Create app.py

app.py

@@ -0,0 +1,38 @@
+import torch
+import gradio as gr
+from huggingface_hub import hf_hub_download
+from PIL import Image
+
+REPO_ID = "rgp/yolov5-street-view-detection-grayscale"
+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.ims[0])
+
+title = "Pedestrians and Transportations detection on the streets - GrayScale"
+description = """This model is a small demo based on an analysis of 680 images - GrayScale.
+"""
+css = ".output-image, .input-image, .image-preview {height: 640px !important}"
+
+input = gr.inputs.Image(shape=(640, 640), image_mode="RGB", source="upload", label="Imagem", optional=False)
+output = gr.outputs.Image(type="pil", label="Output Image")
+examples = [["sample_images/image-2.jpeg"], ["sample_images/image-1.jpg"], ["sample_images/image-3.jpg"], ["sample_images/image-4.jpg"], ["sample_images/image-5.jpg"]]
+
+gr.Interface(
+    fn=object_detection,
+    inputs=input,
+    outputs=output,
+    title=title,
+    description=description,
+    examples=examples,
+    css=css
+).launch(debug=True)