Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,57 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import numpy as np
+from PIL import Image
+import joblib
+import os
+
+# ======================
+# LOAD MODEL
+# ======================
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+model = joblib.load(os.path.join(BASE_DIR, "model.pkl"))
+
+# ======================
+# CLASS NAMES
+# ======================
+class_names = [
+    "T-shirt/top", "Trouser", "Pullover", "Dress", "Coat",
+    "Sandal", "Shirt", "Sneaker", "Bag", "Ankle boot"
+]
+
+# ======================
+# PAGE
+# ======================
+st.set_page_config(page_title="Fashion MNIST", page_icon="👕")
+
+st.title("👕 Fashion MNIST Classifier")
+st.write("Upload a clothing image (28x28 grayscale)")
+
+# ======================
+# FILE UPLOAD
+# ======================
+file = st.file_uploader("Upload image", type=["png", "jpg", "jpeg"])
+
+if file is not None:
+
+    # IMAGE PREPROCESSING
+    img = Image.open(file).convert("L").resize((28, 28))
+    st.image(img, caption="Processed Image", width=150)
+
+    img = np.array(img)
+
+    # SCALE zoals in training
+    img = img / 255.0
+
+    # FLATTEN → (1, 784)
+    img = img.reshape(1, -1)
+
+    # ======================
+    # PREDICT
+    # ======================
+    prediction = model.predict(img)[0]
+    proba = model.predict_proba(img)
+
+    confidence = np.max(proba)
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+    st.success(f"Prediction: {class_names[prediction]}")
+    st.write(f"Confidence: {confidence:.2%}")