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# app.py | |
import gradio as gr | |
import pandas as pd # Import pandas | |
from ocr_request import ocr_request | |
import io | |
def process_file(files): | |
response_arr = [] | |
# Send the uploaded file to the function from ocr_request.py | |
for file in files: | |
response = ocr_request(file.name) | |
response_arr.append(response) | |
print("Main file :", response_arr) | |
#i= [[{'invoice_number': '349136', 'product_description': '1ST FLOOR WALLS', 'predicted_material': 'Framing', 'confidence': 0.8}, {'invoice_number': '349136', 'product_description': "11.875 X 16 ' Pro Lam 2.0 LVL 1.75 ( 7 @ 16 ' , 4 @\n8 ' )", 'predicted_material': 'Framing', 'confidence': 0.9}, {'invoice_number': '349136', 'product_description': "COLUMN\n11.875 X 10 ' Pro Lam 2.0 LVL 1.75", 'predicted_material': 'Framing', 'confidence': 0.9}, {'invoice_number': '3495565136', 'product_description': "Power Column 3 1/2 X 5 1/2 - 08 '", 'predicted_material': 'Framing', 'confidence': 0.9}],[{'invoice_number': '349136', 'product_description': ' FLOOR WALLS', 'predicted_material': 'Framing', 'confidence': 0.8}, {'invoice_number': '349136', 'product_description': "11.875 X 16 ' Pro Lam 2.0 LVL 1.75 ( 7 @ 16 ' , 4 @\n8 ' )", 'predicted_material': 'Framing', 'confidence': 0.9}, {'invoice_number': '349136', 'product_description': "COLUMN\n11.875 X 10 ' Pro Lam 2.0 LVL 1.75", 'predicted_material': 'Framing', 'confidence': 0.9}, {'invoice_number': '349136', 'product_description': "Power Column 3 1/2 X 5 1/2 - 08 '", 'predicted_material': 'Framing', 'confidence': 0.9}]] | |
flat_list = [] | |
for item in response_arr: | |
invoice_number = item['invoice_number'] | |
# Extracting product descriptions | |
products = item.get('predictions', []) or item.get('product_description', []) | |
for product in products: | |
# Rename 'description' key to 'product_description' for uniformity across all products | |
product_description = product.get('product_description', product.get('description')) | |
predicted_material = product['predicted_material'] | |
confidence = product['confidence'] | |
flat_list.append({ | |
'invoice_number': invoice_number, | |
'product_description': product_description, | |
'predicted_material': predicted_material, | |
'confidence': confidence | |
}) | |
df = pd.DataFrame(flat_list) | |
print("Df final : ", df) | |
# Save the dataframe to a CSV in-memory | |
result_csv = df.to_csv(index=False) | |
csv_filename = "categories.csv" | |
with open(csv_filename, "w") as f: | |
f.write(result_csv) | |
return df,csv_filename # Gradio will display this as a table | |
interface = gr.Interface(fn=process_file, | |
inputs=gr.inputs.File(label="Upload a File", file_count='multiple'), | |
outputs=["dataframe",gr.outputs.File(label="Download CSV")]) # Specify "dataframe" as output type | |
interface.launch(share=True) | |