revertall

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")
@@ -18,13 +21,8 @@ def predict(image):
     # Extract bounding boxes and class labels
     predicted_boxes = outputs.pred_boxes[0].cpu().numpy()  # First image
     predicted_classes = outputs.logits.argmax(-1).cpu().numpy()  # Class predictions
-
-    # Log the relevant information (class IDs and bounding boxes)
-    print("Predicted Classes (IDs):", predicted_classes)
-    print("Bounding Boxes (x1, y1, x2, y2):", predicted_boxes)
-
-    # Return the bounding boxes and class IDs for display in JSON
-    return {"predicted_boxes": predicted_boxes.tolist(), "predicted_classes": predicted_classes.tolist()}
+    # Return the bounding boxes for display
+    return {"boxes": predicted_boxes.tolist(), "classes": predicted_classes.tolist()}
 
 # Set up the Gradio interface
 interface = gr.Interface(