Update app.py

app.py

@@ -1,71 +1,55 @@
-import streamlit as st
-import tensorflow as tf
-import numpy as np
-from PIL import Image
-import json
-
-# -----------------------------
-# CONFIG
-# -----------------------------
-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!")
-
-# -----------------------------
-# LOAD MODEL AND LABELS
-# -----------------------------
-@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()
-
-# -----------------------------
-# IMAGE UPLOAD
-# -----------------------------
-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)
-
-    # -----------------------------
-    # PREPROCESS IMAGE
-    # -----------------------------
-    def preprocess_image(img):
-        img = img.resize((32,32))  # CIFAR-10 input size
-        img = np.array(img)/255.0
-        return img
-
-    x = preprocess_image(img)
-
-    # -----------------------------
-    # PREDICTION
-    # -----------------------------
-    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}%")
-
-    # -----------------------------
-    # BAR CHART
-    # -----------------------------
-    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.")
+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.")