Fix compatibility with latest Gradio and Pillow versions

- Update to Gradio 6.1.0 (newer SDK version)
- Update Gradio API: gr.inputs.Image gr.Image and gr.outputs.Image gr.Image
- Replace deprecated PIL method: Image.ANTIALIAS Image.LANCZOS
- Add ultralytics package dependency for YOLOv5 model support

README.md

@@ -4,10 +4,10 @@ emoji: 🔥
 colorFrom: purple
 colorTo: pink
 sdk: gradio
-sdk_version: 3.12.0
+sdk_version: 6.1.0
 app_file: app.py
 pinned: false
 license: apache-2.0
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -11,14 +11,14 @@ model = torch.hub.load('ultralytics/yolov5', 'custom', 'best.pt')  # force_reloa
 
 def yolo(im, size=640):
     g = (size / max(im.size))  # gain
-    im = im.resize((int(x * g) for x in im.size), Image.ANTIALIAS)  # resize
+    im = im.resize((int(x * g) for x in im.size), Image.LANCZOS)  # resize
     results = model(im)  # inference
     results.render()  # updates results.ims with boxes and labels
     return Image.fromarray(results.ims[0])
 
 
-inputs = gr.inputs.Image(type='pil', label="Original Image")
-outputs = gr.outputs.Image(type="pil", label="Output Image")
+inputs = gr.Image(type='pil', label="Original Image")
+outputs = gr.Image(type="pil", label="Output Image")
 
 title = "YOLOv5"
 description = "YOLOv5 demo for fire detection. Upload an image or click an example image to use."

requirements.txt

@@ -1,20 +1,19 @@
 gradio
 pandas
-
-# YOLOv5 requirements
-matplotlib>=3.2.2
-numpy>=1.18.5
-opencv-python>=4.1.1
-Pillow>=7.1.2
-PyYAML>=5.3.1
-requests>=2.23.0
-scipy>=1.4.1
-torch>=1.7.0
-torchvision>=0.8.1
-tqdm>=4.64.0
-seaborn>=0.11.0
+ultralytics
+matplotlib
+numpy
+opencv-python
+Pillow
+PyYAML
+requests
+scipy
+torch
+torchvision
+tqdm
+seaborn
 
 # Extras --------------------------------------
 ipython  # interactive notebook
 psutil  # system utilization
-thop>=0.1.1  # FLOPs computation
+thop  # FLOPs computation