Update app.py

app.py

@@ -1,6 +1,6 @@
-import gradio as gr
-from transformers import LayoutLMv3ForTokenClassification, LayoutLMv3Processor
 from PIL import Image
+from transformers import LayoutLMv3ForTokenClassification, LayoutLMv3Processor
+import gradio as gr
 import torch
 
 # Load the fine-tuned model and processor
@@ -13,26 +13,29 @@ 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"]
+    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()
+        # 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}
+        # Map predictions to labels
+        labeled_output = {id2label[pred]: idx for idx, pred in enumerate(predictions) if pred != 4}
 
-    return labeled_output
+        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"),  # Use `gr.Image` instead of `gr.inputs.Image`
+    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."
@@ -48,3 +51,4 @@ if __name__ == "__main__":
 
 
 
+