import gradio as gr import requests import json import os BASE_URL = "https://api.jigsawstack.com/v1" headers = { "x-api-key": os.getenv("JIGSAWSTACK_API_KEY") } def detect_objects(image_url=None, file_store_key=None, prompts=None, features=None): if not image_url and not file_store_key: return "❌ Please provide either an image URL or file store key.", "", "", None if image_url and file_store_key: return "❌ Provide only one: image URL or file store key.", "", "", None try: payload = {} if image_url: payload["url"] = image_url if file_store_key: payload["file_store_key"] = file_store_key # Add optional parameters if prompts: payload["prompts"] = prompts if features: payload["features"] = features # Always return annotated image payload["annotated_image"] = True # Always use url as return_type payload["return_type"] = "url" response = requests.post(f"{BASE_URL}/ai/object_detection", headers=headers, json=payload) if response.status_code != 200: return f"❌ Error: {response.status_code} - {response.text}", "", "", None result = response.json() if not result.get("success"): return "❌ Detection failed.", "", "", None status = "✅ Detection successful!" objects = result.get("objects", []) annotated_image_url = result.get("annotated_image") # Create description with object details description = f"Image Size: {result.get('width', 'Unknown')} x {result.get('height', 'Unknown')}\n\n" description += f"Total Objects Detected: {len(objects)}\n\n" for i, obj in enumerate(objects): bounds = obj.get("bounds", {}) label = obj.get("label", "Unknown") bound_text = "" if bounds: width = bounds.get("width", "Unknown") height = bounds.get("height", "Unknown") top_left = bounds.get("top_left", {}) if top_left: x, y = top_left.get("x", "?"), top_left.get("y", "?") bound_text = f"Position: ({x}, {y}), Size: {width}x{height}" description += f"• {label}\n {bound_text}\n" raw_json = json.dumps(result, indent=2) return status, description.strip(), raw_json, annotated_image_url except Exception as e: return f"❌ Error: {str(e)}", "", "", None with gr.Blocks() as demo: gr.Markdown("""
Detect objects within images with great accuracy using AI models.
For more details and API usage, see the documentation.