added review sql database

.gitignore

@@ -2,3 +2,5 @@ api/data/
 api/saved_models/
 api/testimages/
 api/__pycache__/
+
+**/*.env

api/bird_cnn.py

@@ -134,7 +134,7 @@ def trainCNN(model, optimizer, loss_module, train_data_loader, validation_data_l
             save_dir = os.path.join(SAVE_PATH, "bird_cnn")
             os.makedirs(save_dir, exist_ok=True)
 
-            save_path = os.path.join(save_dir, f"bird_cnn{epoch+1}")
+            save_path = os.path.join(save_dir, f"bird_cnn")
             torch.save(model.state_dict(), save_path)
 
         print(f"epoch: {epoch+1} | train accuracy: {int(train_acc * 1000) / 10}% | validation accuracy: {int(val_acc * 1000) / 10}%")

api/requirements.txt

@@ -7,3 +7,6 @@ python-multipart==0.0.27
 opencv-python==4.13.0.92
 pillow==12.2.0
 pycocotools==2.0.11
+
+pydantic==2.13.3
+pydantic_core==2.46.3

api/server.py

@@ -1,9 +1,12 @@
 from enum import Enum
 import os
+import sqlite3
 
+from dotenv import load_dotenv
+from pydantic import BaseModel
 import torch
 from torchvision import transforms
-from fastapi import FastAPI, File, UploadFile
+from fastapi import Depends, FastAPI, File, HTTPException, Header, UploadFile
 from fastapi.middleware.cors import CORSMiddleware
 from PIL import Image
 import io
@@ -73,3 +76,62 @@ async def predict(file: UploadFile = File(...)):
             "class": bird_species(cls.item()).name,
             "confidence": confidence.item()
         }
+
+
+load_dotenv("database/.env")
+API_KEY = os.getenv("API_KEY")
+
+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
+
+@app.get("/review")
+def get_reviews():
+    conn = sqlite3.connect("database/reviews.db")
+    cur = conn.cursor()
+
+    cur.execute("""
+    SELECT * FROM reviews;
+    """)
+
+    data = cur.fetchall()
+
+    print(data)
+
+    conn.commit()
+    conn.close()
+    return {"data": data}
+
+@app.post("/review")
+def post_review(review: Review):
+    conn = sqlite3.connect("database/reviews.db")
+    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 post_review(auth=Depends(get_api_key)):
+    conn = sqlite3.connect("database/reviews.db")
+    cur = conn.cursor()
+
+    cur.execute("""
+        DELETE FROM reviews;
+    """)
+
+    conn.commit()
+    conn.close()
+    return {"status": "ok"}

api/train_bird.py

@@ -5,6 +5,7 @@ from torchvision import datasets, transforms
 from torch.utils.data import DataLoader, random_split
 import matplotlib.pyplot as plt
 
+from util import TransformedSubset, visualizeData
 from bird_cnn import Bird_CNN, sample, trainCNN
 
 from enum import Enum
@@ -12,7 +13,7 @@ from enum import Enum
 from PIL import Image
 
 SAVE_PATH = "./saved_models"
-IMAGE_SIZE = 64
+IMAGE_SIZE = 128
 
 class bird_species(Enum):
     Common_Kingfisher = 0
@@ -23,24 +24,34 @@ class bird_species(Enum):
     Ruddy_Shelduck = 5
     Sarus_Crane = 6
 
-transform = transforms.Compose([
+transform_augemnt = transforms.Compose([
+    transforms.RandomAffine(
+        degrees=35,                # no rotation
+        translate=(0.2, 0.2)      # shift up to 20% horizontally/vertically
+    ),
     transforms.RandomHorizontalFlip(p=0.5),
-    transforms.RandomRotation(35),
     transforms.Resize(IMAGE_SIZE),
     transforms.CenterCrop(IMAGE_SIZE),
     transforms.ToTensor()
 ])
 
-dataset = datasets.ImageFolder("data/CUB_200_2011/images", transform=transform)
+transform = transforms.Compose([
+    transforms.Resize(IMAGE_SIZE),
+    transforms.CenterCrop(IMAGE_SIZE),
+    transforms.ToTensor()
+])
+
+dataset = datasets.ImageFolder("data/CUB_200_2011/images")
 
 train_size = int(0.8 * len(dataset))
 val_size = len(dataset) - train_size
 
-train_dataset, val_dataset = random_split(dataset, [train_size, val_size])
+train_subset, val_subset = random_split(dataset, [train_size, val_size])
-
+train_dataset = TransformedSubset(train_subset, transform_augemnt)
-train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
+val_dataset = TransformedSubset(val_subset, transform)
-val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False)
 
+train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
+val_loader = DataLoader(val_dataset, batch_size=64, shuffle=False)
 
 device = torch.device("cpu") if not torch.cuda.is_available() else torch.device("cuda:0")
 print("Using device", device)
@@ -50,7 +61,7 @@ model.to(device)
 optimizer = torch.optim.Adam(model.parameters(), lr=1e-3, weight_decay=1e-4)
 loss_module = nn.CrossEntropyLoss()
 
-trainCNN(model, optimizer, loss_module, train_loader, val_loader, device, 50, SAVE_PATH=SAVE_PATH, save=True)
+trainCNN(model, optimizer, loss_module, train_loader, val_loader, device, 500, SAVE_PATH=SAVE_PATH, save=True)
 exit()
 
 image_path = "test1.jpg"

api/util.py

@@ -1,22 +1,37 @@
 from PIL import Image
 import os
+from matplotlib import pyplot as plt
+import torch
 
-folder = "data/train"
+class TransformedSubset(torch.utils.data.Dataset):
+    def __init__(self, subset, transform=None):
+        self.subset = subset
+        self.transform = transform
 
-widths = []
+    def __getitem__(self, idx):
-heights = []
+        x, y = self.subset[idx]
 
-for root, _, files in os.walk(folder):
+        if self.transform:
-    for file in files:
+            x = self.transform(x)
-        if file.endswith((".jpg", ".png", ".jpeg")):
-            path = os.path.join(root, file)
-            img = Image.open(path)
-            w, h = img.size
-            widths.append(w)
-            heights.append(h)
 
-avg_w = sum(widths) / len(widths)
+        return x, y
-avg_h = sum(heights) / len(heights)
 
-print("Average width:", avg_w)
+    def __len__(self):
-print("Average height:", avg_h)
+        return len(self.subset)
+    
+
+def visualizeData(dataset):
+    images, labels = next(iter(dataset))
+
+    for i in range(4):
+        img = images[i]
+
+        # Convert tensor shape from [C,H,W] -> [H,W,C]
+        img = img.permute(1, 2, 0)
+
+        plt.figure(figsize=(3,3))
+        plt.imshow(img)
+        plt.title(f"Label: {labels[i].item()}")
+        plt.axis("off")
+
+    plt.show()

compose.yaml

@@ -29,6 +29,8 @@ services:
       - cnn_network
     pull_policy: never
     container_name: cnn_api
+    volumes:
+      - ./database:/api/database
 
 
   cnn_website: