update: exe time

app.py

@@ -1,5 +1,7 @@
 ####################################### IMPORT #################################
 import json
+import time
+
 import pandas as pd
 from PIL import Image
 from loguru import logger
@@ -12,7 +14,6 @@ from fastapi.middleware.cors import CORSMiddleware
 from fastapi.exceptions import HTTPException
 import uvicorn
 
-
 from io import BytesIO
 
 from utils import get_image_from_bytes, detect_sample_model_origin
@@ -20,6 +21,7 @@ from utils import detect_sample_model
 from utils import add_bboxs_on_img
 from utils import get_bytes_from_image
 import gradio as gr
+
 ####################################### logger #################################
 
 logger.remove()
@@ -59,6 +61,7 @@ app.add_middleware(
     allow_headers=["*"],
 )
 
+
 @app.on_event("startup")
 def save_openapi_json():
     '''This function is used to save the OpenAPI documentation 
@@ -73,6 +76,7 @@ def save_openapi_json():
     with open("openapi.json", "w") as file:
         json.dump(openapi_data, file)
 
+
 # redirect
 @app.get("/", include_in_schema=False)
 async def redirect():
@@ -97,7 +101,7 @@ def perform_healthcheck():
 
 ######################### Support Func #################################
 
-def crop_image_by_predict(image: Image, predict: pd.DataFrame(), crop_class_name: str,) -> Image:
+def crop_image_by_predict(image: Image, predict: pd.DataFrame(), crop_class_name: str, ) -> Image:
     """Crop an image based on the detection of a certain object in the image.
     
     Args:
@@ -117,10 +121,10 @@ def crop_image_by_predict(image: Image, predict: pd.DataFrame(), crop_class_name
     if len(crop_predicts) > 1:
         crop_predicts = crop_predicts.sort_values(by=['confidence'], ascending=False)
 
-    crop_bbox = crop_predicts[['xmin', 'ymin', 'xmax','ymax']].iloc[0].values
+    crop_bbox = crop_predicts[['xmin', 'ymin', 'xmax', 'ymax']].iloc[0].values
     # crop
     img_crop = image.crop(crop_bbox)
-    return(img_crop)
+    return (img_crop)
 
 
 ######################### MAIN Func #################################
@@ -136,6 +140,7 @@ def img_object_detection_to_json(file: bytes = File(...)):
     Returns:
         dict: JSON format containing the Objects Detections.
     """
+    start = time.time()
     # Step 1: Initialize the result dictionary with None values
     # result={'detect_objects': None}
 
@@ -170,9 +175,13 @@ def img_object_detection_to_json(file: bytes = File(...)):
     }
 
     # Step 5: Logs and return
-    logger.info("results: {}", results_json)
+    # logger.info("results: {}", results_json)
+    execute_time = time.time() - start
+    logger.info("Execute_time")
+    logger.info(execute_time)
     return results_json
 
+
 @app.post("/img_object_detection_to_img")
 def img_object_detection_to_img(file: bytes = File(...)):
     """
@@ -190,11 +199,11 @@ def img_object_detection_to_img(file: bytes = File(...)):
     predict = detect_sample_model(input_image)
 
     # add bbox on image
-    final_image = add_bboxs_on_img(image = input_image, predict = predict)
+    final_image = add_bboxs_on_img(image=input_image, predict=predict)
 
     # return image in bytes format
     return StreamingResponse(content=get_bytes_from_image(final_image), media_type="image/jpeg")
 
 
 if __name__ == "__main__":
-    uvicorn.run(app, host="0.0.0.0", port=7860)
+    uvicorn.run(app, host="0.0.0.0", port=7860)