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7a5fac6 c6dbf09 7a5fac6 c6dbf09 0d990b5 7a5fac6 bbfb731 7a5fac6 bbfb731 7a5fac6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 | 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("Car_Colours_Classify_v1.pt")
def detect_objects(images):
results = model(images)
classes = {0: "beige", 1: "black", 2: "blue", 3: "brown", 4: "gold", 5: "green", 6: "grey", 7: "orange", 8: "pink", 9: "purple", 10: "red", 11: "silver", 12: "tan", 13: "white", 14: "yellow"}
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):
obj = {"image": image_url, "answer": [prediction], "qcUser" : None, "normalfileID" : file_id}
solutions.append(obj)
return solutions
# def send_results_to_api(solutions, url):
# headers = {"Content-Type": "application/json"}
# try:
# logging.info(f"Sending results to API at {url} with data: {solutions}")
# # response = requests.patch(url, json = {"solutions":solutions}) # Set a timeout headers=headers,, timeout=60
# data = {"solutions":solutions}
# response = requests.patch(url, data=json.dumps(data), headers=headers)
# response.raise_for_status()
# logging.info(f"Response from API: {response.text}")
# return response.json()
# except requests.exceptions.RequestException as e:
# logging.error(f"Failed to send results to API: {e}")
# return {"error": f"Failed to send results to API: {str(e)}"}
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]
except Exception as e:
logging.error(f"Error loading images: {e}")
return {"error": f"Error loading images: {str(e)}"}
names = detect_objects(images)
solutions = create_solutions(image_urls, names, file_ids)
# result_url = f"{api}/{job_id}"
# response = send_results_to_api(solutions, result_url)
return json.dumps({"solutions": solutions})
inputt = gr.Textbox(label="Parameters (JSON format) Eg. {'urls':['a.jpg','b.jpg']}")
outputs = gr.JSON()
application = gr.Interface(fn=process_images, inputs=inputt, outputs=outputs, title="Car Colour Classification with API Integration")
application.launch() |