Update app.py

app.py

@@ -2,56 +2,50 @@ import gradio as gr
 import pandas as pd
 
 def display_csv(file, supplier):
-    # Read the CSV file
     df = pd.read_csv(file.name)
-
-    # Filter DataFrame based on selected supplier
-    if supplier == "Supplier 3":
-        df = df[df['Vendor'].isin(["Seed", "EverColor"])]
-    elif supplier == "Supplier 2":
-        df = df[df['Vendor'].isin(["Candy Magic", "OLENS", "Fairy", "Shobido"])]
-    elif supplier == "Supplier 4":
-        df = df[df['Vendor'] == "Geo Medical"]
-    elif supplier == "Supplier 5":
-        df = df[df['Vendor'] == "Ann365"]
-    elif supplier == "Supplier 1":
-        df = df[~df['Vendor'].isin(["Seed", "EverColor", "Candy Magic", "OLENS", "Fairy", "Shobido", "Geo Medical", "Ann365"])]
-    else:
-        pass  # No filtering is applied, show all data
-
-    # Display the full DataFrame
-    display_df = df.copy()
-
-    # Select specific columns to include in the output CSV
+    
+    # Define supplier-specific filters
+    vendor_filters = {
+        "Supplier 1": lambda df: df[~df['Vendor'].isin(["Seed", "EverColor", "Candy Magic", "OLENS", "Fairy", "Shobido", "Geo Medical", "Ann365"])],
+        "Supplier 2": lambda df: df[df['Vendor'].isin(["Candy Magic", "OLENS", "Fairy", "Shobido"])],
+        "Supplier 3": lambda df: df[df['Vendor'].isin(["Seed", "EverColor"])],
+        "Supplier 4": lambda df: df[df['Vendor'] == "Geo Medical"],
+        "Supplier 5": lambda df: df[df['Vendor'] == "Ann365"]
+    }
+    
+    # Apply filter based on selected supplier
+    if supplier and supplier in vendor_filters:
+        df = vendor_filters[supplier](df)
+    
+    # Specify columns for output
     columns_to_include = [
-        'Order Number',
-        'Quantity',
-        'Product Title',
-        'Product Option Name',
-        'Product Option Value',
-        'Order Line item Properties 2 Name',
-        'Order Line item Properties 2 Value',
-        'Order Line item Properties 3 Name',
+        'Order Number', 'Quantity', 'Product Title', 'Product Option Name',
+        'Product Option Value', 'Order Line item Properties 2 Name',
+        'Order Line item Properties 2 Value', 'Order Line item Properties 3 Name',
         'Order Line item Properties 3 Value'
     ]
     download_df = df[columns_to_include]
-
-    # Save the filtered DataFrame with selected columns as a CSV file
+    
+    # Save filtered DataFrame as CSV file
     output_file = "filtered_output.csv"
     download_df.to_csv(output_file, index=False)
-    return display_df, output_file
+    return df, output_file
+
+# Define the main block for the interface
+with gr.Blocks() as demo:
+    with gr.Tab("CSV Viewer"):
+        with gr.Row():
+            file_input = gr.File(label="Upload CSV")
+            supplier_input = gr.Dropdown(choices=["", "Supplier 1", "Supplier 2", "Supplier 3", "Supplier 4", "Supplier 5"], label="Select Supplier")
+            load_button = gr.Button("Load Data")
+        with gr.Row():
+            output_df = gr.DataFrame()
+            output_file = gr.File(label="Download Filtered CSV")
+
+        # Bind the function to inputs and outputs using the button
+        load_button.click(fn=display_csv, inputs=[file_input, supplier_input], outputs=[output_df, output_file])
 
-# Create a Gradio interface with dropdown for supplier selection
-interface = gr.Interface(
-    fn=display_csv,
-    inputs=[
-        "file",
-        gr.Dropdown(choices=["", "Supplier 1", "Supplier 2", "Supplier 3", "Supplier 4", "Supplier 5"], label="Select Supplier", value="")
-    ],
-    outputs=["dataframe", "file"],
-    title="CSV Viewer",
-    description="Upload a CSV file, optionally select a supplier for specific Vendor filtering, view the full contents on screen, and download a file with selected columns."
-)
+    # You can add more tabs here with different functions
 
 # Run the interface
-interface.launch()
+demo.launch()