Create app.py

app.py

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+
+
+import gradio as gr
+import torch
+from PIL import Image, ImageDraw, ImageFont
+import numpy as np
+import matplotlib.pyplot as plt
+import os
+
+# Download images
+torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg', 'zidane.jpg')
+torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/raw/master/data/images/bus.jpg', 'bus.jpg')
+
+# Load YOLOv5 model
+model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
+
+
+def yolo(im):
+    try:
+        # Check if the input is an Image object
+        if isinstance(im, Image.Image):
+            # Convert the PIL image to a numpy array
+            im_array = np.array(im)
+
+            # Perform inference with YOLOv5
+            results = model(im_array)  # inference
+
+            # Get the bounding boxes and labels
+            boxes = results.xyxy[0].cpu().numpy()
+
+            # Convert the results to a PIL Image
+            output_image = Image.fromarray(im_array)
+
+            # Draw the bounding boxes and labels on the output image
+            draw = ImageDraw.Draw(output_image)
+            font = ImageFont.load_default(45)
+
+            for box in boxes:
+                label = results.names[int(box[5])]
+                draw.rectangle([(box[0], box[1]), (box[2], box[3])], outline="red", width=3)
+                draw.text((box[0], box[1]), label, fill="blue", font=font)
+
+            return output_image
+        else:
+            raise ValueError("The input should be an Image object.")
+    except Exception as e:
+        print(f"Error processing image: {e}")
+        return None
+
+
+# Define Gradio interface
+inputs = gr.Image(type='pil', label="Original Image")
+outputs = gr.Image(type="pil", label="Output Image")
+title = "YOLOv5"
+description = "YOLOv5 Gradio demo for object detection. Upload an image or click an example image to use."
+
+article = "<p style='text-align: center'>YOLOv5 is a family of compound-scaled object detection models trained on the COCO dataset, and includes " \
+          "simple functionality for Test Time Augmentation (TTA), model ensembling, hyperparameter evolution, " \
+          "and export to ONNX, CoreML and TFLite. <a href='https://github.com/ultralytics/yolov5'>Source code</a> |" \
+          "<a href='https://apps.apple.com/app/id1452689527'>iOS App</a> | <a href='https://pytorch.org/hub/ultralytics_yolov5'>PyTorch Hub</a></p>"
+
+examples = [['zidane.jpg'], ['bus.jpg']]
+
+gr.Interface(yolo, inputs, outputs, title=title, description=description, article=article, examples=examples, analytics_enabled=False).launch(debug=True)