Spaces:
Sleeping
Sleeping
import gradio as gr | |
import tensorflow as tf | |
from huggingface_hub import from_pretrained_keras | |
from tensorflow.keras import mixed_precision | |
# Load your trained models | |
#model1 = from_pretrained_keras("ml-debi/EfficientNetB0-Food101") | |
#model2 = from_pretrained_keras("ml-debi/EfficientNetB0-Food101") | |
model1 = from_pretrained_keras("ml-debi/test_model") | |
with open('classes.txt', 'r') as f: | |
classes = [line.strip() for line in f] | |
# Add information about the models | |
model1_info = """ | |
### Model 1 Information | |
This model is based on the EfficientNetB0 architecture and was trained on the Food101 dataset. | |
""" | |
#model2_info = """ | |
#### Model 2 Information | |
#This model is based on the EfficientNetB0 architecture and was trained on augmented data, providing improved generalization. | |
#""" | |
examples = [["./examples/club_sandwich.jpg"], ["./examples/edamame.jpg"], ["./examples/eggs_benedict.jpg"]] | |
def preprocess(image): | |
print("before resize", image.shape) | |
image = tf.image.resize(image, [224, 224]) | |
image = tf.expand_dims(image, axis=0) | |
print("After expanddims", image.shape) | |
return image | |
def predict(image): | |
# Choose the model based on the dropdown selection | |
#print("---model_selection---", model_selection) # | |
#model = model1 if model_selection == "EfficentNetB0 Fine Tune" else model2 | |
#print(model.summary()) | |
if mixed_precision.global_policy() == "mixed_float16": | |
mixed_precision.set_global_policy(policy="float32") | |
image = preprocess(image) | |
print(mixed_precision.global_policy()) | |
prediction = model1.predict(image)[0] | |
print("model prediction", prediction) | |
confidences = {model1.config['id2label'][str(i)]: float(prediction[i]) for i in range(101)} | |
return confidences | |
iface = gr.Interface( | |
fn=predict, | |
inputs=[gr.Image()], | |
outputs=[gr.Label(num_top_classes=5)], | |
title="Food Vision Mini Project", | |
description=f"{model1_info}\n", | |
examples=examples | |
) | |
iface.launch() | |