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  1. README.md +6 -6
  2. app.py +36 -0
  3. guide1.txt +0 -0
  4. requirements.txt +4 -0
README.md CHANGED
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  ---
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- title: QuestionMyDoc
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- emoji: 🏆
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- colorFrom: green
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- colorTo: blue
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  sdk: gradio
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- sdk_version: 4.22.0
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  app_file: app.py
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  pinned: false
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- license: mit
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  ---
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  Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
 
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  ---
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+ title: TalkToMyDoc Hitch Hikers Guide
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+ emoji: 🐠
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+ colorFrom: blue
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+ colorTo: green
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  sdk: gradio
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+ sdk_version: 3.27.0
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  app_file: app.py
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  pinned: false
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+ license: openrail
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  ---
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  Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
app.py ADDED
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+ from langchain.embeddings.openai import OpenAIEmbeddings
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+ from langchain.vectorstores import Chroma
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+ from langchain.text_splitter import CharacterTextSplitter
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+ from langchain.chains.question_answering import load_qa_chain
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+ from langchain.llms import OpenAI
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+ import os
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+
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+ with open("guide1.txt") as f:
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+ hitchhikersguide = f.read()
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+
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+ text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0, separator = "\n")
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+ texts = text_splitter.split_text(hitchhikersguide)
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+
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+ embeddings = OpenAIEmbeddings()
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+
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+ docsearch = Chroma.from_texts(texts, embeddings, metadatas=[{"source": str(i)} for i in range(len(texts))]).as_retriever()
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+
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+ chain = load_qa_chain(OpenAI(temperature=0), chain_type="stuff")
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+
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+ def make_inference(query):
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+ docs = docsearch.get_relevant_documents(query)
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+ return(chain.run(input_documents=docs, question=query))
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+
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+ if __name__ == "__main__":
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+ # make a gradio interface
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+ import gradio as gr
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+
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+ gr.Interface(
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+ make_inference,
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+ [
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+ gr.inputs.Textbox(lines=2, label="Query"),
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+ ],
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+ gr.outputs.Textbox(label="Response"),
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+ title="🗣️TalkToMyDoc📄",
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+ description="🗣️TalkToMyDoc📄 is a tool that allows you to ask questions about a document. In this case - Hitch Hitchhiker's Guide to the Galaxy.",
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+ ).launch()
guide1.txt ADDED
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requirements.txt ADDED
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+ langchain
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+ openai
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+ tiktoken
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+ chromadb