Upload 2 files

app.py

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+#ONLY yolov5 single input and output
+import gradio as gr
+from ultralyticsplus import YOLO
+#import yolov5
+from PIL import Image
+
+# Load your model
+# model = yolov5.load('MvitHYF/cocoaseedyolov5mvit')
+model_path = "best.pt"
+model = YOLO(model_path)
+model.conf = 0.40
+model.iou = 0.45
+model.agnostic = True
+model.multi_label = False
+model.max_det = 100
+# model.overrides['conf'] = 0.25  # NMS confidence threshold
+# model.overrides['iou'] = 0.45  # NMS IoU threshold
+# model.overrides['agnostic_nms'] = False  # NMS class-agnostic
+# model.overrides['max_det'] = 1000  # maximum number of detections per image
+
+#css = ".output_image {height: 40rem !important; width: 100% !important;}"
+
+def predict(input_image):
+    try:
+        # Perform inference
+        results = model(input_image, size=(1920), augment=True)
+
+        # Convert result image with bounding boxes to PIL format for Gradio output
+        result_image = Image.fromarray(results.render()[0])
+
+        return result_image
+
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+# Set up Gradio interface
+interface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil", label="Upload an Image1"),
+    outputs=gr.Image(type="pil", label="Result1"),
+    #css = css,
+    title="Object Detection using YOLOv5",
+    description="Upload an image to detect objects using the YOLOv5 model"
+)
+interface.launch()

best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:31ebd2c1660df6974903a85db59e44f78b10685299b030f2d991d803782d992e
+size 14645181