hhh

app.py

@@ -1,86 +1,7 @@
 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")
+def hello(name):
+    return "hi"+name
 
-
-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(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()
+application = gr.Interface(fn=hello, inputs="textbox", outputs="text", title="Image Detection with API Integration")
+application.launch()