Update main.py

main.py

@@ -1,16 +1,6 @@
-from ultralyticsplus import YOLO
-from base64 import b64encode
-from speech_recognition import AudioFile, Recognizer
-import numpy as np
-from scipy.spatial import distance as dist
+from fastapi import FastAPI
 
-from sahi.utils.cv import read_image_as_pil
-from fastapi import FastAPI, File, UploadFile, Form
-from utils import tts, read_image_file, pil_to_base64, base64_to_pil, get_hist
-from typing import Optional
 
-model = YOLO('ultralyticsplus/yolov8s')
-CLASS = model.model.names
 
 app = FastAPI()
 defaul_bot_voice = "おはいようございます"
@@ -19,40 +9,4 @@ area_thres = 0.3
 
 @app.get("/")
 def read_root():
-    return {"Message": "Application startup complete"}
-
-
-@app.post("/aisatsu_api/")
-async def predict_api(
-        file: UploadFile = File(...),
-        last_seen: Optional[str] = Form(None)
-):
-    image = read_image_file(await file.read())
-    results = model.predict(image, show=False)[0]
-    image = read_image_as_pil(image)
-    masks, boxes = results.masks, results.boxes
-    area_image = image.width * image.height
-    voice_bot = None
-    most_close = 0
-    out_img = None
-    diff_value = 0.5
-    if boxes is not None:
-        for xyxy, conf, cls in zip(boxes.xyxy, boxes.conf, boxes.cls):
-            if int(cls) != 0:
-                continue
-            box = xyxy.tolist()
-            area_rate = (box[2] - box[0]) * (box[3] - box[1]) / area_image
-            if area_rate >= most_close:
-                out_img = image.crop(tuple(box)).resize((128, 128))
-                most_close = area_rate
-    if last_seen is not None:
-        last_seen = base64_to_pil(last_seen)
-        if out_img is not None:
-            diff_value = dist.euclidean(get_hist(out_img), get_hist(last_seen))
-    print(most_close, diff_value)
-    if most_close >= area_thres and diff_value >= 0.5:
-        voice_bot = tts(defaul_bot_voice, language="ja")
-    return {
-        "voice": voice_bot,
-        "image": pil_to_base64(out_img) if out_img is not None else None
-    }
+    return {"Message": "Application startup complete"}