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Image_Classification_Service

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PhonePixelGhost commited on 2 days ago

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76419e9

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1 Parent(s): 537d300

Upload main.py

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app/main.py +89 -15

app/main.py CHANGED Viewed

@@ -1,17 +1,100 @@
 from fastapi import FastAPI, File, UploadFile, HTTPException
 from concurrent.futures import ProcessPoolExecutor
 import asyncio
 from app.model import run_inference
 from app.schemas import PredictionResponse
 app = FastAPI(title="ResNet-18 Image Classifier", version="1.0.0")
-# ใช้ 6 Workers ตามจำนวน Physical Cores ของ Ryzen 7500F
-executor = ProcessPoolExecutor(max_workers=6)
-MAX_FILE_SIZE = 10 * 1024 * 1024  # 10 MB
 ALLOWED_CONTENT_TYPES = {"image/jpeg", "image/png", "image/webp", "image/gif"}
 @app.get("/health")
 async def health():
     return {"status": "ok"}
@@ -20,16 +103,7 @@ async def health():
 async def predict(file: UploadFile = File(...)):
     if file.content_type not in ALLOWED_CONTENT_TYPES:
         raise HTTPException(status_code=415, detail="Unsupported media type")
     image_bytes = await file.read()
-    if len(image_bytes) > MAX_FILE_SIZE:
-        raise HTTPException(status_code=413, detail="File too large")
-    # รัน Inference ใน ProcessPoolExecutor เพื่อกระจายโหลดลง 6 Cores
     loop = asyncio.get_event_loop()
-    try:
-        result = await loop.run_in_executor(executor, run_inference, image_bytes)
-        return result
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=f"Inference error: {str(e)}")

 from fastapi import FastAPI, File, UploadFile, HTTPException
+from fastapi.responses import HTMLResponse
 from concurrent.futures import ProcessPoolExecutor
 import asyncio
 from app.model import run_inference
 from app.schemas import PredictionResponse
 app = FastAPI(title="ResNet-18 Image Classifier", version="1.0.0")
+executor = ProcessPoolExecutor(max_workers=4)
 ALLOWED_CONTENT_TYPES = {"image/jpeg", "image/png", "image/webp", "image/gif"}
+@app.get("/", response_class=HTMLResponse)
+async def demo_ui():
+    return """
+    <!DOCTYPE html>
+    <html>
+    <head>
+        <title>ResNet-18 Image Classifier</title>
+        <meta name="viewport" content="width=device-width, initial-scale=1">
+        <link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;600&display=swap" rel="stylesheet">
+        <style>
+            body { font-family: 'Inter', sans-serif; background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); min-height: 100vh; display: flex; align-items: center; justify-content: center; margin: 0; color: #333; }
+            .card { background: white; padding: 2rem; border-radius: 1.5rem; box-shadow: 0 20px 25px -5px rgba(0, 0, 0, 0.1); width: 100%; max-width: 450px; text-align: center; }
+            h1 { color: #1a202c; margin-bottom: 1.5rem; font-size: 1.5rem; }
+            #preview { width: 100%; height: 250px; border: 2px dashed #e2e8f0; border-radius: 1rem; object-fit: cover; margin-bottom: 1.5rem; display: none; }
+            .upload-btn-wrapper { position: relative; overflow: hidden; display: inline-block; width: 100%; }
+            .btn { border: none; color: white; background: #667eea; padding: 0.75rem 2rem; border-radius: 0.75rem; font-weight: 600; cursor: pointer; transition: 0.3s; width: 100%; font-size: 1rem; }
+            .btn:hover { background: #5a67d8; transform: translateY(-2px); }
+            input[type=file] { font-size: 100px; position: absolute; left: 0; top: 0; opacity: 0; cursor: pointer; }
+            #result { margin-top: 1.5rem; padding: 1rem; border-radius: 1rem; background: #f7fafc; display: none; text-align: left; }
+            .label { font-weight: 600; color: #4a5568; }
+            .value { color: #2d3748; float: right; }
+            .loading { display: none; margin-top: 1rem; }
+        </style>
+    </head>
+    <body>
+        <div class="card">
+            <h1>ResNet-18 Quantized</h1>
+            <img id="preview" src="#" alt="Preview">
+            <div class="upload-btn-wrapper">
+                <button class="btn" id="btn-text">Select Image to Predict</button>
+                <input type="file" id="fileInput" accept="image/*">
+            </div>
+            <div id="loading" class="loading">⌛ Predicting...</div>
+            <div id="result">
+                <div><span class="label">Label:</span> <span class="value" id="res-label">-</span></div>
+                <div style="margin-top:0.5rem"><span class="label">Confidence:</span> <span class="value" id="res-score">-</span></div>
+                <div style="margin-top:0.5rem"><span class="label">Inference:</span> <span class="value" id="res-time">-</span></div>
+            </div>
+        </div>
+        <script>
+            const fileInput = document.getElementById('fileInput');
+            const preview = document.getElementById('preview');
+            const resultDiv = document.getElementById('result');
+            const loading = document.getElementById('loading');
+            const btnText = document.getElementById('btn-text');
+            fileInput.onchange = evt => {
+                const [file] = fileInput.files;
+                if (file) {
+                    preview.src = URL.createObjectURL(file);
+                    preview.style.display = 'block';
+                    predict(file);
+                }
+            }
+            async function predict(file) {
+                const formData = new FormData();
+                formData.append('file', file);
+                loading.style.display = 'block';
+                resultDiv.style.display = 'none';
+                btnText.disabled = true;
+                try {
+                    const response = await fetch('/predict', { method: 'POST', body: formData });
+                    const data = await response.json();
+                    document.getElementById('res-label').innerText = data.label;
+                    document.getElementById('res-score').innerText = (data.score * 100).toFixed(2) + '%';
+                    document.getElementById('res-time').innerText = data.inference_time_ms.toFixed(2) + ' ms';
+                    resultDiv.style.display = 'block';
+                } catch (e) {
+                    alert('Error predicting image');
+                } finally {
+                    loading.style.display = 'none';
+                    btnText.disabled = false;
+                }
+            }
+        </script>
+    </body>
+    </html>
+    """
 @app.get("/health")
 async def health():
     return {"status": "ok"}
 async def predict(file: UploadFile = File(...)):
     if file.content_type not in ALLOWED_CONTENT_TYPES:
         raise HTTPException(status_code=415, detail="Unsupported media type")
     image_bytes = await file.read()
     loop = asyncio.get_event_loop()
+    result = await loop.run_in_executor(executor, run_inference, image_bytes)
+    return result