File size: 1,553 Bytes
bd746ed 5b07194 bd746ed a9eefb1 5b07194 b874912 5b07194 bd746ed 5b07194 bd746ed 5b07194 bd746ed 5b07194 bd746ed 5b07194 bd746ed 5b07194 bd746ed 5b07194 bd746ed 5b07194 bd746ed 5b07194 bd746ed b443de4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 |
import gradio as gr
from transformers import LayoutLMv3ForTokenClassification, LayoutLMv3Processor
from PIL import Image
import torch
# Load the fine-tuned model and processor
model_path = "quadranttechnologies/Receipt_Image_Analyzer"
model = LayoutLMv3ForTokenClassification.from_pretrained(model_path)
processor = LayoutLMv3Processor.from_pretrained(model_path)
# Define label mapping
id2label = {0: "company", 1: "date", 2: "address", 3: "total", 4: "other"}
# Define prediction function
def predict_receipt(image):
# Preprocess the image
encoding = processor(image, return_tensors="pt", truncation=True, padding="max_length", max_length=512)
input_ids = encoding["input_ids"]
attention_mask = encoding["attention_mask"]
bbox = encoding["bbox"]
pixel_values = encoding["pixel_values"]
# Get model predictions
outputs = model(input_ids=input_ids, attention_mask=attention_mask, bbox=bbox, pixel_values=pixel_values)
predictions = outputs.logits.argmax(-1).squeeze().tolist()
# Map predictions to labels
labeled_output = {id2label[pred]: idx for idx, pred in enumerate(predictions) if pred != 4}
return labeled_output
# Create Gradio Interface
interface = gr.Interface(
fn=predict_receipt,
inputs=gr.inputs.Image(type="pil"),
outputs="json",
title="Receipt Information Analyzer",
description="Upload a scanned receipt image to extract information like company name, date, address, and total."
)
# Launch the interface
if __name__ == "__main__":
interface.launch()
|