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app5.py

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+import streamlit as st
+import numpy as np
+from PIL import Image
+import onnxruntime as ort
+
+# === MODEL SETTINGS ===
+MODEL_PATH = "cnn_largefish_model.onnx"
+IMG_SIZE = 64
+
+CLASS_NAMES = [
+    "House Mackerel",
+    "Black Sea Sprat",
+    "Sea Bass",
+    "Red Mullet",
+    "Trout",
+    "Striped Red Mullet",
+    "Shrimp",
+    "Gilt-Head Bream",
+    "Red Sea Bream",
+]
+
+
+@st.cache_resource
+def load_session():
+    """
+    Laad het ONNX-model één keer in een ONNX Runtime sessie.
+    """
+    session = ort.InferenceSession(
+        MODEL_PATH,
+        providers=["CPUExecutionProvider"],
+    )
+    input_name = session.get_inputs()[0].name
+    return session, input_name
+
+
+def preprocess_image(image: Image.Image) -> np.ndarray:
+    """
+    Resize + normaliseer naar (1, 64, 64, 3) met waarden 0–1.
+    """
+    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)  # (1, 64, 64, 3)
+    return arr
+
+
+def predict(image: Image.Image):
+    """
+    Run één voorspelling via ONNX Runtime.
+    """
+    session, input_name = load_session()
+    x = preprocess_image(image)
+
+    # ONNX Runtime geeft een list terug; [0] is de output tensor
+    preds = session.run(None, {input_name: x})[0][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
+
+
+# === STREAMLIT UI ===
+st.set_page_config(page_title="Large-Scale Fish Classifier", page_icon="🐟")
+
+st.title("🐟 Large-Scale Fish Classifier")
+st.write("Upload een afbeelding van een vis en het model voorspelt de soort.")
+
+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)
+
+        st.subheader("Voorspelling")
+        st.write(f"**{pred_class}** met **{pred_conf:.2%}** zekerheid.")
+
+        st.subheader("Class probabilities")
+        st.bar_chart(
+            {CLASS_NAMES[i]: float(preds[i]) for i in range(len(CLASS_NAMES))}
+        )
+else:
+    st.info("➡️ Upload eerst een afbeelding (jpg/jpeg/png).")

cnn_largefish_model.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0363ff3a5a2b98a411c9ccb71b54ff5827f952bfbb2fc1d55bb2d1a49bc5144d
+size 12841638