Upload app.py
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app.py
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# It provides functions for creating UIs, displaying data, and handling user inputs.
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# This module provides a way to interact with the operating system, such as accessing environment variables, working with files
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# and directories, executing shell commands, etc
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import os
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import streamlit as st
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# It allows you to store sensitive information or configuration settings separate from your code
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# and access them within your application.
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from dotenv import load_dotenv
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# Helps us generate embeddings
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# An embedding is a vector (list) of floating point numbers. The distance between two vectors measures their relatedness.
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embeddings = OpenAIEmbeddings()
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# The below snippet helps us to import CSV file data for our tasks
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from langchain.document_loaders.csv_loader import CSVLoader
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loader = CSVLoader(
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file_path="myData.csv",
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docs = db.similarity_search(user_input)
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print(docs)
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st.subheader("Top Matches:")
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st.text(docs[0])
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st.text(docs[1].page_content)
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st.text(docs[:3]
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# It provides functions for creating UIs, displaying data, and handling user inputs.
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# This module provides a way to interact with the operating system, such as accessing environment variables, working with files
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# and directories, executing shell commands, etc
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# import os
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import streamlit as st
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# It allows you to store sensitive information or configuration settings separate from your code
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# and access them within your application.
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from dotenv import load_dotenv
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from langchain.document_loaders.csv_loader import CSVLoader
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# Helps us generate embeddings
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# An embedding is a vector (list) of floating point numbers. The distance between two vectors measures their relatedness.
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embeddings = OpenAIEmbeddings()
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# The below snippet helps us to import CSV file data for our tasks
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loader = CSVLoader(
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file_path="myData.csv",
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docs = db.similarity_search(user_input)
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print(docs)
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st.subheader("Top Matches:")
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st.text(docs)
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st.text(docs[0])
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st.text(docs[1].page_content)
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st.text([doc.page_content for doc in docs[:3]])
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