Update main.py

main.py

@@ -12,10 +12,8 @@ from fastapi.responses import StreamingResponse
 from fastapi.middleware.gzip import GZipMiddleware
 
 from utils import tts, read_image_file, pil_to_base64, base64_to_pil, get_hist
-from typing import Optional
 from huggingface_hub import hf_hub_download
 
-
 from io import BytesIO
 import zipfile
 
@@ -23,13 +21,14 @@ model_path = hf_hub_download(repo_id="ultralyticsplus/yolov8s", filename='yolov8
 model = YOLO(model_path)
 
 CLASS = model.model.names
-defaul_bot_voice = "おはいようございます"
-area_thres = 0.3
+default_bot_voice = "おはいようございます"
+area_threshold = 0.3
 ZIP = False
 
 app = FastAPI()
 app.add_middleware(GZipMiddleware, minimum_size=1000)
 
+
 @app.get("/")
 def read_root():
     return {"Message": "Application startup complete"}
@@ -53,7 +52,7 @@ async def predict_api(
     results = model.predict(image, show=False)[0]
     masks, boxes = results.masks, results.boxes
     area_image = image.width * image.height
-    
+
     # select and crop face image
     if boxes is not None:
         for xyxy, conf, cls in zip(boxes.xyxy, boxes.conf, boxes.cls):
@@ -76,13 +75,13 @@ async def predict_api(
         if out_img is not None:
             diff_value = dist.euclidean(get_hist(out_img), get_hist(last_seen))
     print("Hist time", time.time() - start_time)
-
-    # return results 
+    
+    # return results
     start_time = time.time()
     print(f"Distance: {most_close}. Different value: {diff_value}")
-    if most_close >= area_thres and diff_value >= 0.5:
+    if most_close >= area_threshold and diff_value >= 0.5:
         if ZIP:
-            voice_bot_path = tts(defaul_bot_voice, language="ja")
+            voice_bot_path = tts(default_bot_voice, language="ja")
             image_bot_path = pil_to_base64(out_img)
             print("Voice time", time.time() - start_time)
             io = BytesIO()