Create app.py

app.py

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+from PIL import Image
+from transformers import LayoutLMv3ForTokenClassification, LayoutLMv3Processor
+import gradio as gr
+import torch
+
+# 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)
+
+# 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()