Create app.py

app.py

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+import torch
+import gradio as gr
+from huggingface_hub import hf_hub_download
+from PIL import Image
+
+REPO_ID = "jgba-school/Modelos-Preditivos-Conexionistas-2022.01/tree/main/models"
+FILENAME = "best.pt"
+
+yolov5_weights = hf_hub_download(repo_id=REPO_ID, filename=FILENAME)
+
+model = torch.hub.load('ultralytics/yolov5', 'custom', path=yolov5_weights, force_reload=True)  # local repo
+
+def object_detection(im, size=640):
+    results = model(im)  # inference
+    #results.print()  # print results to screen
+    #results.show()  # display results
+    #results.save()  # save as results1.jpg, results2.jpg... etc.
+    results.render()  # updates results.imgs with boxes and labels
+    return Image.fromarray(results.imgs[0])
+
+title = "Kart Plates Localizer"
+description = """This model is a small demo based in a 305 images analysis. For best results, more examples are necessary.
+"""
+
+image = gr.inputs.Image(shape=(640, 640), image_mode="RGB", source="upload", label="Imagem", optional=False)
+outputs = gr.outputs.Image(type="pil", label="Output Image")
+
+gr.Interface(
+    fn=object_detection,
+    inputs=image,
+    outputs=outputs,
+    title=title,
+    description=description,
+    examples=[["sample_images/IMG_0125.JPG"], ["sample_images/IMG_0129.JPG"], 
+              ["sample_images/IMG_0157.JPG"], ["sample_images/IMG_0158.JPG"]],
+).launch()