Delete app.py

app.py

@@ -1,55 +0,0 @@
-import gradio as gr
-import torch
-from transformers import LayoutLMv3Processor, LayoutLMv3ForTokenClassification
-
-# Load the model and processor
-processor = LayoutLMv3Processor.from_pretrained("quadranttechnologies/Table_OCR")
-model = LayoutLMv3ForTokenClassification.from_pretrained("quadranttechnologies/Table_OCR")
-model.eval()
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-model.to(device)
-
-def process_image(image):
-    # Preprocess the image using the processor
-    encoding = processor(image, return_tensors="pt", truncation=True, padding="max_length", max_length=512)
-
-    # Move inputs to the same device as the model
-    encoding = {key: val.to(device) for key, val in encoding.items()}
-
-    # Perform inference
-    with torch.no_grad():
-        outputs = model(**encoding)
-        predictions = torch.argmax(outputs.logits, dim=-1)
-
-    # Extract input IDs, bounding boxes, and predicted labels
-    words = encoding["input_ids"]
-    bboxes = encoding["bbox"]
-    labels = predictions.squeeze().tolist()
-
-    # Format output as JSON
-    structured_output = []
-    for word_id, bbox, label in zip(words.squeeze().tolist(), bboxes.squeeze().tolist(), labels):
-        # Decode the word ID to text
-        word = processor.tokenizer.decode([word_id]).strip()
-        if word:  # Avoid adding empty words
-            structured_output.append({
-                "word": word,
-                "bounding_box": bbox,
-                "label": model.config.id2label[label]  # Convert label ID to label name
-            })
-
-    return structured_output
-
-# Define the Gradio interface
-interface = gr.Interface(
-    fn=process_image,
-    inputs=gr.Image(type="pil"),  # Accepts image input
-    outputs="json"  # Outputs JSON structure
-)
-
-# Launch the app
-if __name__ == "__main__":
-    interface.launch(share=False)
-
-
-