changed cnn architecture, added website image upload

.gitignore

@@ -1 +1,2 @@
-bird_cnn/data/
+bird_cnn/data/
+bird_cnn/saved_models/

bird_cnn/Dockerfile

@@ -0,0 +1,16 @@
+FROM python:3.11-slim
+
+# Set working directory
+WORKDIR /app
+
+# Copy requirements first (better Docker layer caching)
+COPY . .
+
+# Install Python dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Make sure the startup script is executable
+RUN chmod +x start_server.sh
+
+# Use the script as the container entrypoint
+ENTRYPOINT ["./start_server.sh"]

bird_cnn/bird_cnn.py

@@ -3,26 +3,77 @@ import os
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-import numpy as np
 from tqdm import tqdm
 
+class  SeparableConvolution(nn.Module):
+    def __init__(self, c_in, c_out, kernel_size):
+        super().__init__()
+        self.depthwise = nn.Conv2d(c_in, c_in, kernel_size, groups=c_in, padding=kernel_size//2)
+        self.bn1 = nn.BatchNorm2d(c_in)
+        self.pointwise = nn.Conv2d(c_in, c_out, kernel_size=1)
+        self.bn2 = nn.BatchNorm2d(c_out)
+
+    def forward(self, x):
+        x = self.depthwise(x)
+        x = self.bn1(x)
+        x = F.relu(x)
+
+        x = self.pointwise(x)
+        x = self.bn2(x)
+        x = F.relu(x)
+
+        return x
+
 
 class Bird_CNN(nn.Module):
-    def __init__(self, c_in, c_hidden, c_out, kernel_size, img_width, img_height):
+    def __init__(self, c_in, c_hidden, c_out):
         super().__init__()
-        self.model = nn.Sequential(
-            nn.Conv2d(c_in, c_hidden, kernel_size, padding=kernel_size//2),
-            nn.ReLU(),
 
-            nn.Conv2d(c_hidden, c_hidden, kernel_size, padding=kernel_size//2),
+        self.conv_init = nn.Sequential(
+            nn.Conv2d(c_in, c_hidden, kernel_size=3, padding=1),
+            nn.BatchNorm2d(c_hidden),
             nn.ReLU(),
-
+            nn.Conv2d(c_hidden, c_hidden, 3, stride=2, padding=1)
-            nn.Flatten(),
-            nn.Linear(c_hidden * img_height * img_width, c_out)
         )
 
+        # 1x1 conv branch
+        self.branch1 = SeparableConvolution(c_in=c_hidden, c_out=64, kernel_size=1)
+
+        # 1x1 -> 3x3 conv branch
+        self.branch2 = SeparableConvolution(c_in=c_hidden, c_out=128, kernel_size=3)
+
+        # 1x1 -> 5x5 conv branch
+        self.branch3 = SeparableConvolution(c_in=c_hidden, c_out=32, kernel_size=5)
+
+        # 3x3 max pooling -> 1x1 conv branch
+        self.branch4 = nn.Sequential(
+            nn.MaxPool2d(kernel_size=3, stride=1, padding=1),
+            nn.Conv2d(c_hidden, 32, kernel_size=1),
+            nn.ReLU()
+        )
+
+        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
+        self.flatten = nn.Flatten()
+        self.linear = nn.Linear(256, c_out)
+
+        self.dropout = nn.Dropout(0.3)
+
     def forward(self, x):
-        return self.model(x)
+        x = self.conv_init(x)
+
+        b1 = self.branch1(x)
+        b2 = self.branch2(x)
+        b3 = self.branch3(x)
+        b4 = self.branch4(x)
+
+        x = torch.cat([b1, b2, b3, b4], dim=1)
+        x = F.relu(x)
+        x = self.avgpool(x)
+        x = torch.flatten(x, 1)
+
+        x = self.dropout(x)
+
+        return self.linear(x)
 
 
 def trainCNN(model, optimizer, loss_module, train_data_loader, validation_data_loader, device, num_epochs, SAVE_PATH, save=False):
@@ -79,7 +130,7 @@ 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, "bird_cnn")
+            save_path = os.path.join(save_dir, f"bird_cnn{epoch+1}")
             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}%")

bird_cnn/main.py

@@ -34,13 +34,13 @@ val_size = len(dataset) - train_size
 
 train_dataset, val_dataset = random_split(dataset, [train_size, val_size])
 
-train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
+train_loader = DataLoader(train_dataset, batch_size=26, shuffle=True)
-val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False)
+val_loader = DataLoader(val_dataset, batch_size=26, shuffle=False)
 
 device = torch.device("cpu") if not torch.cuda.is_available() else torch.device("cuda:0")
 print("Using device", device)
 
-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])
+model = Bird_CNN(c_in=3, c_hidden=15, c_out=7)
 model.to(device)
 optimizer = torch.optim.Adam(model.parameters(), lr=1e-3, weight_decay=1e-4)
 loss_module = nn.CrossEntropyLoss()

bird_cnn/requirements.txt

@@ -0,0 +1,7 @@
+fastapi==0.136.1
+networkx==3.6.1
+numpy==2.3.4
+torch==2.11.0+cu126
+torchvision==0.26.0+cu126
+tqdm==4.67.3
+uvicorn==0.46.0

bird_cnn/server.py

@@ -4,6 +4,7 @@ import os
 import torch
 from torchvision import transforms
 from fastapi import FastAPI, File, UploadFile
+from fastapi.middleware.cors import CORSMiddleware
 from PIL import Image
 import io
 import torch.nn.functional as F
@@ -43,6 +44,18 @@ model.eval()
 
 app = FastAPI()
 
+origins = [
+    "http://localhost:5173",
+]
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=origins,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
 @app.post("/predict")
 async def predict(file: UploadFile = File(...)):
     image_bytes = await file.read()

bird_cnn/start_server.bat

@@ -0,0 +1 @@
+python -m uvicorn server:app --reload

cnn_website/package-lock.json

@@ -9,7 +9,8 @@
       "version": "0.0.0",
       "dependencies": {
         "react": "^19.2.5",
-        "react-dom": "^19.2.5"
+        "react-dom": "^19.2.5",
+        "react-router-dom": "^7.14.2"
       },
       "devDependencies": {
         "@eslint/js": "^10.0.1",
@@ -264,31 +265,6 @@
         "node": ">=6.9.0"
       }
     },
-    "node_modules/@emnapi/core": {
-      "version": "1.10.0",
-      "resolved": "https://registry.npmjs.org/@emnapi/core/-/core-1.10.0.tgz",
-      "integrity": "sha512-yq6OkJ4p82CAfPl0u9mQebQHKPJkY7WrIuk205cTYnYe+k2Z8YBh11FrbRG/H6ihirqcacOgl2BIO8oyMQLeXw==",
-      "dev": true,
-      "license": "MIT",
-      "optional": true,
-      "peer": true,
-      "dependencies": {
-        "@emnapi/wasi-threads": "1.2.1",
-        "tslib": "^2.4.0"
-      }
-    },
-    "node_modules/@emnapi/runtime": {
-      "version": "1.10.0",
-      "resolved": "https://registry.npmjs.org/@emnapi/runtime/-/runtime-1.10.0.tgz",
-      "integrity": "sha512-ewvYlk86xUoGI0zQRNq/mC+16R1QeDlKQy21Ki3oSYXNgLb45GV1P6A0M+/s6nyCuNDqe5VpaY84BzXGwVbwFA==",
-      "dev": true,
-      "license": "MIT",
-      "optional": true,
-      "peer": true,
-      "dependencies": {
-        "tslib": "^2.4.0"
-      }
-    },
     "node_modules/@emnapi/wasi-threads": {
       "version": "1.2.1",
       "resolved": "https://registry.npmjs.org/@emnapi/wasi-threads/-/wasi-threads-1.2.1.tgz",
@@ -1056,6 +1032,19 @@
       "dev": true,
       "license": "MIT"
     },
+    "node_modules/cookie": {
+      "version": "1.1.1",
+      "resolved": "https://registry.npmjs.org/cookie/-/cookie-1.1.1.tgz",
+      "integrity": "sha512-ei8Aos7ja0weRpFzJnEA9UHJ/7XQmqglbRwnf2ATjcB9Wq874VKH9kfjjirM6UhU2/E5fFYadylyhFldcqSidQ==",
+      "license": "MIT",
+      "engines": {
+        "node": ">=18"
+      },
+      "funding": {
+        "type": "opencollective",
+        "url": "https://opencollective.com/express"
+      }
+    },
     "node_modules/cross-spawn": {
       "version": "7.0.6",
       "resolved": "https://registry.npmjs.org/cross-spawn/-/cross-spawn-7.0.6.tgz",
@@ -2110,6 +2099,7 @@
       "resolved": "https://registry.npmjs.org/react-dom/-/react-dom-19.2.5.tgz",
       "integrity": "sha512-J5bAZz+DXMMwW/wV3xzKke59Af6CHY7G4uYLN1OvBcKEsWOs4pQExj86BBKamxl/Ik5bx9whOrvBlSDfWzgSag==",
       "license": "MIT",
+      "peer": true,
       "dependencies": {
         "scheduler": "^0.27.0"
       },
@@ -2117,6 +2107,44 @@
         "react": "^19.2.5"
       }
     },
+    "node_modules/react-router": {
+      "version": "7.14.2",
+      "resolved": "https://registry.npmjs.org/react-router/-/react-router-7.14.2.tgz",
+      "integrity": "sha512-yCqNne6I8IB6rVCH7XUvlBK7/QKyqypBFGv+8dj4QBFJiiRX+FG7/nkdAvGElyvVZ/HQP5N19wzteuTARXi5Gw==",
+      "license": "MIT",
+      "dependencies": {
+        "cookie": "^1.0.1",
+        "set-cookie-parser": "^2.6.0"
+      },
+      "engines": {
+        "node": ">=20.0.0"
+      },
+      "peerDependencies": {
+        "react": ">=18",
+        "react-dom": ">=18"
+      },
+      "peerDependenciesMeta": {
+        "react-dom": {
+          "optional": true
+        }
+      }
+    },
+    "node_modules/react-router-dom": {
+      "version": "7.14.2",
+      "resolved": "https://registry.npmjs.org/react-router-dom/-/react-router-dom-7.14.2.tgz",
+      "integrity": "sha512-YZcM5ES8jJSM+KrJ9BdvHHqlnGTg5tH3sC5ChFRj4inosKctdyzBDhOyyHdGk597q2OT6NTrCA1OvB/YDwfekQ==",
+      "license": "MIT",
+      "dependencies": {
+        "react-router": "7.14.2"
+      },
+      "engines": {
+        "node": ">=20.0.0"
+      },
+      "peerDependencies": {
+        "react": ">=18",
+        "react-dom": ">=18"
+      }
+    },
     "node_modules/rolldown": {
       "version": "1.0.0-rc.17",
       "resolved": "https://registry.npmjs.org/rolldown/-/rolldown-1.0.0-rc.17.tgz",
@@ -2174,6 +2202,12 @@
         "semver": "bin/semver.js"
       }
     },
+    "node_modules/set-cookie-parser": {
+      "version": "2.7.2",
+      "resolved": "https://registry.npmjs.org/set-cookie-parser/-/set-cookie-parser-2.7.2.tgz",
+      "integrity": "sha512-oeM1lpU/UvhTxw+g3cIfxXHyJRc/uidd3yK1P242gzHds0udQBYzs3y8j4gCCW+ZJ7ad0yctld8RYO+bdurlvw==",
+      "license": "MIT"
+    },
     "node_modules/shebang-command": {
       "version": "2.0.0",
       "resolved": "https://registry.npmjs.org/shebang-command/-/shebang-command-2.0.0.tgz",

cnn_website/package.json

@@ -11,7 +11,8 @@
   },
   "dependencies": {
     "react": "^19.2.5",
-    "react-dom": "^19.2.5"
+    "react-dom": "^19.2.5",
+    "react-router-dom": "^7.14.2"
   },
   "devDependencies": {
     "@eslint/js": "^10.0.1",

cnn_website/src/App.jsx

@@ -1,15 +1,22 @@
 import { useState } from 'react'
+import { BrowserRouter as Router, Routes, Route } from "react-router-dom";
 import reactLogo from './assets/react.svg'
 import viteLogo from './assets/vite.svg'
 import heroImg from './assets/hero.png'
 import './App.css'
 import Menu from './Menu'
+import Homepage from './Homepage';
+import Bird_CNN from './Bird_CNN';
 
 function App() {
 
   return (
     <>
       <Menu />
+      <Routes>
+        <Route path="/" element={<Homepage />} />
+        <Route path="/bird_cnn" element={<Bird_CNN />} />
+      </Routes>
     </>
   );
 }

cnn_website/src/Bird_CNN.css

@@ -0,0 +1,5 @@
+.content-block {
+    display: flex;
+    align-items: baseline;
+    gap: 20px;
+}

cnn_website/src/Bird_CNN.jsx

@@ -0,0 +1,68 @@
+import { useState } from 'react'
+import './Bird_CNN.css'
+
+function Bird_CNN() {
+
+    const [file, setFile] = useState(null);
+    const [birdClass, setBirdClass] = useState(null);
+    const [confidence, setConfidence] = useState(null);
+    const [error, setError] = useState(false);
+
+    const handleImage = (e) => {
+        setFile(e.target.files[0]);
+    };
+
+    const sendImage = async (e) => {
+        if (!file) return;
+
+        const formData = new FormData();
+        formData.append("file", file);
+
+        try {
+            const response = await fetch("http://127.0.0.1:8000/predict", {
+                method: "POST",
+                body: formData,
+            });
+
+            if (!response.ok) {
+                setError(true);
+            }
+            else {
+                setError(false);
+            }
+
+            const result = await response.json();
+            setBirdClass(result["class"]);
+            const confidence = result["confidence"];
+            setConfidence(`${Math.round(confidence * 100)}%`);
+        }
+        catch (error) {
+            console.error("Upload failed:", error);
+        }
+    };
+
+    return (
+        <>
+            <h1>bird-cnn</h1>
+            <div>
+                <input type="file" accept="image/jpeg" onChange={handleImage} />
+                <button onClick={sendImage}>Upload</button>
+
+
+                <div className='response-block'>
+                    {!error && <div className='content-block class'>
+                        <h3>Bird Species: </h3>
+                        <p id='bird-class-text'>{birdClass}</p>
+                    </div>}
+                    {!error && <div className='content-block confidence'>
+                        <h4>Model Confidence: </h4>
+                        <p id='bird-confidence-text'>{confidence}</p>
+                    </div>}
+                    {error && <h4>An Error has uccured. Please try again later.</h4>}
+                </div>
+            </div>
+        </>
+    );
+}
+
+export default Bird_CNN;

cnn_website/src/Homepage.jsx

@@ -0,0 +1,9 @@
+function Homepage() {
+    return (
+        <>
+            <p>Homepage</p>
+        </>
+    );
+}
+
+export default Homepage;

cnn_website/src/Menu.jsx

@@ -1,15 +1,12 @@
+import { Link } from 'react-router-dom';
 import './Menu.css'
 
 function Menu() {
     return (
         <>
             <nav id="navbar-main">
-                <li className="menu-item">
+                <Link to="/">Homepage</Link>
-                    <a className="link-text">Test</a>
+                <Link to="/bird_cnn">Birds</Link>
-                </li>
-                <li className="menu-item">
-                    <a className="link-text">Test2</a>
-                </li>
             </nav>
         </>
     );

cnn_website/src/main.jsx

@@ -2,9 +2,12 @@ import { StrictMode } from 'react'
 import { createRoot } from 'react-dom/client'
 import './index.css'
 import App from './App.jsx'
+import { BrowserRouter } from 'react-router-dom'
 
 createRoot(document.getElementById('root')).render(
   <StrictMode>
-    <App />
+    <BrowserRouter>
-  </StrictMode>,
+      <App />
-)
+    </BrowserRouter>
+  </StrictMode>
+);