Update app.py

app.py

@@ -1,28 +1,50 @@
 import gradio as gr
 import torch
-from PIL import Image
-import os 
-
-
-# Model
-model = torch.hub.load('StarAtNyte1/yolov7_custom', 'best')  # force_reload=True to update
-
-def yolo(im):
-    g = (size / max(im.size))  # gain
-    im = im.resize((int(x * g) for x in im.size), Image.ANTIALIAS)  # resize
-
-    results = model(im)  # inference
-    results.render()  # updates results.imgs with boxes and labels
-    results.save()
-    os.system("ls")
-    return "out.png"
-
-
-inputs = gr.inputs.Image(type='pil', label="Original Image")
-outputs = gr.outputs.Image(type="file", label="Output Image")
-
-title = "YOLOv7"
-description = "YOLOv7 Gradio demo for object detection. Upload an image or click an example image to use."
-
-examples = [['zidane.jpg'], ['bus.jpg']]
-gr.Interface(yolo, inputs, outputs, title=title, description=description, article=article, examples=examples, theme="huggingface").launch(cache_examples=True,enable_queue=True)
+import yolov7
+
+
+# Images
+torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg', 'zidane.jpg')
+torch.hub.download_url_to_file('https://raw.githubusercontent.com/obss/sahi/main/tests/data/small-vehicles1.jpeg', 'small-vehicles1.jpeg')
+    
+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,
+):
+
+    model = yolov7.load(model_path, device="cpu", hf_model=True, trace=False)
+    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=[
+            "StarAtNyte1/yolov7_custom",
+
+        ],
+        default="StarAtNyte1/yolov7_custom",
+        label="Model",
+    ),
+]
+
+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"
+
+examples = [['small-vehicles1.jpeg', 'kadirnar/yolov7-tiny-v0.1', 640, 0.25, 0.45], ['zidane.jpg', 'kadirnar/yolov7-v0.1', 640, 0.25, 0.45]]
+demo_app = gr.Interface(
+    fn=yolov7_inference,
+    inputs=inputs,
+    outputs=outputs,
+    title=title,
+    examples=examples,
+    cache_examples=True,
+    theme='huggingface',
+)
+demo_app.launch(debug=True, enable_queue=True)