Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+from transformers import pipeline
+import io
+
+# Initialize the Hugging Face model pipeline (e.g., sentiment analysis, question answering, etc.)
+# Replace this with the model that fits your query processing needs
+model = pipeline("text-classification")  # Example for a text classification task
+
+def process_query(query, dataframe):
+    """
+    This function processes the user query with the Hugging Face model and returns the result.
+    You can adapt this based on your specific query processing.
+    """
+    # For simplicity, let's assume we're running text classification on a text column in the dataframe.
+    # This part will change depending on your use case.
+    results = []
+    for index, row in dataframe.iterrows():
+        result = model(row['text_column'])  # Example, modify 'text_column' to your actual column name
+        results.append(result[0]['label'])
+    
+    dataframe['query_result'] = results
+    return dataframe
+
+def handle_file_upload():
+    """
+    Function to handle multiple file uploads.
+    """
+    uploaded_files = st.file_uploader("Upload multiple Excel files", type=["xlsx"], accept_multiple_files=True)
+
+    if uploaded_files:
+        return uploaded_files
+    return None
+
+def main():
+    st.title("Excel Query Processing Application")
+    
+    # Step 1: File upload section
+    uploaded_files = handle_file_upload()
+    
+    if uploaded_files:
+        # Step 2: Process each uploaded Excel file
+        for file in uploaded_files:
+            # Read the Excel file into a DataFrame
+            df = pd.read_excel(file)
+            st.write(f"Data from {file.name}:")
+            st.write(df.head())  # Show a preview of the data
+            
+            # Step 3: Get user query input
+            query = st.text_input("Enter your query to process the file:", "")
+            
+            if query:
+                # Step 4: Process the query on the data
+                result_df = process_query(query, df)
+                
+                # Step 5: Display the processed result
+                st.write("Processed Result:")
+                st.write(result_df.head())  # Show a preview of the result
+                
+                # Step 6: Provide an option to download the processed file
+                output = io.BytesIO()
+                result_df.to_excel(output, index=False)
+                output.seek(0)
+                
+                st.download_button(
+                    label="Download Processed Excel",
+                    data=output,
+                    file_name=f"processed_{file.name}",
+                    mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
+                )
+
+if __name__ == "__main__":
+    main()