| from fastapi import FastAPI, UploadFile, File
|
| from fastapi.responses import JSONResponse
|
| import tensorflow as tf
|
| import numpy as np
|
| from PIL import Image
|
| import io
|
|
|
| app = FastAPI()
|
|
|
| interpreter = tf.lite.Interpreter(model_path="daun_padi_cnn_model.tflite")
|
| interpreter.allocate_tensors()
|
| input_details = interpreter.get_input_details()
|
| output_details = interpreter.get_output_details()
|
|
|
| CLASS_NAMES = [
|
| "Bacterial Leaf Blight", "Leaf Blast", "Leaf Scald",
|
| "Brown Spot", "Narrow Brown Spot", "Healthy"
|
| ]
|
|
|
| @app.post("/predict")
|
| async def predict(file: UploadFile = File(...)):
|
| image = Image.open(io.BytesIO(await file.read())).convert("RGB")
|
| image = image.resize((150, 150))
|
| img_array = np.expand_dims(np.array(image), axis=0).astype(np.float32) / 255.0
|
|
|
| interpreter.set_tensor(input_details[0]['index'], img_array)
|
| interpreter.invoke()
|
| prediction = interpreter.get_tensor(output_details[0]['index'])[0]
|
|
|
| predicted_index = int(np.argmax(prediction))
|
| predicted_label = CLASS_NAMES[predicted_index]
|
| confidence = float(np.max(prediction))
|
|
|
| return JSONResponse({
|
| "label": predicted_label,
|
| "confidence": round(confidence, 4),
|
| "probabilities": {
|
| CLASS_NAMES[i]: round(float(pred), 4) for i, pred in enumerate(prediction)
|
| }
|
| })
|
|
|
