| import streamlit as st |
| import tensorflow as tf |
| import numpy as np |
| from PIL import Image |
| import json |
|
|
| st.set_page_config( |
| page_title="CIFAR-10 Classifier", |
| page_icon="🖼️", |
| layout="centered", |
| ) |
| st.title("🚀 CIFAR-10 Image Classifier") |
| st.markdown("Upload an image and see what the model predicts!") |
|
|
| @st.cache_resource |
| def load_model_and_labels(): |
| model = tf.keras.models.load_model("models/cifar10_cnn.keras") |
| with open("models/labels_map.json", "r") as f: |
| labels = json.load(f) |
| return model, labels |
|
|
| model, labels = load_model_and_labels() |
|
|
|
|
| uploaded_file = st.file_uploader("Upload an image (PNG/JPG)", type=["png","jpg","jpeg"]) |
|
|
| if uploaded_file: |
| img = Image.open(uploaded_file).convert("RGB") |
| st.image(img, caption="Uploaded Image", use_column_width=False) |
|
|
| def preprocess_image(img): |
| img = img.resize((32,32)) |
| img = np.array(img)/255.0 |
| return img |
|
|
| x = preprocess_image(img) |
|
|
|
|
| with st.spinner("Predicting..."): |
| x_input = x.reshape(1,32,32,3) |
| preds = model.predict(x_input)[0] |
| top_idx = preds.argsort()[-3:][::-1] |
|
|
| st.subheader("✅ Prediction") |
| st.write(f"**Top-1:** {labels[str(top_idx[0])]} ({preds[top_idx[0]]*100:.2f}%)") |
|
|
| st.subheader("📊 Top-3 Predictions") |
| for i in top_idx: |
| st.write(f"{labels[str(i)]}: {preds[i]*100:.2f}%") |
|
|
| st.subheader("📈 All Class Probabilities") |
| st.bar_chart({labels[str(i)]: float(preds[i]) for i in range(len(labels))}) |
|
|
| else: |
| st.info("Upload an image to see predictions.") |
|
|
