app.py

import gradio as gr
import base64
from io import BytesIO
from PIL import Image
import tensorflow as tf
import numpy as np

# Carregar o modelo TensorFlow Lite
interpreter = tf.lite.Interpreter(model_path="model_unquant.tflite")
interpreter.allocate_tensors()

input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
classes = ['Bastonete', 'Basófilo']

def predict_base64_image(base64_image):
    try:
        # Decodificar Base64 para imagem
        image_data = base64.b64decode(base64_image)
        image = Image.open(BytesIO(image_data)).convert("RGB")
        image_array = np.array(image).astype(np.float32) / 255.0
        image_array = np.expand_dims(image_array, axis=0)

        # Realizar a inferência
        interpreter.set_tensor(input_details[0]['index'], image_array)
        interpreter.invoke()
        output_data = interpreter.get_tensor(output_details[0]['index'])
        predicted_class_index = np.argmax(output_data)
        predicted_class_name = classes[predicted_class_index]
        predicted_confidence = output_data[0][predicted_class_index] * 100

        # Retornar o resultado
        return {"class": predicted_class_name, "confidence": f"{predicted_confidence:.2f}%"}
    except Exception as e:
        return {"error": str(e)}

# Configuração do Gradio
interface = gr.Interface(
    fn=predict_base64_image,
    inputs="text",  # Base64 será enviado como texto
    outputs="json",  # Retorna um JSON com a classe e a confiança
    api_name="/predict"
)

# Iniciar o servidor
interface.launch()