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import gradio as gr |
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from langchain import HuggingFaceHub |
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from langchain.chains.question_answering import load_qa_chain |
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from langchain.document_loaders import PyMuPDFLoader, TextLoader |
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from langchain.embeddings import HuggingFaceEmbeddings |
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from langchain.text_splitter import RecursiveCharacterTextSplitter |
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from langchain.vectorstores import FAISS |
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from timeit import default_timer as timer |
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loader = TextLoader("rdna3.txt") |
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documents = loader.load() |
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splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=50) |
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chunks = splitter.split_documents(documents) |
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embeddings = HuggingFaceEmbeddings() |
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db = FAISS.from_documents(chunks, embeddings) |
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llm = HuggingFaceHub( |
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repo_id='google/flan-t5-base', |
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model_kwargs={"temperature": 0, "max_length": 128}) |
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chain = load_qa_chain(llm, chain_type="stuff") |
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def ask(question): |
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answers = db.similarity_search(question, k=4) |
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result = chain.run(input_documents=answers, question=question) |
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return result |
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ask("What is VGPR") |
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iface = gr.Interface( |
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fn=ask, |
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inputs=gr.Textbox(label="Question", placeholder="What is..."), |
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outputs=gr.Textbox(label="Answer"), |
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allow_flagging=False) |
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iface.launch(share=True) |
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