import tensorflow as tf
import os
import numpy as np
from .processImg import process_image_file
def detectxray(imagepath):
    weightspath = "models/COVIDNet-CXR4-A"
    metaname = "model.meta"
    ckptname = "model-18540"
    n_classes = "3"
    in_tensorname = "input_1:0"
    out_tensorname = "norm_dense_1/Softmax:0"
    input_size = 480
    top_percent = 0.08
    mapping = {'normal': 0, 'pneumonia': 1, 'COVID-19': 2}
    inv_mapping = {0: 'normal', 1: 'pneumonia', 2: 'COVID-19'}
    mapping_keys = list(mapping.keys())
    sess = tf.Session()
    tf.get_default_graph()
    saver = tf.train.import_meta_graph(os.path.join(weightspath, metaname))
    saver.restore(sess, os.path.join(weightspath, ckptname))
    graph = tf.get_default_graph()
    image_tensor = graph.get_tensor_by_name(in_tensorname)
    pred_tensor = graph.get_tensor_by_name(out_tensorname)
    x = process_image_file(imagepath, input_size, top_percent=top_percent)
    x = x.astype('float32') / 255.0
    feed_dict = {image_tensor: np.expand_dims(x, axis=0)}
    pred = sess.run(pred_tensor, feed_dict=feed_dict)
    prediction = inv_mapping[pred.argmax(axis=1)[0]]
    pred_normal = round(pred[0][mapping['normal']], 3)
    pred_pneu = round(pred[0][mapping['pneumonia']], 3)
    pred_covid = round(pred[0][mapping['COVID-19']], 3)
    return prediction,pred_normal,pred_pneu,pred_covid