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("Multiple_Object_BB_Detection_v1.pt")

def detect_objects(images):
    results = model(images)
    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
    cat_id =1
    for image_url, bbox, bbox2, file_id in zip(image_urls, all_bboxes, all_bboxes2, file_ids):       # Loop through each image ID and its corresponding prediction

        for subbox, subbox2 in zip(bbox, bbox2):

            w = subbox2[2]
            h = subbox2[3]
            area = w*h
            seg=[[]]
            ans = {"segmentation":seg, "area":area, 'iscrowd':0, "image_id":img_id, "bbox": subbox, "category_id":cat_id, "id":box_id }
            ansx=[]
            ansx.append(ans)
                            
            box_id +=1
            
        solutions.append({"url": image_url,'answer':ansx, "qcUser" : None, "normalfileID": file_id })
        img_id +=1
    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)}"}
        
    all_bboxes, all_bboxes2 = detect_objects(images)  # Perform object detection
    solutions = create_solutions(image_urls, all_bboxes, all_bboxes2, 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)")
outputs = gr.JSON()

application = gr.Interface(fn=process_images, inputs=inputt, outputs=outputs, title="Multiple Object Detection with API Integration")
application.launch()