Upload 2 files

app1.py

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+import streamlit as st
+import numpy as np
+from PIL import Image
+
+import tensorflow as tf
+from tensorflow.keras.models import load_model
+from tensorflow.keras.applications.resnet50 import preprocess_input
+
+# -----------------------------------------------------------
+# 1. Config
+# -----------------------------------------------------------
+
+IMG_SIZE = (128, 128)  # zelfde als bij training
+MODEL_PATH = "indian_birds_resnet50.h5"   # <-- pas aan indien nodig
+
+# VUL HIER JOUW LABELS IN (dezelfde volgorde als tijdens training)
+labels = [
+    "Asian Green Bee-Eater",
+    "Brown-Headed Barbet",
+    "Cattle Egret",
+    "Common Kingfisher",
+    "Common Myna",
+    "Common Rosefinch",
+    "Common Tailorbird",
+    "Coppersmith Barbet",
+    "Forest Wagtail",
+    "Gray Wagtail",
+    "Hoopoe",
+    "House Crow",
+    "Indian Grey Hornbill",
+    "Indian Peafowl",
+    "Indian Pitta",
+    "Indian Roller",
+    "Jungle Babbler",
+    "Northern Lapwing",
+    "Red Wattled Lapwing",
+    "Ruddy Shelduck",
+    "Rufous Treepie",
+    "Sarus Crane",
+    "White Wagtail",
+    "White-Breasted Kingfisher",
+    "White-Breasted Waterhen"
+]
+
+label_map = {name: i for i, name in enumerate(labels)}
+inv_label_map = {v: k for k, v in label_map.items()}
+
+# -----------------------------------------------------------
+# 2. Model laden (gecached)
+# -----------------------------------------------------------
+
+@st.cache_resource
+def load_bird_model():
+    model = load_model(MODEL_PATH)
+    return model
+
+model = load_bird_model()
+
+# -----------------------------------------------------------
+# 3. Hulpfuncties
+# -----------------------------------------------------------
+
+def preprocess_image(img: Image.Image) -> np.ndarray:
+    """Resize + preprocess zoals bij training."""
+    img = img.convert("RGB").resize(IMG_SIZE)
+    x = np.array(img).astype("float32")
+    x = preprocess_input(x)         # ResNet50 preprocessing
+    x = np.expand_dims(x, axis=0)   # batch-dimensie
+    return x
+
+def predict_image(img: Image.Image):
+    x = preprocess_image(img)
+    probs = model.predict(x, verbose=0)[0]      # vorm (25,)
+    pred_id = int(np.argmax(probs))
+    pred_label = inv_label_map[pred_id]
+    pred_prob = float(probs[pred_id])
+
+    # top-3
+    top3_ids = probs.argsort()[-3:][::-1]
+    top3 = [(inv_label_map[int(i)], float(probs[i])) for i in top3_ids]
+
+    return pred_label, pred_prob, top3
+
+# -----------------------------------------------------------
+# 4. Streamlit UI
+# -----------------------------------------------------------
+
+st.set_page_config(page_title="Indian Bird Classifier", layout="centered")
+
+st.title("🕊️ Indian Bird Species Classifier")
+st.write(
+    "Upload een vogelafbeelding uit de *25 Indian Bird Species* dataset "
+    "en het model (ResNet50) voorspelt de soort."
+)
+
+uploaded_file = st.file_uploader(
+    "Kies een afbeelding (.jpg, .png)",
+    type=["jpg", "jpeg", "png"]
+)
+
+if uploaded_file is not None:
+    # Afbeelding tonen
+    image = Image.open(uploaded_file)
+
+    st.image(image, caption="Geüploade afbeelding", use_column_width=True)
+
+    if st.button("🔮 Voorspel vogelsoort"):
+        with st.spinner("Model is aan het voorspellen..."):
+            pred_label, pred_prob, top3 = predict_image(image)
+
+        st.success(f"Voorspelling: **{pred_label}** ({pred_prob:.2%} zekerheid)")
+
+        st.subheader("Top 3 voorspellingen")
+        for name, p in top3:
+            st.write(f"- {name}: **{p:.2%}**")
+
+else:
+    st.info("Upload een afbeelding om te starten.")

indian_birds_resnet50.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f5fa69d6edb2bb2551c9a35645c95978bad1ecb6724f00570a85e37911443191
+size 216754364