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from PIL import Image
from transformers import LayoutLMv3ForTokenClassification, LayoutLMv3Processor
import gradio as gr
import torch
import pytesseract
# Set the Linux path for Tesseract
pytesseract.pytesseract.tesseract_cmd = "/usr/bin/tesseract"
# Load the fine-tuned model and processor from local files
model_path = "./" # Path to the directory containing the uploaded model files
model = LayoutLMv3ForTokenClassification.from_pretrained(model_path)
processor = LayoutLMv3Processor.from_pretrained(model_path, apply_ocr=True)
# Define label mapping
id2label = {0: "company", 1: "date", 2: "address", 3: "total", 4: "other"}
# Define prediction function
def predict_receipt(image):
try:
# 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
except Exception as e:
return {"error": str(e)}
# Create Gradio Interface
interface = gr.Interface(
fn=predict_receipt,
inputs=gr.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()
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