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