Upload app.py

app.py

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+# -*- coding: utf-8 -*-
+"""app.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1WeNkl1pYnT0qeOTsUFooLFLJ1arRHC00
+"""
+
+# %pip install ultralytics -q
+# %pip install gradio -q
+
+import cv2
+import os
+import PIL.Image as Image
+import gradio as gr
+from huggingface_hub import hf_hub_download
+from ultralytics import ASSETS, YOLO
+
+# load trained model
+model = YOLO("best.pt")
+
+def predict_image(img, conf_threshold, iou_threshold):
+    results = model.predict(
+        source=img,
+        conf=conf_threshold,
+        iou=iou_threshold,
+        show_labels=True,
+        show_conf=True,
+        imgsz=640,
+    )
+
+    for r in results:
+        im_array = r.plot()
+        im = Image.fromarray(im_array[..., ::-1])
+
+    return im
+
+
+iface = gr.Interface(
+    fn=predict_image,
+    inputs=[
+        gr.Image(type="pil", label="Upload Image"),
+        gr.Slider(minimum=0, maximum=1, value=0.25, label="Confidence threshold"),
+        gr.Slider(minimum=0, maximum=1, value=0.45, label="IoU threshold")
+    ],
+    outputs=gr.Image(type="pil", label="Result"),
+    title="Fire Detecttion using YOLOv8n on Gradio",
+    description="Upload images for inference. The Ultralytics YOLOv8n trained model is used for inference.",
+    examples=[
+        [ASSETS / "bus.jpg", 0.25, 0.45],
+        [ASSETS / "zidane.jpg", 0.25, 0.45],
+    ]
+)
+
+if __name__ == '__main__':
+    iface.launch()