app.py

import streamlit as st
import numpy as np
from PIL import Image
from keras.models import load_model
from keras.saving import register_keras_serializable
import tensorflow as tf

@register_keras_serializable(package="Custom", name="f1_score")
def f1_score(y_true, y_pred):
    def recall(y_true, y_pred):
        true_positives = tf.keras.backend.sum(tf.keras.backend.round(tf.keras.backend.clip(y_true * y_pred, 0, 1)))
        possible_positives = tf.keras.backend.sum(tf.keras.backend.round(tf.keras.backend.clip(y_true, 0, 1)))
        recall = true_positives / (possible_positives + tf.keras.backend.epsilon())
        return recall

    def precision(y_true, y_pred):
        true_positives = tf.keras.backend.sum(tf.keras.backend.round(tf.keras.backend.clip(y_true * y_pred, 0, 1)))
        predicted_positives = tf.keras.backend.sum(tf.keras.backend.round(tf.keras.backend.clip(y_pred, 0, 1)))
        precision = true_positives / (predicted_positives + tf.keras.backend.epsilon())
        return precision

    precision = precision(y_true, y_pred)
    recall = recall(y_true, y_pred)
    return 2 * ((precision * recall) / (precision + recall + tf.keras.backend.epsilon()))

# st.title("Origami Model")
st.markdown(
        "<h1 style='color: #522258;'>Origami style prediction</h1>",
        unsafe_allow_html=True
    )
st.write("This application shows which origamist your folding style is the most similar.")

# Load your pre-trained model
@st.cache_resource 
def load_keras_model():
    custom_objects = {'f1_score': f1_score}
    model = load_model('resnet_50_ver2.keras', custom_objects=custom_objects)
    return model

try:
    classifier = load_keras_model()
except Exception as e:
    st.error(f"Error loading the model: {e}")

origamists = ['Beth Johnson', 'Chen Xiao', 'Choi Ju Young', 'Eric Joisel', 'Gen Hagiwara', 'Giang Dinh', 'Hideo Komatsu', 'Hojyo Takashi', 'Kaede Nakamura', 'Kamiya Satoshi', 'Katsuta Kyohei', 'Kei Watanabe', 'Kota Imai', 'Robert J Lang', 'Shuki Kato', 'Tran Trung Hieu']

if 'images' not in st.session_state:
    st.session_state['images'] = []
if 'predictions' not in st.session_state:
    st.session_state['predictions'] = []

left_column, right_column = st.columns([2,1])

with right_column:
    # Upload an image
    image_file = st.file_uploader("Upload an image", type=['jpg', 'jpeg', 'png'])

    st.markdown(
        "<h2 style='color: #C63C51;'>Combined Prediction:</h2>",
        unsafe_allow_html=True
    )

with left_column:
        st.markdown(
        "<h2 style='color: #C63C51;'>Predictions for each image:</h2>",
        unsafe_allow_html=True
        )

if image_file:
    image = Image.open(image_file)
    # Preprocess the image to fit the model input
    processed_image = image.resize((224, 224))
    processed_image = np.array(processed_image)
    if processed_image.shape[2] == 4:  # Check if image has an alpha channel
        processed_image = processed_image[..., :3]  # Drop the alpha channel
    processed_image = processed_image / 255.0  # Normalize the image

    st.session_state['images'].append(image)
    processed_image = np.expand_dims(processed_image, axis=0)  # Add batch dimension

    # Predict
    prediction = classifier.predict(processed_image)[0]
    st.session_state['predictions'].append(prediction)

if st.session_state['predictions']:
    combined_prediction = np.mean(st.session_state['predictions'], axis=0)
    normalized_prediction = combined_prediction / np.sum(combined_prediction)
    top_3_indices = np.argsort(normalized_prediction)[-3:][::-1]

    with right_column:
        for i in top_3_indices:
            label = origamists[i]
            confidence = normalized_prediction[i]
            st.write(f"{label}: {confidence:.2f}")

    with left_column:
       

        for idx, (img, pred) in enumerate(zip(st.session_state['images'], st.session_state['predictions'])):
            col1, col2 = st.columns([1, 2])  # Adjust column widths for better alignment
            with col1:
                st.image(img, caption=f"Image {idx + 1}", use_column_width=True)
            with col2:
                # st.write(f"Image {idx + 1} Predictions:")
                st.markdown(
                f"<h4 style='color: #D95F59;'>Image {idx + 1} Predictions:</h4>",
                unsafe_allow_html=True
                )

                top_3_indices = np.argsort(pred)[-3:][::-1]
                for i in top_3_indices:
                    label = origamists[i]
                    confidence = pred[i]
                    st.write(f"{label}, confidence: {confidence:.2f}")


st.markdown("""
    <style>
    .css-1kyxreq.e1fqkh3o1 {
        display: flex;
        align-items: center;
    }
    </style>
    """, unsafe_allow_html=True)