arshad-ml commited on
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2e65ca5
1 Parent(s): 124b501

Upload app.py

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Files changed (1) hide show
  1. app.py +4 -3
app.py CHANGED
@@ -2,7 +2,7 @@
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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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@@ -10,6 +10,7 @@ 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.
@@ -31,7 +32,6 @@ st.header("Hey, Ask me something & I will give out similar things")
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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",
@@ -62,6 +62,7 @@ if submit:
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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].page_content)
 
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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]])