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import gradio as gr
import numpy as np
from PIL import Image
from tensorflow.keras import models
from tensorflow.keras.preprocessing.image import load_img
import tensorflow as tf
from hugsvision.inference.TorchVisionClassifierInference import TorchVisionClassifierInference
models_name = [
"VGG16",
"mobilenet_v2",
"DenseNet"
]
# open categories.txt in read mode
categories = open("categories.txt", "r")
labels = categories.readline().split(";")
# create a radio
radio = gr.inputs.Radio(models_name, default="DenseNet", type="value")
def predict_image(image, model_name):
print("======================")
print(type(image))
print(type(model_name))
print("==========")
print(image)
print(model_name)
print("======================")
if model_name == "DenseNet":
image = np.array(image) / 255
image = np.expand_dims(image, axis=0)
model = model = models.load_model("./models/" + model_name + "/model.h5")
pred = model.predict(image)
pred = dict((labels[i], "%.2f" % pred[0][i]) for i in range(len(labels)))
else:
image = Image.fromarray(np.uint8(image)).convert('RGB')
classifier = TorchVisionClassifierInference(
model_path = "./models/" + model_name
)
pred = classifier.predict_image(img=image, return_str=False)
for key in pred.keys():
pred[key] = pred[key]/100
print(pred)
return pred
image = gr.inputs.Image(shape=(300, 300), label="Upload Your Image Here")
label = gr.outputs.Label(num_top_classes=len(labels))
interface = gr.Interface(
fn=predict_image,
inputs=[image, radio],
outputs=label,
capture_session=True,
allow_flagging=False,
)
interface.launch() |