Create app.py

app.py

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+import gradio as gr
+from PIL import Image
+from ultralytics import YOLO
+import requests
+import json
+
+# Load the pretrained YOLO model
+model = YOLO("Single_Object_BB_Detection_v1.pt")
+
+
+Api = "myapi"                    # API endpoint
+
+def detect_objects(images):
+    results = model(images, max_det=1)
+    all_bboxes = []
+    all_bboxes2 = []
+    for result in results:
+        boxes = result.boxes.xywhn.tolist()
+        boxes2 = result.boxes.xywh.tolist()
+        all_bboxes.append(boxes)
+        all_bboxes2.append(boxes2)
+    return all_bboxes, all_bboxes2
+
+
+def create_solutions(image_urls, all_bboxes, all_bboxes2):
+    solutions = []
+    img_id = 1
+    box_id = 1
+    category_id = 1
+    for image_url, bboxes, bboxes2 in zip(image_urls, all_bboxes, all_bboxes2):
+        for box, box2 in zip(bboxes, bboxes2):
+            if isinstance(box2[0], list):
+                w = box2[0][2]
+                h = box2[0][3]
+            else:
+                w = box2[2]
+                h = box2[3]
+            
+            area = w * h
+            seg = [[]]
+
+            ans = {
+                "segmentation": seg,
+                "area": area,
+                "iscrowd": 0,
+                "image_id": img_id,
+                "bbox": box,
+                "category_id": category_id,
+                "id": box_id
+            }
+            obj = {"url": image_url, "answer": [ans]}
+            solutions.append(obj)
+
+            box_id += 1
+        img_id += 1
+    return solutions
+
+
+def process_images(params):
+    image_urls = params.get("image_urls", [])
+    api = params.get("api", "")
+    job_id = params.get("job_id", "")
+
+    images = [Image.open(requests.get(url, stream=True).raw) for url in image_urls]  # images from URLs
+
+    all_bboxes, all_bboxes2 = detect_objects(images)       # Perform object detection
+    solutions = create_solutions(image_urls, all_bboxes, all_bboxes2)             # Createing solutions with image URLs and bounding boxes
+
+    result_url = Api + f"{api}/{job_id}"
+    # send_results_to_api(solutions, result_url)
+
+    return json.dumps({"result_url": result_url, "solutions": solutions}, indent=4)
+
+# def send_results_to_api(data, result_url):  
+#     response = requests.post(result_url, json=data)
+#     if response.status_code == 200:
+#         return response.json()  
+#     else:
+#         return {"error": "Failed to send results to API"}
+
+#interface
+chatbox = gr.Textbox(default="Enter parameters here...", label="Parameters (JSON format)")
+outputs = gr.JSON()
+
+application = gr.Interface(fn=process_images, inputs=chatbox, outputs=outputs, title="Image Detection with API Integration")
+application.launch()