Update app.py

app.py

@@ -1,26 +1,44 @@
-### app.py
-import torch
-import requests
-from PIL import Image
-from torchvision import transforms
 import gradio as gr
-model = torch.hub.load('https://huggingface.co/spaces/gpbhupinder/test/blob/main/model_-%2023%20june%202024%2019_22.pt', pretrained=True).eval()
+import PIL.Image as Image
 
+from ultralytics import ASSETS, YOLO
 
-# Download human-readable labels for ImageNet.
-response = requests.get("https://git.io/JJkYN")
-labels = response.text.split("\n")
+model = YOLO("yolov8n.pt")
 
-def predict(inp):
-  inp = transforms.ToTensor()(inp).unsqueeze(0)
-  with torch.no_grad():
-    prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)
-    confidences = {labels[i]: float(prediction[i]) for i in range(1000)}
-  return confidences
 
+def predict_image(img, conf_threshold, iou_threshold):
+    """Predicts objects in an image using a YOLOv8 model with adjustable confidence and IOU thresholds."""
+    results = model.predict(
+        source=img,
+        conf=conf_threshold,
+        iou=iou_threshold,
+        show_labels=True,
+        show_conf=True,
+        imgsz=640,
+    )
 
+    for r in results:
+        im_array = r.plot()
+        im = Image.fromarray(im_array[..., ::-1])
 
-gr.Interface(fn=predict,
-             inputs=gr.Image(type="pil"),
-             outputs=gr.Label(num_top_classes=3),
-             examples=["lion.jpg", "cheetah.jpg"]).launch()
+    return im
+
+
+iface = gr.Interface(
+    fn=predict_image,
+    inputs=[
+        gr.Image(type="pil", label="Upload Image"),
+        gr.Slider(minimum=0, maximum=1, value=0.25, label="Confidence threshold"),
+        gr.Slider(minimum=0, maximum=1, value=0.45, label="IoU threshold"),
+    ],
+    outputs=gr.Image(type="pil", label="Result"),
+    title="Ultralytics Gradio",
+    description="Upload images for inference. The Ultralytics YOLOv8n model is used by default.",
+    examples=[
+        [ASSETS / "bus.jpg", 0.25, 0.45],
+        [ASSETS / "zidane.jpg", 0.25, 0.45],
+    ],
+)
+
+if __name__ == "__main__":
+    iface.launch()