Spaces:
Runtime error
Runtime error
File size: 2,474 Bytes
1723e60 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 |
import numpy as np
import gradio as gr
import tensorflow as tf
from PIL import Image, ImageDraw, ImageFont
# Function to load the modified model without recompiling
def load_modified_model(model_path):
return tf.keras.models.load_model(model_path)
# Load the trained model
print("Loading model...")
model = load_modified_model('denseNet121.h5')
print("Model loaded successfully.")
# Function to classify food vs. non-food image using the loaded model
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)
# 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', 'image_5.jpg']
# 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)
|