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efficientnetv2s.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c2da0b93a32a2f206e8e437f4a516720bcda36693b9f5f054e5387a701131119
+size 247255104

main.py

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+from fastapi import FastAPI, File, UploadFile, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import JSONResponse
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+import os
+import uuid
+import tempfile
+
+app = FastAPI(
+    title="Medical Image Classification API",
+    description="AI-powered medical image classification service",
+    version="1.0.0"
+)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+model = None
+class_names = {
+    0: "AKIEC",  # Actinic keratoses and intraepithelial carcinoma
+    1: "BCC",    # Basal cell carcinoma
+    2: "BKL",    # Benign keratosis-like lesions
+    3: "DF",     # Dermatofibroma
+    4: "MEL",    # Melanoma
+    5: "NV",     # Melanocytic nevi
+    6: "VASC"    # Vascular lesions
+}
+full_names = {
+    "AKIEC": "Actinic keratoses and intraepithelial carcinoma",
+    "BCC": "Basal cell carcinoma",
+    "BKL": "Benign keratosis-like lesions",
+    "DF": "Dermatofibroma",
+    "MEL": "Melanoma",
+    "NV": "Melanocytic nevi",
+    "VASC": "Vascular lesions"
+}
+UPLOAD_DIR = tempfile.mkdtemp()
+
+def load_model():
+    global model
+    model_path = "efficientnetv2s.h5"
+    if not os.path.exists(model_path):
+        raise FileNotFoundError(f"Model file not found: {model_path}")
+    model = tf.keras.models.load_model(model_path)
+    return model
+
+def predict_image(image_path):
+    global model
+    if model is None:
+        model = load_model()
+    img = Image.open(image_path).convert('RGB')
+    img = img.resize((224, 224))
+    img_array = np.array(img)
+    img_array = np.expand_dims(img_array, axis=0)
+    img_array = img_array.astype(np.float32) / 255.0
+    predictions = model.predict(img_array)
+    predicted_class = np.argmax(predictions[0])
+    confidence = float(predictions[0][predicted_class])
+    class_code = class_names[predicted_class]
+    class_full_name = full_names[class_code]
+    all_predictions = []
+    for i, prob in enumerate(predictions[0]):
+        all_predictions.append({
+            "label": class_names[i],
+            "confidence": float(prob)
+        })
+    all_predictions.sort(key=lambda x: x["confidence"], reverse=True)
+    return class_full_name, confidence, all_predictions
+
+@app.get("/health")
+async def health_check():
+    return {"status": "healthy", "service": "medical-image-classifier"}
+
+@app.post("/api/classify")
+async def classify_image_api(file: UploadFile = File(...)):
+    try:
+        if not file.content_type or not file.content_type.startswith('image/'):
+            raise HTTPException(status_code=400, detail="File must be an image")
+        unique_filename = f"{uuid.uuid4().hex}_{file.filename}"
+        file_path = os.path.join(UPLOAD_DIR, unique_filename)
+        with open(file_path, "wb") as buffer:
+            content = await file.read()
+            buffer.write(content)
+        label, confidence, all_predictions = predict_image(file_path)
+        os.remove(file_path)
+        formatted_predictions = []
+        for pred in all_predictions:
+            formatted_predictions.append({
+                "label": pred["label"],
+                "confidence": float(pred["confidence"]),
+                "confidence_percent": f"{pred['confidence'] * 100:.2f}%"
+            })
+        return JSONResponse(
+            status_code=200,
+            content={
+                "success": True,
+                "prediction": {
+                    "top_prediction": {
+                        "label": label,
+                        "confidence": float(confidence),
+                        "confidence_percent": f"{confidence * 100:.2f}%"
+                    },
+                    "all_predictions": formatted_predictions
+                }
+            }
+        )
+    except Exception as e:
+        if 'file_path' in locals() and os.path.exists(file_path):
+            os.remove(file_path)
+        raise HTTPException(status_code=500, detail=f"Classification failed: {str(e)}")
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8003)

requirements.txt

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+fastapi==0.104.1
+uvicorn[standard]==0.24.0
+tensorflow==2.10.0
+numpy==1.21.6
+Pillow==9.4.0
+python-multipart==0.0.5