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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):
if not image_url and not file_store_key:
return "❌ Please provide either an image URL or file store key.", [], "", ""
if image_url and file_store_key:
return "❌ Provide only one: image URL or file store key.", [], "", ""
try:
payload = {}
if image_url:
payload["url"] = image_url
if file_store_key:
payload["file_store_key"] = file_store_key
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}", [], "", ""
result = response.json()
if not result.get("success"):
return "❌ Detection failed.", [], "", ""
status = "✅ Detection successful!"
tags = result.get("tags", [])
objects = result.get("objects", [])
description = f"Image Size: {result.get('width')} x {result.get('height')}\n\n"
for obj in objects:
bounds = obj.get("bounds", {})
bound_text = ""
if bounds.get("top_left") and bounds.get("top_right"):
tl = bounds["top_left"]
tr = bounds["top_right"]
bound_text = f"Bounds: ({tl['x']}, {tl['y']}) to ({tr['x']}, {tr['y']})"
description += f"• {obj['name']} (Confidence: {obj['confidence']:.2f})\n {bound_text}\n"
raw_json = json.dumps(result, indent=2)
return status, tags, description.strip(), raw_json
except Exception as e:
return f"❌ Error: {str(e)}", [], "", ""
with gr.Blocks() as demo:
gr.Markdown("""
<div style='text-align: center; margin-bottom: 24px;'>
<h1 style='font-size:2.2em; margin-bottom: 0.2em;'>🧩 Object Detection</h1>
<p style='font-size:1.2em; margin-top: 0;'>Detect objects within images with great accuracy using AI models.</p>
<p style='font-size:1em; margin-top: 0.5em;'>For more details and API usage, see the <a href='https://jigsawstack.com/docs/api-reference/ai/object-detection' target='_blank'>documentation</a>.</p>
</div>
""")
with gr.Row():
with gr.Column():
input_type = gr.Radio(choices=["Image URL", "File Store Key"], value="Image URL", label="Input Type")
image_url = gr.Textbox(label="Image URL", placeholder="https://example.com/image.jpg", visible=True)
file_store_key = gr.Textbox(label="File Store Key", placeholder="my-image.jpg", visible=False)
detect_btn = gr.Button("🔍 Detect Objects")
clear_btn = gr.Button("Clear")
with gr.Column():
status_box = gr.Textbox(label="Status", interactive=False)
tag_display = gr.Label(label="Detected Tags")
desc_display = gr.Textbox(label="Object Details", lines=10, interactive=False)
json_box = gr.Accordion("Raw JSON Response", open=False)
with json_box:
json_output = gr.Textbox(show_label=False, lines=20, interactive=False)
def toggle_inputs(choice):
return (
gr.update(visible=(choice == "Image URL")),
gr.update(visible=(choice == "File Store Key"))
)
input_type.change(fn=toggle_inputs, inputs=input_type, outputs=[image_url, file_store_key])
def on_detect(input_mode, url, key):
if input_mode == "Image URL":
return detect_objects(image_url=url.strip())
else:
return detect_objects(file_store_key=key.strip())
detect_btn.click(fn=on_detect, inputs=[input_type, image_url, file_store_key],
outputs=[status_box, tag_display, desc_display, json_output])
def clear_all():
return "Image URL", "", "", "", "", ""
clear_btn.click(fn=clear_all, inputs=[], outputs=[
input_type, image_url, file_store_key, status_box, desc_display, json_output
])
demo.launch()
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