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import gradio as gr |
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import logging |
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from langchain.chains import RetrievalQA |
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from langchain.llms import OpenAI |
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from langchain.document_loaders import PDFMinerLoader |
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from langchain.indexes import VectorstoreIndexCreator |
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import os |
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def set_openai_key(raw_key): |
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logging.warning(raw_key) |
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os.environ["OPENAI_API_KEY"] = raw_key |
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def create_langchain(pdf_object): |
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logging.info(f"Creating langchain for {pdf_object.name}") |
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loader = PDFMinerLoader(pdf_object.name) |
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index_creator = VectorstoreIndexCreator() |
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docsearch = index_creator.from_loaders([loader]) |
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chain = RetrievalQA.from_chain_type( |
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llm=OpenAI(), |
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chain_type="stuff", |
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retriever=docsearch.vectorstore.as_retriever(), |
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input_key="question", |
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verbose=True, |
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return_source_documents=True, |
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) |
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return chain, gr.Button.update(interactive=True) |
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def ask_question(chain, question_text): |
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logging.info(type(chain)) |
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return chain({"question": question_text})["result"] |
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def create_ask(pdf_object, question_text): |
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loader = PDFMinerLoader(pdf_object.name) |
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index_creator = VectorstoreIndexCreator() |
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docsearch = index_creator.from_loaders([loader]) |
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chain = RetrievalQA.from_chain_type( |
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llm=OpenAI(), |
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chain_type="stuff", |
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retriever=docsearch.vectorstore.as_retriever(), |
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input_key="question", |
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verbose=True, |
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return_source_documents=True, |
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) |
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return chain({"question": question_text})["result"] |
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with gr.Blocks() as demo: |
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oai_token = gr.Textbox( |
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label="OpenAI Token", |
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placeholder="Lm-iIas452gaw3erGtPar26gERGSA5RVkFJQST23WEG524EWEl", |
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) |
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oai_token.change(set_openai_key, oai_token) |
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pdf_object = gr.File( |
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label="Upload your CV in PDF format", file_count="single", type="file" |
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) |
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chain_state = gr.State() |
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question_placeholder = """ |
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Enumerate the candidate's top 5 hard skills and rate them by importance from 0 to 5. |
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Example: |
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- Algebra 5/5 |
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""" |
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question_box = gr.Textbox(label="Question", value=question_placeholder) |
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qa_button = gr.Button(value="Submit question", interactive=False) |
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lchain = pdf_object.change( |
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create_langchain, inputs=pdf_object, outputs=[chain_state, qa_button] |
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) |
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qa_button.click( |
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ask_question, |
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inputs=[chain_state, question_box], |
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outputs=gr.Textbox(label="Answer"), |
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) |
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demo.launch(debug=True) |
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