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import streamlit as st
import tensorflow as tf
from tensorflow.keras.preprocessing import image
import numpy as np
import os
# Load the model
MODEL_PATH = "/home/petpooja-504/Desktop/cnn/final_model.keras"
model = tf.keras.models.load_model(MODEL_PATH)
# Define the class names directly for the Food-101 dataset
CLASS_NAMES = [
'apple_pie', 'baby_back_ribs', 'baklava', 'beef_carpaccio', 'beef_tartare', 'beet_salad', 'beignets',
'bibimbap', 'bread_pudding', 'breakfast_burrito', 'bruschetta', 'caesar_salad', 'cannoli', 'caprese_salad',
'carrot_cake', 'ceviche', 'cheese_plate', 'cheesecake', 'chicken_curry', 'chicken_quesadilla',
'chicken_wings', 'chocolate_cake', 'chocolate_mousse', 'churros', 'clam_chowder', 'club_sandwich',
'crab_cakes', 'creme_brulee', 'croque_madame', 'cup_cakes', 'deviled_eggs', 'donuts', 'dumplings',
'edamame', 'eggs_benedict', 'escargots', 'falafel', 'filet_mignon', 'fish_and_chips', 'foie_gras',
'french_fries', 'french_onion_soup', 'french_toast', 'fried_calamari', 'fried_rice', 'frozen_yogurt',
'garlic_bread', 'gnocchi', 'greek_salad', 'grilled_cheese_sandwich', 'grilled_salmon', 'guacamole',
'gyoza', 'hamburger', 'hot_and_sour_soup', 'hot_dog', 'huevos_rancheros', 'hummus', 'ice_cream',
'lasagna', 'lobster_bisque', 'lobster_roll_sandwich', 'macaroni_and_cheese', 'macarons', 'miso_soup',
'mussels', 'nachos', 'omelette', 'onion_rings', 'oysters', 'pad_thai', 'paella', 'pancakes', 'panna_cotta',
'peking_duck', 'pho', 'pizza', 'pork_chop', 'poutine', 'prime_rib', 'pulled_pork_sandwich', 'ramen',
'ravioli', 'red_velvet_cake', 'risotto', 'samosa', 'sashimi', 'scallops', 'seaweed_salad', 'shrimp_and_grits',
'spaghetti_bolognese', 'spaghetti_carbonara', 'spring_rolls', 'steak', 'strawberry_shortcake', 'sushi',
'tacos', 'takoyaki', 'tiramisu', 'tuna_tartare', 'waffles'
]
# Define the function to predict the image
# Define the function to predict the image
def predict_image(img_path):
# Load and preprocess the image
img = image.load_img(img_path, target_size=(224, 224)) # Resize to match model's expected input size
img_array = image.img_to_array(img) # Convert image to array
img_array = np.expand_dims(img_array, axis=0) # Add batch dimension
img_array = img_array / 255.0 # Normalize the image as done during training
# Make prediction
predictions = model.predict(img_array)
# Get prediction probabilities
prediction_probs = predictions[0] # Prediction probabilities
predicted_class_index = np.argmax(prediction_probs)
predicted_class = CLASS_NAMES[predicted_class_index] # Fetch the class name
return predicted_class
# Streamlit UI components
st.title("Food-101 Classification Model")
st.write("Upload an image of food to predict its class.")
# Upload image
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
# Display the uploaded image
st.image(uploaded_file, caption="Uploaded Image", use_column_width=True)
# Save the image temporarily
img_path = "uploaded_image.jpg"
with open(img_path, "wb") as f:
f.write(uploaded_file.getbuffer())
# Make prediction
predicted_class = predict_image(img_path)
# Display the predicted class
st.write(f"Predicted Class: {predicted_class}")
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