Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,92 @@
-import altair as alt
+import streamlit as st
 import numpy as np
 import pandas as pd
-import streamlit as st
+from PIL import Image
+import tensorflow as tf
+
+# --- PAS DIT AAN ALS JE ANDERE KLASSEN HEBT ---
+CLASS_NAMES = [
+    "Ajwa",
+    "Galaxy",
+    "Medjool",
+    "Meneifi",
+    "Nabtat Ali",
+    "Rutab",
+    "Shaishe",
+    "Sokari",
+    "Sugaey"
+]
+
+IMG_SIZE = (128, 128)  # dezelfde grootte als in je model
+
+
+@st.cache_resource
+def load_model():
+    """Laad het Keras-model één keer en cache het."""
+    model = tf.keras.models.load_model("date_fruit_model.h5")
+    return model
+
+
+def preprocess_image(image: Image.Image) -> np.ndarray:
+    """
+    Maakt een PIL-image klaar voor het model:
+    - resize
+    - naar np.array
+    - normaliseren [0,1]
+    - batch-dimensie toevoegen
+    """
+    image = image.convert("RGB")        # voor de zekerheid
+    image = image.resize(IMG_SIZE)
+    arr = np.array(image, dtype="float32") / 255.0
+    arr = np.expand_dims(arr, axis=0)   # shape: (1, 128, 128, 3)
+    return arr
+
+
+def main():
+    st.set_page_config(page_title="Date Fruit Classifier", layout="centered")
+
+    st.title("🍇 Date Fruit Classifier")
+    st.write("Upload een foto van een dadel en het model probeert de soort te raden.")
+
+    # Sidebar info
+    st.sidebar.header("Info")
+    st.sidebar.write("Model: Convolutional Neural Network (Keras/TensorFlow)")
+    st.sidebar.write(f"Aantal klassen: **{len(CLASS_NAMES)}**")
+
+    uploaded_file = st.file_uploader(
+        "Kies een afbeelding", type=["jpg", "jpeg", "png"]
+    )
+
+    if uploaded_file is not None:
+        # Toon de geüploade afbeelding
+        image = Image.open(uploaded_file)
+        st.image(image, caption="Geüploade afbeelding", use_container_width=True)
+
+
+        if st.button("Classificeer"):
+            with st.spinner("Bezig met voorspellen..."):
+                model = load_model()
+                input_arr = preprocess_image(image)
+                preds = model.predict(input_arr)[0]   # shape: (n_classes,)
+
+                pred_idx = int(np.argmax(preds))
+                pred_name = CLASS_NAMES[pred_idx]
+                pred_conf = float(preds[pred_idx])
+
+            st.subheader("🔎 Voorspelling")
+            st.write(f"**Klasse:** {pred_name}")
+            st.write(f"**Vertrouwen:** {pred_conf:.2%}")
+
+            # Probabilities per klasse
+            st.subheader("📊 Waarschijnlijkheid per klasse")
+            probs_df = pd.DataFrame({
+                "Klasse": CLASS_NAMES,
+                "Score": preds
+            })
+            probs_df = probs_df.set_index("Klasse")
+
+            st.bar_chart(probs_df)
+
 
-"""
-# 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])),
-    ))
+if __name__ == "__main__":
+    main()