| from fastapi import FastAPI, File, UploadFile |
| from fastapi.middleware.cors import CORSMiddleware |
| import tensorflow as tf |
| import numpy as np |
| from PIL import Image |
| import json |
|
|
| app = FastAPI() |
|
|
| |
| app.add_middleware( |
| CORSMiddleware, |
| allow_origins=["*"], |
| allow_methods=["*"], |
| allow_headers=["*"], |
| ) |
|
|
| |
| model = tf.keras.models.load_model("models/bocchichan_model_inference.h5") |
|
|
| |
| with open("models/labelsbocchi.json", "r") as f: |
| labels = json.load(f) |
| label_keys = list(labels.keys()) |
|
|
| |
| def preprocess_image(image_file): |
| img = Image.open(image_file).resize((224, 224)).convert("RGB") |
| img_array = np.asarray(img).astype(np.float32) / 255.0 |
| return np.expand_dims(img_array, axis=0) |
|
|
| |
| @app.post("/predict/") |
| async def predict(file: UploadFile = File(...)): |
| img_array = preprocess_image(file.file) |
|
|
| output = model.predict(img_array) |
| pred_idx = int(np.argmax(output)) |
| confidence = float(np.max(output)) * 100 |
| label_id = label_keys[pred_idx] |
|
|
| return { |
| "label": labels[label_id], |
| "label_id": label_id, |
| "confidence": round(confidence, 2), |
| "threshold_check": "✅ Gambar terdeteksi!" if confidence >= 60 else "Kurang yakin, coba lagi!", |
| } |
|
|
| |
| @app.get("/") |
| def read_root(): |
| return { |
| "message": "Hello from NWSPD! Use POST /predict to classify image." |
| } |
|
|
