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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("Covid_Positive_Negative_Classify_v1.pt")
def detect_objects(images):
results = model(images)
classes={1:"Negative", 0:"Positive"}
names=[]
probss=[]
for result in results:
probs = result.probs.top1
probss.append(probs)
names.append(classes[probs])
return names, probss
def create_solutions(image_urls, names, probss, file_ids):
solutions = [] #list to store all the objects
for image_url, class_name, prob, file_id in zip(image_urls, names, probss, file_ids):
obj = {"image": image_url, "answer": class_name, "qcUserID" : None, "normalfileID": file_id}
solutions.append(obj)
print("image done")
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", [])
# normalFileId = 0
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:
print(f"operating on : {image_urls}")
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, probss = detect_objects(images) # Perform object detection
solutions = create_solutions(image_urls, names, probss, file_ids) # Create solutions with image URLs and bounding boxes
# result_url = f"{api}/{job_id}"
# send_results_to_api(solutions, result_url)
print("Solution sent")
return json.dumps({"solutions": solutions})
inputt = gr.Textbox(label="Parameters (JSON format) Eg. img_url:['','']")
outputs = gr.JSON()
application = gr.Interface(fn=process_images, inputs=inputt, outputs=outputs, title="Covid +ve -ve Classification with API Integration")
application.launch() |