Create app.py

app.py

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+"""import gradio as gr
+import torch
+from PIL import Image
+from pathlib import Path
+import numpy as np
+
+# Load YOLO Model
+model = torch.hub.load('ultralytics/yolov5', 'custom', path='https://huggingface.co/ayoubsa/yolo_model/blob/main/best.pt')  # Replace 'model.pt' with your uploaded model's path
+
+# Function to make predictions
+def predict(image):
+    # Convert input to PIL image if it's not
+    if isinstance(image, np.ndarray):
+        image = Image.fromarray(image)
+    
+    # Run inference
+    results = model(image)
+    
+    # Extract predictions (bounding boxes, labels, confidence scores)
+    predictions = results.pandas().xyxy[0]  # Pandas dataframe
+    annotated_image = np.array(results.render()[0])  # Annotated image as NumPy array
+    
+    return annotated_image, predictions[['name', 'confidence']].to_dict(orient="records")
+
+# Create Gradio Interface
+image_input = gr.inputs.Image(type="numpy", label="Input Image")
+output_image = gr.outputs.Image(type="numpy", label="Annotated Image")
+output_text = gr.outputs.JSON(label="Predictions (Labels & Confidence)")
+
+interface = gr.Interface(
+    fn=predict,
+    inputs=image_input,
+    outputs=[output_image, output_text],
+    title="YOLO Object Detection",
+    description="Upload an image to detect objects using YOLO.",
+    examples=["example1.jpg", "example2.jpg", "example3.jpg"]  # Provide paths to example images
+)
+
+interface.launch()"""
+
+
+from datasets import load_dataset
+import gradio as gr
+import random
+import numpy as np
+from PIL import Image
+import torch
+
+# Load the YOLO model
+model = torch.hub.load('ultralytics/yolov5', 'custom', path='model.pt')  # Replace with your uploaded model's path
+
+# Load your dataset from Hugging Face
+dataset = load_dataset("username/your_dataset_name")  # Replace with your dataset's repository name on Hugging Face
+
+# Function to get random examples from the dataset
+def get_random_examples(dataset, num_examples=3):
+    images = dataset['train'][:]['image']  # Assuming the images are in the 'train' split and column 'image'
+    random_examples = random.sample(images, num_examples)
+    return random_examples
+
+# Define the prediction function
+def predict(image):
+    results = model(image)  # Perform object detection using YOLO
+    results.render()  # Render bounding boxes on the image
+    output_image = Image.fromarray(results.imgs[0])  # Convert to PIL image
+    return output_image
+
+# Get examples for Gradio app
+example_images = get_random_examples(dataset, num_examples=3)
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.inputs.Image(type="pil"),  # PIL Image for compatibility with YOLO
+    outputs=gr.outputs.Image(type="pil"),
+    examples=example_images  # Linking examples directly from Hugging Face dataset
+)
+
+# Launch the Gradio app
+iface.launch()
+