server cleanup

2026-05-31 22:11:13 +02:00
parent 8ca0568017
commit edf4f76c6f
9 changed files with 80 additions and 15 deletions
@@ -0,0 +1,216 @@
+from enum import Enum
+import os
+import sqlite3
+import time
+
+import cv2
+from dotenv import load_dotenv
+import numpy as np
+from pydantic import BaseModel
+import torch
+from torchvision import transforms
+from fastapi import Depends, FastAPI, File, HTTPException, Header, UploadFile, WebSocket, WebSocketDisconnect
+from fastapi.middleware.cors import CORSMiddleware
+from PIL import Image
+import io
+import torch.nn.functional as F
+
+import threading
+
+from yolo_model_production import convert_prediction, Yolo_model
+from bird_cnn_production import Bird_CNN
+
+BUILD_PATH = "./build_models"
+IMAGE_SIZE_CNN = 64
+IMAGE_SIZE_YOLO = 64
+
+sem_ai = threading.Semaphore(1)
+
+class bird_species(Enum):
+    Common_Kingfisher = 0
+    Common_Myna = 1
+    House_Crow = 2
+    Indian_Peacock = 3
+    Indian_Pitta = 4
+    Ruddy_Shelduck = 5
+    Sarus_Crane = 6
+
+transform_bird = transforms.Compose([
+    transforms.Resize(IMAGE_SIZE_CNN),
+    transforms.CenterCrop(IMAGE_SIZE_CNN),
+    transforms.ToTensor()
+])
+
+transform_face = transforms.Compose([
+    transforms.Resize((IMAGE_SIZE_YOLO, IMAGE_SIZE_YOLO)),
+    transforms.ToTensor()
+])
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+model_bird = Bird_CNN(c_in=3, c_hidden=16, c_out=7)
+full_path = os.path.join(BUILD_PATH, "bird_cnn")
+model_bird.load_state_dict(torch.load(full_path, map_location=torch.device(device)))
+model_bird.to(device)
+model_bird.eval()
+
+modeL_face = Yolo_model(c_in=3, boxes=1, grid=6, labels=1, c_hidden=16)
+full_path = os.path.join(BUILD_PATH, "face_detection_yolo")
+modeL_face.load_state_dict(torch.load(full_path, map_location=torch.device(device)))
+modeL_face.to(device)
+modeL_face.eval()
+
+app = FastAPI()
+
+origins = [
+    "https://marvinkrausser.com",
+    "https://api.marvinkrausser.com",
+]
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=origins,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+@app.post("/predict")
+async def predict(file: UploadFile = File(...)):
+    with sem_ai:
+        image_bytes = await file.read()
+
+        image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
+        image = transform_bird(image).unsqueeze(0).to(device)
+
+        with torch.no_grad():
+            pred = model_bird(image)
+            probs = F.softmax(pred, dim=1)
+            confidence, cls = torch.max(probs, dim=1)
+
+        return {
+            "class": bird_species(cls.item()).name,
+            "confidence": confidence.item()
+        }
+    
+@app.websocket("/predict_face")
+async def predict_face(websocket: WebSocket):
+    await websocket.accept()
+
+    #last = time.time()
+
+    try:
+        while True:
+            jpg_bytes = await websocket.receive_bytes()
+
+            #if time.time() - last < 1:
+                #continue
+
+            #last = time.time()
+            with sem_ai:
+
+                np_arr = np.frombuffer(jpg_bytes, np.uint8)
+                frame = cv2.imdecode(np_arr, cv2.IMREAD_COLOR)
+
+                frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+                image = Image.fromarray(frame)
+
+                H, W, _ = frame.shape
+                scale_w = W / IMAGE_SIZE_YOLO
+                scale_h = H / IMAGE_SIZE_YOLO
+
+                image = transform_face(image).unsqueeze(0).to(device)
+
+                with torch.no_grad():
+                    pred = modeL_face(image)
+                    bboxes, _, _ = convert_prediction(
+                        pred.squeeze(0),
+                        image.squeeze(0),
+                        threshold=0.9
+                    )
+
+                boxes_to_send = []
+                for bbox in bboxes:
+                    xmin = int(bbox[0] * scale_w)
+                    ymin = int(bbox[1] * scale_h)
+                    xmax = int(bbox[2] * scale_w)
+                    ymax = int(bbox[3] * scale_h)
+                    boxes_to_send.append([xmin, ymin, xmax, ymax])
+
+                await websocket.send_json({"bboxes": boxes_to_send})
+
+    except WebSocketDisconnect:
+        print("Client disconnected")
+
+
+load_dotenv("./database/.env")
+API_KEY = os.getenv("API_KEY")
+DATABASE = "./database/reviews.db"
+
+def get_api_key(authorization: str = Header(None)):
+    if authorization != f"Bearer {API_KEY}":
+        raise HTTPException(status_code=401, detail="Unauthorized")
+    return authorization
+
+class Review(BaseModel):
+    website: str
+    rating: int
+    text: str
+    date: str
+
+sem_db = threading.Semaphore(1)
+
+@app.get("/review")
+def get_reviews(auth=Depends(get_api_key)):
+    with sem_db:
+        conn = sqlite3.connect(
+            DATABASE,
+            check_same_thread=False
+        )
+        cur = conn.cursor()
+
+        cur.execute("""
+        SELECT * FROM reviews;
+        """)
+
+        data = cur.fetchall()
+
+        print(data)
+
+        conn.close()
+        return {"data": data}
+
+@app.post("/review")
+def post_review(review: Review):
+    with sem_db:
+        conn = sqlite3.connect(
+            DATABASE,
+            check_same_thread=False
+        )
+        cur = conn.cursor()
+
+        cur.execute("""
+            INSERT INTO reviews (website, rating, text, created_at)
+            VALUES (?, ?, ?, ?)
+        """, (review.website, review.rating, review.text, review.date))
+
+        conn.commit()
+        conn.close()
+        return {"status": "ok"}
+
+@app.delete("/review")
+def delete_review(auth=Depends(get_api_key)):
+    with sem_db:
+        conn = sqlite3.connect(
+            DATABASE,
+            check_same_thread=False
+        )
+        cur = conn.cursor()
+
+        cur.execute("""
+            DELETE FROM reviews;
+        """)
+
+        conn.commit()
+        conn.close()
+        return {"status": "ok"}