Update app.py

app.py

@@ -1,7 +1,10 @@
 import gradio as gr
-from transformers import AutoImageProcessor, AutoModelForObjectDetection
+from huggingface_hub import hf_hub_download
+from PIL import Image
 import torch
+from transformers import AutoImageProcessor, AutoModelForObjectDetection
 
+gr.load("models/microsoft/table-transformer-structure-recognition").launch()
 # Load the processor and model for table structure recognition
 processor = AutoImageProcessor.from_pretrained("microsoft/table-transformer-structure-recognition")
 model = AutoModelForObjectDetection.from_pretrained("microsoft/table-transformer-structure-recognition")
@@ -19,12 +22,8 @@ def predict(image):
     predicted_boxes = outputs.pred_boxes[0].cpu().numpy()  # First image
     predicted_classes = outputs.logits.argmax(-1).cpu().numpy()  # Class predictions
 
-    # Filter predictions to only include columns
-    column_class_id = 1  # Assuming class ID 1 corresponds to columns, adjust if needed
-    column_boxes = predicted_boxes[predicted_classes == column_class_id]
-    
-    # Return the bounding boxes for columns
-    return {"boxes": column_boxes.tolist(), "classes": ["column"] * len(column_boxes)}
+    # Return the bounding boxes for display
+    return {"boxes": predicted_boxes.tolist(), "classes": predicted_classes.tolist()}
 
 # Set up the Gradio interface
 interface = gr.Interface(