working server

bird_cnn/server.py

@@ -0,0 +1,61 @@
+from enum import Enum
+import os
+
+import torch
+from torchvision import transforms
+from fastapi import FastAPI, File, UploadFile
+from PIL import Image
+import io
+import torch.nn.functional as F
+
+from bird_cnn import Bird_CNN
+
+SAVE_PATH = "./saved_models"
+#IMAGE_SIZE = (1141, 850)
+IMAGE_SIZE = (300, 300)
+
+transform = transforms.Compose([
+    transforms.Resize(IMAGE_SIZE),
+    transforms.ToTensor()
+])
+
+class bird_species(Enum):
+    Common_Kingfisher = 0
+    CommonMyna = 1
+    House_Crow = 2
+    Indian_Peacock = 3
+    Indian_Pitta = 4
+    Ruddy_Shelduck = 5
+    Sarus_Crane = 6
+
+transform = transforms.Compose([
+    transforms.Resize(IMAGE_SIZE),
+    transforms.ToTensor()
+])
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+model = Bird_CNN(c_in=3, c_hidden=15, c_out=7, kernel_size=3, img_width=IMAGE_SIZE[0], img_height=IMAGE_SIZE[1])
+full_path = os.path.join(SAVE_PATH, "bird_cnn", "bird_cnn")
+model.load_state_dict(torch.load(full_path, weights_only=False))
+model.to(device)
+model.eval()
+
+app = FastAPI()
+
+@app.post("/predict")
+async def predict(file: UploadFile = File(...)):
+    image_bytes = await file.read()
+
+    image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
+    image = transform(image).unsqueeze(0).to(device)
+
+    with torch.no_grad():
+        pred = model(image)
+        probs = F.softmax(pred, dim=1)
+        confidence, cls = torch.max(probs, dim=1)
+
+    return {
+        "class": bird_species(cls.item()).name,
+        "confidence": confidence.item()
+    }