Update app.py

app.py

@@ -1,60 +1,39 @@
-import gradio as gr
 import torch
-from ultralytics import YOLO
-
-
-# Images
-torch.hub.download_url_to_file('https://github.com/Michael-OvO/Burn-Detection-Classification/blob/main/inference/images/1st_degree_1.jpg', '1st_degree_1.jpg')
-torch.hub.download_url_to_file('https://github.com/Michael-OvO/Burn-Detection-Classification/blob/main/inference/images/3rd_degree_1.jpg', '3rd_degree_1.jpg')
-    
-def yolov7_inference(
-    image: gr.inputs.Image = None,
-    model_path: gr.inputs.Dropdown = None,
-    image_size: gr.inputs.Slider = 640,
-    conf_threshold: gr.inputs.Slider = 0.25,
-    iou_threshold: gr.inputs.Slider = 0.45,
-):
-    """
-    YOLOv7 inference function
-    Args:
-        image: Input image
-        model_path: Path to the model
-        image_size: Image size
-        conf_threshold: Confidence threshold
-        iou_threshold: IOU threshold
-    Returns:
-        Rendered image
-    """
-    model = YOLO('skin_burn.pt')
-    model.conf = conf_threshold
-    model.iou = iou_threshold
-    results = model([image], size=image_size)
-    return results.render()[0]
-        
-
-inputs = [
-    gr.inputs.Image(type="pil", label="Input Image"),
-    gr.inputs.Dropdown(
-        choices=[
-            "skin_burn.pt",
-            "Other"
-        ],
-        default="skin_burn",
-        label="Model",
-    ),
-    gr.inputs.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size"),
-    gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.25, step=0.05, label="Confidence Threshold"),
-    gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold"),
-]
-
-outputs = gr.outputs.Image(type="filepath", label="Output Image")
-title = "Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors"
-
-demo_app = gr.Interface(
-    fn=yolov7_inference,
-    inputs=inputs,
+import gradio as gr
+from huggingface_hub import hf_hub_download
+from PIL import Image
+
+REPO_ID = "Michael/YOLOv7"
+FILENAME = "skin_burn.pt"
+
+
+yolov7_custom_weights = hf_hub_download(repo_id=REPO_ID, filename=FILENAME,repo_type='space')
+
+model = torch.hub.load('WongKinYiu/yolov7:main',model='custom', path_or_model=yolov7_custom_weights, force_reload=True)
+def object_detection(im, size=416):
+    results = model(im) 
+    results.render()  
+    return Image.fromarray(results.imgs[0])
+
+title = "Yolov7 Custom"
+
+image = gr.inputs.Image(shape=(614,614), image_mode="RGB", source="upload", label="Upload Image", optional=False)
+outputs = gr.outputs.Image(type="pil", label="Output Image")
+
+Custom_description="Custom Training Performed on colab style='text-decoration: underline' target='_blank'>Link</a> </center><br> <center>Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors </center> <br> <b>1st</b> class is for Person Detected<br><b>2nd</b> class is for Car Detected"
+
+Footer = (
+    "MOdel train on our custome dataset")
+
+
+Top_Title="<center>Yolov7 🚀 Custom Trained style='text-decoration: underline' target='_blank'></center></a>Face with mask and face without mask Detection"
+css = ".output-image, .input-image {height: 50rem !important; width: 100% !important;}"
+css = ".image-preview {height: auto !important;}"
+
+gr.Interface(
+    fn=object_detection,
+    inputs=image,
     outputs=outputs,
-    title=title,
-    theme='huggingface',
-)
-demo_app.launch(debug=True, enable_queue=True)
+    title=Top_Title,
+    description=Custom_description,
+    article=Footer,).launch()