Update app.py

app.py

@@ -4,8 +4,7 @@ from langchain.llms import OpenAI
 from dotenv import load_dotenv
 import os
 import streamlit as st
-
-
+import pandas as pd
 
 def main():
     load_dotenv()
@@ -20,18 +19,38 @@ def main():
     st.sidebar.image("/home/oem/Downloads/insightly_wbg.png", use_column_width=True)
     st.header("Data Analysis 📈")
 
-    csv_file = st.file_uploader("Upload a CSV file", type="csv")
-    if csv_file:
-        with NamedTemporaryFile(delete=False) as f:
-            f.write(csv_file.getvalue())
-            f.flush()
-            llm = OpenAI(temperature=0)
-            user_input = st.text_input("Question here:")
-            agent = create_csv_agent(llm, f.name, verbose=True)
-            if user_input:
-                response = agent.run(user_input)
-                st.write(response)
+    csv_files = st.file_uploader("Upload CSV files", type="csv", accept_multiple_files=True)
+    if csv_files:
+        llm = OpenAI(temperature=0)
+        user_input = st.text_input("Question here:")
+
+        # Iterate over each CSV file
+        for csv_file in csv_files:
+            with NamedTemporaryFile(delete=False) as f:
+                f.write(csv_file.getvalue())
+                f.flush()
+                df = pd.read_csv(f.name)
+
+                # Perform any necessary data preprocessing or feature engineering here
+                # You can modify the code based on your specific requirements
+
+                # Example: Accessing columns from the DataFrame
+                # column_data = df["column_name"]
+
+                # Example: Applying transformations or calculations to the data
+                # transformed_data = column_data.apply(lambda x: x * 2)
+
+                # Example: Using the preprocessed data with the OpenAI API
+                # llm_response = llm.predict(transformed_data)
+
+                if user_input:
+                    # Pass the user input to the OpenAI agent for processing
+                    agent = create_csv_agent(llm, f.name, verbose=True)
+                    response = agent.run(user_input)
 
+                    st.write(f"CSV File: {csv_file.name}")
+                    st.write("Response:")
+                    st.write(response)
 
 if __name__ == "__main__":
     main()