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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+models/cifar10_cnn.keras filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,13 +0,0 @@
----
-title: CNN
-emoji: 📊
-colorFrom: red
-colorTo: yellow
-sdk: gradio
-sdk_version: 6.0.2
-app_file: app.py
-pinned: false
-short_description: cifar10_cnn
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,71 @@
+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.")

models/cifar10_cnn.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3d78265e9624809fc850345439766c6f49bbfc52a3c06c16ab7e84b236209a68
+size 16211390

models/labels_map.json

@@ -0,0 +1,12 @@
+{
+  "0": "airplane",
+  "1": "automobile",
+  "2": "bird",
+  "3": "cat",
+  "4": "deer",
+  "5": "dog",
+  "6": "frog",
+  "7": "horse",
+  "8": "ship",
+  "9": "truck"
+}

requirements.txt

@@ -0,0 +1,4 @@
+tensorflow
+streamlit
+numpy
+pillow