Spaces:
Sleeping
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singhjagpreet
commited on
Commit
β’
4a5ab79
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Parent(s):
a554da2
env added
Browse files- Dockerfile +15 -0
- README.md +9 -0
- app.py +6 -2
Dockerfile
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FROM python:3.10
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WORKDIR /app
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COPY /src /app/src
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COPY requirements.txt /app/.
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COPY app.py /app/.
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COPY chainlit.md /app/.
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COPY .env /app/.
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RUN pip install -r requirements.txt
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CMD ["chainlit", "run", "app.py"]
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README.md
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# Document Reader
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Don't waste time reading lengthy Terms and Conditions! Upload your files here and ask anything you want to know.
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---
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title: DR
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emoji: π
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colorFrom: red
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colorTo: blue
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sdk: docker
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pinned: false
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---
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# Document Reader
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Don't waste time reading lengthy Terms and Conditions! Upload your files here and ask anything you want to know.
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app.py
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import os
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import logging
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#export HNSWLIB_NO_NATIVE = 1
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from langchain.chains import ConversationalRetrievalChain
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from langchain.chat_models import ChatOpenAI
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import chainlit as cl
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from src.config import Config
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from src.utils import get_docsearch, get_source
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# text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
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# embeddings = OpenAIEmbeddings()
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welcome_message = """Welcome to Your Document Reader!
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Here to assist you with any questions you have about a file. You can upload a file and ask me questions related to its content. Here's how you can get started:
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answer = res["answer"]
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source_documents = res["source_documents"]
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-
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text_elements = get_source(answer, source_documents)
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await cl.Message(content=answer, elements=text_elements).send()
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import os
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import logging
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# from dotenv import load_dotenv
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#export HNSWLIB_NO_NATIVE = 1
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from langchain.chains import ConversationalRetrievalChain
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from langchain.chat_models import ChatOpenAI
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import chainlit as cl
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import openai
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from src.config import Config
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from src.utils import get_docsearch, get_source
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# text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
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# embeddings = OpenAIEmbeddings()
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# load_dotenv()
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# OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
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welcome_message = """Welcome to Your Document Reader!
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Here to assist you with any questions you have about a file. You can upload a file and ask me questions related to its content. Here's how you can get started:
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answer = res["answer"]
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source_documents = res["source_documents"]
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text_elements = get_source(answer, source_documents)
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await cl.Message(content=answer, elements=text_elements).send()
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