FirstInterfacePush

app.py

@@ -1,3 +1,35 @@
 import gradio as gr
+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")
+
+# Define the inference function
+def predict(image):
+    # Preprocess the input image
+    inputs = processor(images=image, return_tensors="pt")
+    
+    # Perform object detection using the model
+    with torch.no_grad():
+        outputs = model(**inputs)
+
+    # 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
+
+    # Return the bounding boxes for display
+    return {"boxes": predicted_boxes.tolist(), "classes": predicted_classes.tolist()}
+
+# Set up the Gradio interface
+interface = gr.Interface(
+    fn=predict,  # The function that gets called when an image is uploaded
+    inputs=gr.Image(type="pil"),  # Image input (as PIL image)
+    outputs="json",  # Outputting a JSON with the boxes and classes
+)
+
+# Launch the Gradio app
+interface.launch()