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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) | |