| import os
|
| import pickle as pkl
|
| from pathlib import Path
|
|
|
| import numpy as np
|
| import streamlit as st
|
|
|
| import tensorflow as tf
|
| from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input
|
| from tensorflow.keras.layers import GlobalMaxPool2D
|
| from tensorflow.keras.preprocessing import image as kimage
|
|
|
| from sklearn.neighbors import NearestNeighbors
|
| from numpy.linalg import norm
|
|
|
|
|
|
|
|
|
|
|
| def extract_features(image_path: str, model: tf.keras.Model) -> np.ndarray:
|
| """Return L2-normalized 2048-d embedding from an image path."""
|
| img = kimage.load_img(image_path, target_size=(224, 224))
|
| arr = kimage.img_to_array(img)
|
| arr = np.expand_dims(arr, axis=0)
|
| arr = preprocess_input(arr)
|
| feat = model.predict(arr, verbose=0).flatten()
|
| return feat / (norm(feat) + 1e-10)
|
|
|
|
|
| @st.cache_resource
|
| def load_resnet():
|
| base = ResNet50(weights="imagenet", include_top=False, input_shape=(224, 224, 3))
|
| base.trainable = False
|
| return tf.keras.Sequential([base, GlobalMaxPool2D()])
|
|
|
|
|
| @st.cache_resource
|
| def load_data(features_path: str, filenames_path: str):
|
| with open(features_path, "rb") as f:
|
| features = pkl.load(f)
|
| with open(filenames_path, "rb") as f:
|
| filenames = pkl.load(f)
|
|
|
| features = np.array(features, dtype="float32")
|
|
|
|
|
| if len(filenames) != features.shape[0]:
|
| st.warning(
|
| f"Mismatch gevonden: len(filenames)={len(filenames)} maar features={features.shape[0]}. "
|
| f"Ik gebruik automatisch de eerste {features.shape[0]} filenames."
|
| )
|
| filenames = filenames[: features.shape[0]]
|
|
|
| return features, filenames
|
|
|
|
|
| @st.cache_resource
|
| def fit_nn(features: np.ndarray, n_neighbors: int):
|
| nn = NearestNeighbors(n_neighbors=n_neighbors, algorithm="brute", metric="euclidean")
|
| nn.fit(features)
|
| return nn
|
|
|
|
|
| def resolve_path(p: str, images_dir: str) -> str:
|
| """Open the image even if filenames.pkl contains only basenames."""
|
| if os.path.exists(p):
|
| return p
|
| return os.path.join(images_dir, p)
|
|
|
|
|
|
|
|
|
|
|
| st.set_page_config(page_title="Fashion Recommender", layout="wide")
|
| st.title("🧥 Fashion Deep Learning Recommender (mini-set ok)")
|
|
|
| with st.sidebar:
|
| st.header("📁 Bestanden")
|
| images_dir = st.text_input("Images map", value="images")
|
| features_pkl = st.text_input("Features pickle", value="Images_features.pkl")
|
| filenames_pkl = st.text_input("Filenames pickle", value="filenames.pkl")
|
|
|
| st.divider()
|
| st.header("⚙️ Aanbevelingen")
|
| k = st.slider("Aantal aanbevelingen", 1, 20, 5)
|
| show_distance = st.checkbox("Toon euclidean afstand", value=False)
|
|
|
| if not Path(features_pkl).exists() or not Path(filenames_pkl).exists():
|
| st.error(
|
| "Ik kan je .pkl bestanden niet vinden. "
|
| "Zet `Images_features.pkl` en `filenames.pkl` in dezelfde map als dit script, "
|
| "of geef het juiste pad op in de sidebar."
|
| )
|
| st.stop()
|
|
|
| features, filenames = load_data(features_pkl, filenames_pkl)
|
|
|
| st.caption(f"Geladen: **{len(filenames)}** items | embedding dim: **{features.shape[1]}**")
|
|
|
|
|
| n_neighbors = min(k + 1, features.shape[0])
|
| neighbors = fit_nn(features, n_neighbors=n_neighbors)
|
| model = load_resnet()
|
|
|
| tab1, tab2 = st.tabs(["Kies uit dataset", "Upload een foto"])
|
|
|
|
|
|
|
|
|
| with tab1:
|
| st.subheader("Kies een item uit de dataset")
|
|
|
| q = st.text_input("Zoek op bestandsnaam (bv. 16871)", value="")
|
| if q.strip():
|
| matches = [f for f in filenames if q in os.path.basename(f)]
|
| else:
|
| matches = filenames
|
|
|
| if not matches:
|
| st.info("Geen matches. Probeer een andere zoekterm.")
|
| else:
|
|
|
| selected = st.selectbox("Selecteer", options=matches[:5000])
|
| selected_path = resolve_path(selected, images_dir)
|
|
|
| if not os.path.exists(selected_path):
|
| st.error(f"Bestand niet gevonden: {selected_path}")
|
| st.stop()
|
|
|
| colA, colB = st.columns([1, 2], gap="large")
|
| with colA:
|
| st.write("**Gekozen item**")
|
| st.image(selected_path, use_container_width=True)
|
| st.caption(os.path.basename(selected_path))
|
|
|
| with colB:
|
| st.write("**Aanbevelingen**")
|
| query_vec = extract_features(selected_path, model)
|
| dists, idxs = neighbors.kneighbors([query_vec], n_neighbors=n_neighbors)
|
|
|
| recs = []
|
| for d, idx in zip(dists[0], idxs[0]):
|
| p = resolve_path(filenames[idx], images_dir)
|
|
|
|
|
| try:
|
| if os.path.abspath(p) == os.path.abspath(selected_path):
|
| continue
|
| except Exception:
|
| pass
|
|
|
| if os.path.exists(p):
|
| recs.append((p, float(d)))
|
| if len(recs) >= k:
|
| break
|
|
|
| cols = st.columns(min(5, max(1, len(recs))))
|
| for i, (p, d) in enumerate(recs):
|
| with cols[i % len(cols)]:
|
| st.image(p, use_container_width=True)
|
| st.caption(os.path.basename(p))
|
| if show_distance:
|
| st.caption(f"afstand: {d:.4f}")
|
|
|
|
|
|
|
|
|
| with tab2:
|
| st.subheader("Upload een afbeelding en krijg vergelijkbare items")
|
| up = st.file_uploader("Upload een JPG/PNG", type=["jpg", "jpeg", "png"])
|
| if up is not None:
|
| tmp_dir = Path(".streamlit_tmp")
|
| tmp_dir.mkdir(exist_ok=True)
|
| tmp_path = tmp_dir / up.name
|
| tmp_path.write_bytes(up.getvalue())
|
|
|
| colA, colB = st.columns([1, 2], gap="large")
|
| with colA:
|
| st.write("**Geüpload**")
|
| st.image(str(tmp_path), use_container_width=True)
|
| st.caption(up.name)
|
|
|
| with colB:
|
| st.write("**Aanbevelingen**")
|
| query_vec = extract_features(str(tmp_path), model)
|
| dists, idxs = neighbors.kneighbors([query_vec], n_neighbors=n_neighbors)
|
|
|
| recs = []
|
| for d, idx in zip(dists[0], idxs[0]):
|
| p = resolve_path(filenames[idx], images_dir)
|
| if os.path.exists(p):
|
| recs.append((p, float(d)))
|
| if len(recs) >= k:
|
| break
|
|
|
| cols = st.columns(min(5, max(1, len(recs))))
|
| for i, (p, d) in enumerate(recs):
|
| with cols[i % len(cols)]:
|
| st.image(p, use_container_width=True)
|
| st.caption(os.path.basename(p))
|
| if show_distance:
|
| st.caption(f"afstand: {d:.4f}")
|
|
|
