app.py

import streamlit as st
import pandas as pd
import numpy as np
import cv2
from PIL import Image
import tensorflow as tf

# Function to load the model
@st.cache_resource
def load_model():
    model = tf.keras.models.load_model('path_to_your_saved_model.h5')  # Provide the path to your model
    return model

# Function to preprocess the image
def preprocess_image(image):
    image = np.array(image.convert('RGB'))
    image = cv2.resize(image, (224, 224))  # Resize the image to the input shape required by your model
    image = image / 255.0  # Normalize the image
    image = np.expand_dims(image, axis=0)
    return image

# Function to predict the class
def predict(image, model):
    processed_image = preprocess_image(image)
    prediction = model.predict(processed_image)
    return prediction

# Main app
def main():
    st.title("Food Item Recognition and Estimation")
    st.write("Upload an image of a food item and the model will recognize the food item and estimate its calories.")

    model = load_model()

    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
    if uploaded_file is not None:
        image = Image.open(uploaded_file)
        st.image(image, caption='Uploaded Image.', use_column_width=True)
        
        st.write("")
        st.write("Classifying...")
        prediction = predict(image, model)
        
        st.write(f"Predicted class: {np.argmax(prediction)}")  # Update with your model's prediction logic

if __name__ == "__main__":
    main()