Upload application file

app.py

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+import numpy as np
+import gradio as gr
+from detection import detect_objects
+from config import PASCAL_CLASSES
+
+
+def inference(
+    image: np.ndarray,
+    iou_thresh: float, thresh: float,
+    enable_grad_cam: str,
+    transparency: float,
+):
+    infer_output = detect_objects(image, iou_thresh, thresh, enable_grad_cam, transparency)
+    return infer_output
+
+
+title = "YoloV3 for Pascal VOC Dataset"
+description = f"Pytorch Implementation of YoloV3 model trained on Pascal VOC dataset with GradCAM \n Classes in pascol voc are: {', '.join(PASCAL_CLASSES)}"
+example_images = [
+    ["images/001114.jpg", 0.7, 0.5, True, 0.6],
+    ["images/001133.jpg", 0.6, 0.5, True, 0.6],
+    ["images/001142.jpg", 0.65, 0.45, True, 0.6],
+    ["images/001147.jpg", 0.6, 0.5, True, 0.6],
+    ["images/001155.jpg", 0.7, 0.7, True, 0.6],
+]
+
+demo = gr.Interface(
+    inference,
+    inputs=[
+        gr.Image(label="Input Image"),
+        gr.Slider(0, 1, value=0.5, label="IOU Threshold"),
+        gr.Slider(0, 1, value=0.4, label="Threshold"),
+        gr.Checkbox(label="Show Grad Cam"),
+        gr.Slider(0, 1, value=0.5, label="Opacity of GradCAM"),
+    ],
+    outputs=[
+        gr.Gallery(rows=2, columns=1),
+    ],
+    title=title,
+    description=description,
+    examples=example_images,
+)
+
+demo.launch(debug=True)