Update app.py

app.py

@@ -2,6 +2,21 @@ import streamlit as st
 from streamlit_chat import message
 # Initialize the CSVLoader to load the uploaded CSV file
 from langchain.document_loaders.csv_loader import CSVLoader
+# Allow users to upload a CSV file
+uploaded_file = st.sidebar.file_uploader("Upload your Data", type="csv")
+
+if uploaded_file:
+    # Initialize the CSVLoader to load the uploaded CSV file
+    import tempfile
+    with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
+        tmp_file.write(uploaded_file.getvalue())
+        tmp_file_path = tmp_file.name
+
+    loader = CSVLoader(file_path=tmp_file_path, encoding="utf-8", csv_args={'delimiter': ','})
+    data = loader.load()
+    db.save_local(DB_FAISS_PATH)
+    llm = load_llm()
+
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.embeddings import HuggingFaceEmbeddings
 from langchain.vectorstores import FAISS
@@ -15,9 +30,7 @@ st.title("Chat with CSV using open source LLM Inference Point 🦙🦜")
 # Display a markdown message with additional information
 st.markdown("<h3 style='text-align: center; color: white;'>Built by <a href='https://github.com/AIAnytime'>AI Anytime with ❤️ </a></h3>", unsafe_allow_html=True)
 
-loader = CSVLoader(file_path="data/2019.csv", encoding="utf-8", csv_args={"delimiter": ","})
 # Initialize the CSVLoader to load the uploaded CSV file
-    loader = CSVLoader(file_path="data/2019.csv", encoding="utf-8", csv_args={'delimiter': ','})
     data = loader.load()
     db.save_local(DB_FAISS_PATH)
     llm = load_llm()