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from fastapi import FastAPI, File, UploadFile, HTTPException
from pydantic import BaseModel
import tensorflow as tf
import numpy as np
import cv2
app = FastAPI()
# Cargar el modelo TFLite
interpreter = tf.lite.Interpreter(model_path="model.tflite")
interpreter.allocate_tensors()
# Obtener detalles de las entradas y salidas del modelo
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
# Función para preprocesar la imagen
def preprocess_image(image):
image = cv2.resize(image, (224, 224))
image = image / 255.0
image = np.expand_dims(image, axis=0).astype(np.float32)
return image
# Ruta de predicción
@app.post("/predict/")
async def predict(file: UploadFile = File(...)):
try:
# Leer la imagen
image = await file.read()
image = cv2.imdecode(np.frombuffer(image, np.uint8), cv2.IMREAD_COLOR)
# Preprocesar la imagen
processed_image = preprocess_image(image)
# Realizar la predicción
interpreter.set_tensor(input_details[0]['index'], processed_image)
interpreter.invoke()
output_data = interpreter.get_tensor(output_details[0]['index'])
# Determinar la clase y la confianza
class_idx = np.argmax(output_data[0])
labels = ['Benign', 'Malignant']
result = labels[class_idx]
confidence = float(output_data[0][class_idx])
return {"class": result, "confidence": confidence}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
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