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import gradio as gr
import pathlib
import numpy as np
import tensorflow
import PIL.Image
from PIL import Image
class Model:
def __init__(self, model_filepath):
self.graph_def = tensorflow.compat.v1.GraphDef()
self.graph_def.ParseFromString(model_filepath.read_bytes())
input_names, self.output_names = self._get_graph_inout(self.graph_def)
assert len(input_names) == 1
self.input_name = input_names[0]
self.input_shape = self._get_input_shape(self.graph_def, self.input_name)
def predict(self, image_filepath):
image = Image.fromarray(image_filepath).resize(self.input_shape)
input_array = np.array(image, dtype=np.float32)[np.newaxis, :, :, :]
with tensorflow.compat.v1.Session() as sess:
tensorflow.import_graph_def(self.graph_def, name='')
out_tensors = [sess.graph.get_tensor_by_name(o + ':0') for o in self.output_names]
outputs = sess.run(out_tensors, {self.input_name + ':0': input_array})
return {name: outputs[i] for i, name in enumerate(self.output_names)}
@staticmethod
def _get_graph_inout(graph_def):
input_names = []
inputs_set = set()
outputs_set = set()
for node in graph_def.node:
if node.op == 'Placeholder':
input_names.append(node.name)
for i in node.input:
inputs_set.add(i.split(':')[0])
outputs_set.add(node.name)
output_names = list(outputs_set - inputs_set)
return input_names, output_names
@staticmethod
def _get_input_shape(graph_def, input_name):
for node in graph_def.node:
if node.name == input_name:
return [dim.size for dim in node.attr['shape'].shape.dim][1:3]
def print_outputs(outputs):
labelopen = open("labels.txt", 'r')
labels = [line.split(',') for line in labelopen.readlines()]
outputs = list(outputs.values())[0]
return str(labels[outputs[0].argmax()][0])
def main(gambar):
m = pathlib.Path("model.pb")
#i = pathlib.Path(gambar)
model = Model(m)
outputs = model.predict(gambar)
return print_outputs(outputs)
demo = gr.Interface(main, gr.Image(shape=(500, 500)), "text")
demo.launch() |