Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,89 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+# ====== MODEL SETTINGS ======
+MODEL_PATH = "cnn_largefish_model.h5"  # op HuggingFace gewoon in de root neerzetten
+IMG_SIZE = 64  # jouw resize in Kaggle
+
+CLASS_NAMES = [
+    'House Mackerel',
+    'Black Sea Sprat',
+    'Sea Bass',
+    'Red Mullet',
+    'Trout',
+    'Striped Red Mullet',
+    'Shrimp',
+    'Gilt-Head Bream',
+    'Red Sea Bream'
+]
+
+
+# ====== FUNCTIES ======
+@st.cache_resource
+def load_model():
+    """Laad het Keras-model één keer en cache het."""
+    model = tf.keras.models.load_model(MODEL_PATH)
+    return model
+
+
+def preprocess_image(image: Image.Image) -> np.ndarray:
+    """Resize + normaliseer afbeelding naar hetzelfde formaat als training."""
+    image = image.convert("RGB")
+    image = image.resize((IMG_SIZE, IMG_SIZE))
+    arr = np.array(image).astype("float32") / 255.0
+    arr = np.expand_dims(arr, axis=0)  # shape: (1, 64, 64, 3)
+    return arr
 
-"""
-# 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).
+def predict_image(model, image: Image.Image):
+    """Voorspel klasse + probabilities voor één afbeelding."""
+    x = preprocess_image(image)
+    preds = model.predict(x)[0]  # shape: (9,)
+    pred_idx = int(np.argmax(preds))
+    pred_class = CLASS_NAMES[pred_idx]
+    pred_conf = float(preds[pred_idx])
+    return pred_class, pred_conf, preds
 
-In the meantime, below is an example of what you can do with just a few lines of code:
+
+# ====== STREAMLIT UI ======
+st.set_page_config(
+    page_title="Fish Classifier",
+    page_icon="🐟",
+)
+
+st.title("🐟 Large-Scale Fish Classifier")
+st.write(
+    """
+Upload een afbeelding van een vis uit de dataset 
+en het model voorspelt de soort.
 """
+)
+
+# Model alvast laden (toont spinner)
+with st.spinner("Model laden..."):
+    model = load_model()
+
+uploaded_file = st.file_uploader(
+    "Upload een afbeelding", type=["jpg", "jpeg", "png"]
+)
+
+if uploaded_file is not None:
+    image = Image.open(uploaded_file)
+    st.image(image, caption="Geüploade afbeelding", use_column_width=True)
+
+    if st.button("Classify"):
+        with st.spinner("Bezig met voorspellen..."):
+            pred_class, pred_conf, preds = predict_image(model, image)
+
+        st.subheader("Voorspelling")
+        st.write(f"**{pred_class}** met **{pred_conf:.2%}** zekerheid.")
+
+        # Probabilities plotten als bar chart
+        prob_dict = {CLASS_NAMES[i]: float(preds[i]) for i in range(len(CLASS_NAMES))}
+        st.subheader("Class probabilities")
+        st.bar_chart(prob_dict)
 
-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])),
-    ))
+else:
+    st.info("➡️ Upload eerst een afbeelding (jpg/jpeg/png).")