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| import random | |
| import requests | |
| from PIL import Image, ImageDraw, ImageFont | |
| import gradio as gr | |
| import torch | |
| from transformers import pipeline | |
| # Load object detection pipeline | |
| finder = pipeline( | |
| "object-detection", | |
| model="facebook/detr-resnet-50", | |
| torch_dtype=torch.bfloat16 | |
| ) | |
| # Generate a random bright color | |
| def random_color(): | |
| return tuple(random.randint(50, 255) for _ in range(3)) | |
| # Main detection function | |
| def Searchy(input_type, url, file, threshold): | |
| # Load image based on choice | |
| if input_type == "Image URL": | |
| image = Image.open(requests.get(url, stream=True).raw).convert("RGB") | |
| else: | |
| image = Image.open(file).convert("RGB") | |
| # Run object detection | |
| output = finder(image, return_tensors="pt") | |
| # Draw bounding boxes | |
| draw = ImageDraw.Draw(image) | |
| for item in output: | |
| if item['score'] < threshold: | |
| continue | |
| label = item['label'] | |
| score = item['score'] | |
| box = item['box'] | |
| xmin, ymin, xmax, ymax = box['xmin'], box['ymin'], box['xmax'], box['ymax'] | |
| # Random bright color for each object | |
| color = random_color() | |
| # Draw rectangle | |
| draw.rectangle([(xmin, ymin), (xmax, ymax)], outline=color, width=1) | |
| # Dynamic font size | |
| font_size = max(5, int(image.width * 0.025)) | |
| try: | |
| font = ImageFont.truetype("arial.ttf", font_size) | |
| except: | |
| font = ImageFont.load_default() | |
| # Label text | |
| text = f"{label} {score:.2f}" | |
| # Measure text size | |
| bbox = draw.textbbox((0, 0), text, font=font) | |
| text_width = bbox[2] - bbox[0] | |
| text_height = bbox[3] - bbox[1] | |
| # Background rectangle for text | |
| draw.rectangle( | |
| [(xmin, ymin - text_height - 6), (xmin + text_width + 6, ymin)], | |
| fill=color | |
| ) | |
| # Text in contrasting color | |
| draw.text((xmin + 3, ymin - text_height - 3), text, fill="black", font=font) | |
| return image | |
| # Toggle visibility of inputs | |
| def toggle_inputs(choice): | |
| if choice == "Image URL": | |
| return gr.update(visible=True), gr.update(visible=False) | |
| else: | |
| return gr.update(visible=False), gr.update(visible=True) | |
| # Theme | |
| theme = gr.themes.Soft( | |
| primary_hue="orange", | |
| secondary_hue="blue", | |
| neutral_hue="slate" | |
| ).set( | |
| button_primary_background_fill="linear-gradient(90deg, #ff7e5f, #feb47b)", | |
| button_primary_text_color="white", | |
| block_background_fill="#f9f9f9", | |
| block_border_color="#ddd" | |
| ) | |
| # Build Gradio UI | |
| with gr.Blocks(theme=theme, css=open("style.css").read()) as demo: | |
| gr.Markdown("## 🖼️ Object Finder — Choose URL or Upload", elem_id="header") | |
| with gr.Row(): | |
| with gr.Column(scale=1, elem_id="input_panel"): | |
| input_type = gr.Radio( | |
| ["Image URL", "Upload Image"], | |
| value="Image URL", | |
| label="Select Input Type" | |
| ) | |
| url_input = gr.Textbox( | |
| label="Enter Image URL", | |
| placeholder="https://example.com/image.jpg", | |
| visible=True | |
| ) | |
| file_input = gr.File( | |
| label="Upload Image", | |
| type="filepath", | |
| visible=False | |
| ) | |
| threshold_slider = gr.Slider( | |
| minimum=0.0, maximum=1.0, value=0.5, step=0.01, | |
| label="Confidence Threshold" | |
| ) | |
| submit_btn = gr.Button("🔍 Detect Objects", elem_id="detect_btn") | |
| with gr.Column(scale=2): | |
| output_img = gr.Image( | |
| type="pil", | |
| label="Image with Detected Objects", | |
| image_mode="RGB", | |
| height=500, | |
| elem_id="output_image", | |
| show_download_button=True | |
| ) | |
| # Events | |
| input_type.change(toggle_inputs, inputs=input_type, outputs=[url_input, file_input]) | |
| submit_btn.click(Searchy, inputs=[input_type, url_input, file_input, threshold_slider], outputs=output_img) | |
| demo.launch() | |