Delete app.py

app.py

@@ -1,45 +0,0 @@
-#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 = "/Users/heartties/Desktop/yolo-safety-env/code/v5cocoaseedmvit2024space/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()