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import streamlit as st
import numpy as np
import cv2
from PIL import Image
import tensorflow as tf
import os
# Function to load the model
@st.cache_resource
def load_model():
model_path = 'models/my_model.h5' # Path relative to the script
if not os.path.isfile(model_path):
st.error(f"Model file not found: {model_path}")
return None
try:
model = tf.keras.models.load_model(model_path)
st.success("Model loaded successfully!")
return model
except Exception as e:
st.error(f"Error loading model: {e}")
return None
# Function to preprocess the image
def preprocess_image(image):
image = np.array(image.convert('RGB'))
image = cv2.resize(image, (224, 224))
image = image / 255.0
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()
if model is None:
st.write("Model could not be loaded.")
return
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...")
try:
prediction = predict(image, model)
predicted_class = np.argmax(prediction, axis=1)[0]
st.write(f"Predicted class: {predicted_class}")
except Exception as e:
st.error(f"Error in prediction: {e}")
if __name__ == "__main__":
main()
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