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import numpy as np
import gradio as gr
import tensorflow as tf
from PIL import Image, ImageDraw, ImageFont
model = tf.keras.models.load_model('denseNet121.h5')
def classify_food_vs_nonfood(image):
try:
# Preprocess image
#image_size = (224, 224)
#image = image.resize(image_size)
#image_np = np.array(image) / 255.0
#image_np_expanded = np.expand_dims(image_np, axis=0)
image_np_expanded = np.expand_dims(np.array(image.resize((224, 224))) / 255.0, axis=0)
# Make prediction
prediction = model.predict(image_np_expanded)
final_prediction = np.argmax(prediction[0])
# Display result
results = {0: 'Food', 1: 'Non Food'}
label = results[final_prediction]
# Create a draw object
draw = ImageDraw.Draw(image)
# Specify font and size
font = ImageFont.load_default()
# Get text size
text_font = ImageFont.truetype("Hack-Regular.ttf", 24)
text_bbox = draw.textbbox((0, 0), label, font=text_font)
text_size = (text_bbox[2] - text_bbox[0], text_bbox[3] - text_bbox[1])
# Calculate text position
text_position = ((image_size[0] - text_size[0]) // 2, 10)
# Add text to the image
draw.text(text_position, label, fill=(255, 0, 0), font=text_font)
# Return modified image
return image
except Exception as e:
print("Error processing image:", e)
# Define inputs for Gradio interface
image_input = gr.inputs.Image(shape=(224, 224), type="pil")
# Define example images as file paths
ex_image_paths = ['image_1.jpeg', 'image_2.jpeg', 'image_3.jpeg', 'image_4.jpg',
'Panama-early-c01_resize.jpg', 'Tomato_YLCV.jpg', 'downy-mildew-disease.jpg', 'jounalism-plant-photo.jpg',
'pexels_facemask.jpg', 'rice_leaf_healthy.jpg', 'rice_leaf_unhealthy.png', '00000007.jpg', '000000000009.jpg',
"A-cacao-tree-affected-by-witches’-broom.jpg", "DLSU_logo.png"]
# Launch Gradio interface with example images
food_vs_nonfood_interface = gr.Interface(classify_food_vs_nonfood,
inputs=image_input,
outputs="image",
title="Food vs NonFood Classifier",
description="Upload an image to classify whether it's food or non-food.",
examples=ex_image_paths)
food_vs_nonfood_interface.launch(inline=False)
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