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| import gradio as gr | |
| from PIL import Image | |
| from ultralytics import YOLO | |
| import requests | |
| import json | |
| import logging | |
| logging.basicConfig(level=logging.INFO) | |
| model = YOLO("Vehicles_Classify_v1.pt") | |
| def detect_objects(images): | |
| results = model(images) | |
| # classes={ 0:"family sedan", 1:"bus", 2:"fire engine",3:"heavy truck", 4:"jeep", 5:"mini bus", 6:"racing car", 7:"SUV", 8:"taxi", 9:"truck" } | |
| classes={ 0:"SUV", 1:"bus", 2:"family sedan", 3:"fire engine",4:"heavy truck", 5:"jeep", 6:"mini bus", 7:"racing car", 8:"taxi", 9:"truck" } | |
| names=[] | |
| for result in results: | |
| probs = result.probs.top1 | |
| names.append(classes[probs]) | |
| return names | |
| def create_solutions(image_urls, names, file_ids): | |
| solutions = [] | |
| for image_url, prediction, file_id in zip(image_urls, names, file_ids): | |
| prediction_list=[] | |
| prediction_list.append(prediction) | |
| obj = {"image": image_url, "answer": prediction_list, "qcUser" : None, "normalfileID" : file_id } | |
| solutions.append(obj) | |
| return solutions | |
| # def send_results_to_api(data, result_url): | |
| # # Example function to send results to an API | |
| # headers = {"Content-Type": "application/json"} | |
| # response = requests.post(result_url, json=data, headers=headers) | |
| # if response.status_code == 200: | |
| # return response.json() # Return any response from the API if needed | |
| # else: | |
| # return {"error": f"Failed to send results to API: {response.status_code}"} | |
| def process_images(params): | |
| try: | |
| params = json.loads(params) | |
| except json.JSONDecodeError as e: | |
| logging.error(f"Invalid JSON input: {e.msg} at line {e.lineno} column {e.colno}") | |
| return {"error": f"Invalid JSON input: {e.msg} at line {e.lineno} column {e.colno}"} | |
| image_urls = params.get("urls", []) | |
| if not params.get("normalfileID",[]): | |
| file_ids = [None]*len(image_urls) | |
| else: | |
| file_ids = params.get("normalfileID",[]) | |
| # api = params.get("api", "") | |
| # job_id = params.get("job_id", "") | |
| if not image_urls: | |
| logging.error("Missing required parameters: 'urls'") | |
| return {"error": "Missing required parameters: 'urls'"} | |
| try: | |
| images = [Image.open(requests.get(url, stream=True).raw) for url in image_urls] # images from URLs | |
| except Exception as e: | |
| logging.error(f"Error loading images: {e}") | |
| return {"error": f"Error loading images: {str(e)}"} | |
| names = detect_objects(images) # Perform object detection | |
| solutions = create_solutions(image_urls, names, file_ids) # Create solutions with image URLs and bounding boxes | |
| # result_url = f"{api}/{job_id}" | |
| # send_results_to_api(solutions, result_url) | |
| return json.dumps({"solutions": solutions}) | |
| inputt = gr.Textbox(label="Parameters (JSON format) Eg. {'img_url':['a.jpg','b.jpg']}") | |
| outputs = gr.JSON() | |
| application = gr.Interface(fn=process_images, inputs=inputt, outputs=outputs, title="Vehicle Classification with API Integration") | |
| application.launch() |