Create app.py

app.py

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+from typing import Tuple
+import gradio as gr
+import supervision as sv
+import numpy as np
+from PIL import Image
+from huggingface_hub import hf_hub_download
+from ultralytics import YOLO
+
+# Load the YOLO model from Hugging Face
+model_path = hf_hub_download(
+    repo_id="cultural-heritage/medieval-manuscript-yolov11",
+    filename="medieval-yolov11n.pt"
+)
+# Load the YOLO model from local path
+model = YOLO(model_path)
+
+# Create annotators
+LABEL_ANNOTATOR = sv.LabelAnnotator(text_color=sv.Color.BLACK)
+BOX_ANNOTATOR = sv.BoxAnnotator()
+
+def detect_and_annotate(
+    image: np.ndarray,
+    conf_threshold: float,
+    iou_threshold: float
+) -> np.ndarray:
+    # Perform inference
+    results = model.predict(
+        image,
+        conf=conf_threshold,
+        iou=iou_threshold
+    )[0]
+    
+    # Convert results to supervision Detections
+    boxes = results.boxes.xyxy.cpu().numpy()
+    confidence = results.boxes.conf.cpu().numpy()
+    class_ids = results.boxes.cls.cpu().numpy().astype(int)
+    
+    # Create Detections object
+    detections = sv.Detections(
+        xyxy=boxes,
+        confidence=confidence,
+        class_id=class_ids
+    )
+    
+    # Create labels with confidence scores
+    labels = [
+        f"{results.names[class_id]} ({conf:.2f})"
+        for class_id, conf
+        in zip(class_ids, confidence)
+    ]
+
+    # Annotate image
+    annotated_image = image.copy()
+    annotated_image = BOX_ANNOTATOR.annotate(scene=annotated_image, detections=detections)
+    annotated_image = LABEL_ANNOTATOR.annotate(scene=annotated_image, detections=detections, labels=labels)
+    
+    return annotated_image
+
+# Create Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Medieval Manuscript Detection with YOLO")
+    
+    with gr.Row():
+        with gr.Column():
+            input_image = gr.Image(
+                label="Input Image",
+                type='numpy'
+            )
+            with gr.Accordion("Detection Settings", open=True):
+                with gr.Row():
+                    conf_threshold = gr.Slider(
+                        label="Confidence Threshold",
+                        minimum=0.0,
+                        maximum=1.0,
+                        step=0.05,
+                        value=0.25,
+                    )
+                    iou_threshold = gr.Slider(
+                        label="IoU Threshold",
+                        minimum=0.0,
+                        maximum=1.0,
+                        step=0.05,
+                        value=0.45,
+                        info="Decrease for stricter detection, increase for more overlapping boxes"
+                    )
+            with gr.Row():
+                clear_btn = gr.Button("Clear")
+                detect_btn = gr.Button("Detect", variant="primary")
+                
+        with gr.Column():
+            output_image = gr.Image(
+                label="Detection Result",
+                type='numpy'
+            )
+
+    def process_image(
+        image: np.ndarray,
+        conf_threshold: float,
+        iou_threshold: float
+    ) -> Tuple[np.ndarray, np.ndarray]:
+        if image is None:
+            return None, None
+        annotated_image = detect_and_annotate(image, conf_threshold, iou_threshold)
+        return image, annotated_image
+
+    def clear():
+        return None, None
+
+    # Connect buttons to functions
+    detect_btn.click(
+        process_image,
+        inputs=[input_image, conf_threshold, iou_threshold],
+        outputs=[input_image, output_image]
+    )
+    clear_btn.click(
+        clear,
+        inputs=None,
+        outputs=[input_image, output_image]
+    )
+
+if __name__ == "__main__":
+    demo.launch(debug=True, show_error=True)