file') | |
| status = gr.Textbox(label="Status",placeholder='',interactive=False) | |
| with gr.Row(): | |
| input = gr.Textbox(label="type in your question") | |
| output = gr.Textbox(label="output") | |
| submit_query = gr.Button("submit") | |
| load_pdf.click(load_doc,inputs=pdf_doc,outputs=status) | |
| submit_query.click(answer_query,input,output) | |
| demo.launch() |