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Runtime error
| import os | |
| os.environ["TF_USE_LEGACY_KERAS"] = "1" | |
| import streamlit as st | |
| import numpy as np | |
| from PIL import Image | |
| import tensorflow as tf | |
| from tensorflow.keras.models import load_model | |
| from tensorflow.keras.applications.efficientnet import preprocess_input | |
| # ----------------------------------- | |
| # CONFIG | |
| # ----------------------------------- | |
| IMG_SIZE = 300 | |
| CLASS_NAMES = ['cup', 'fork', 'glass', 'knife', 'plate', 'spoon'] | |
| # ----------------------------------- | |
| # MODEL LOADING | |
| # ----------------------------------- | |
| def load_model_file(): | |
| return keras.models.load_model("src/kitchen1.keras", compile=False) | |
| model = load_model_file() | |
| # ----------------------------------- | |
| # IMAGE PREPROCESS | |
| # ----------------------------------- | |
| def process_image(img): | |
| img = img.resize((IMG_SIZE, IMG_SIZE)) | |
| img = np.array(img) | |
| if img.ndim == 2: | |
| img = np.stack([img]*3, axis=-1) | |
| elif img.shape[-1] == 4: | |
| img = img[..., :3] | |
| img = np.expand_dims(img, 0) | |
| img = preprocess_input(img) | |
| return img | |
| # ----------------------------------- | |
| # UI LAYOUT | |
| # ----------------------------------- | |
| st.title("🍽️ Kitchenware Classifier") | |
| st.write("Upload an image to classify kitchen items using EfficientNetB3.") | |
| uploaded = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"]) | |
| if uploaded: | |
| img = Image.open(uploaded).convert("RGB") | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| st.image(img, caption="Uploaded Image", use_container_width=True) | |
| with col2: | |
| st.write("Classifying...") | |
| image_tensor = process_image(img) | |
| preds = model.predict(image_tensor) | |
| predicted_index = np.argmax(preds) | |
| predicted_class = CLASS_NAMES[predicted_index] | |
| confidence = float(np.max(preds)) | |
| st.success(f"Prediction: **{predicted_class.upper()}**") | |
| st.metric("Confidence", f"{confidence * 100:.2f}%") | |
| st.progress(int(confidence * 100)) |