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import gradio as gr
import tensorflow as tf
import numpy as np
from PIL import Image
# Load your trained models
model1 = tf.keras.models.load_model('model/FoodVisionFineTuneAug/')
model2 = tf.keras.models.load_model('model/FoodVisionFineTune/')
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.
"""
def preprocess(image: Image.Image):
# Convert numpy array to PIL Image
image = Image.fromarray((image * 255).astype(np.uint8))
image = image.resize((224, 224)) # replace with the input size of your models
image = np.array(image)
# image = image / 255.0 # normalize if you've done so while training
image = np.expand_dims(image, axis=0)
return image
def predict(model_selection, image: Image.Image):
# Choose the model based on the dropdown selection
model = model1 if model_selection == "EfficentNetB0 Fine Tune" else model2
image = preprocess(image)
prediction = model.predict(image)
predicted_class = classes[np.argmax(prediction)]
confidence = np.max(prediction)
return predicted_class, confidence
iface = gr.Interface(
fn=predict,
inputs=[gr.Dropdown(["EfficentNetB0 Fine Tune", "EfficentNetB0 Fine Tune Augmented"]), gr.Image()],
outputs=[gr.Textbox(label="Predicted Class"), gr.Textbox(label="Confidence")],
title="Transfer Learning Mini Project",
description=f"{model1_info}\n\n{model2_info}",
)
iface.launch()
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