Upload main files

Images_features2.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2a707cde72a062b48a907de8ed64aa0667d879259f9f0b8f1ec8ed653c6ce9f4
+size 365621870

app.py

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+import streamlit as st
+import pickle as pkl
+from PIL import Image
+from sklearn.neighbors import NearestNeighbors
+from tensorflow.keras.models import load_model
+from tensorflow.keras.applications.resnet50 import preprocess_input
+from tensorflow.keras.preprocessing import image
+import os
+from numpy.linalg import norm
+import numpy as np
+
+# Load necessary files and model
+filenames = pkl.load(open('filenames2.pkl', 'rb'))
+Image_features = pkl.load(open('Images_features2.pkl', 'rb'))
+neighbors = NearestNeighbors(n_neighbors=6, algorithm='brute', metric='euclidean')
+neighbors.fit(Image_features)
+model = load_model('model.h5')
+
+# Feature extraction function
+def extract_features_from_images(image_path, model):
+    img = image.load_img(image_path, target_size=(224, 224))
+    img_array = image.img_to_array(img)
+    img_expand_dim = np.expand_dims(img_array, axis=0)
+    img_preprocess = preprocess_input(img_expand_dim)
+    result = model.predict(img_preprocess).flatten()
+    norm_result = result / norm(result)
+    return norm_result
+
+# Custom CSS for background and title
+st.markdown(
+    """
+    <style>
+    body {
+        background-color: #f0f8ff; /* Soft light blue background */
+    }
+    .title {
+        color: #1f7a8c; /* Teal color */
+        font-family: 'Arial Black', sans-serif;
+        text-align: center;
+    }
+    </style>
+    """,
+    unsafe_allow_html=True,
+)
+
+# Streamlit UI
+st.markdown('<h1 class="title">Fashion Deep Learning Classification 👗</h1>', unsafe_allow_html=True)
+st.write('Upload a clothing image, and the model recommends similar products.')
+
+file = st.file_uploader('Upload an Image', type=['jpg', 'jpeg', 'png'])
+
+if file is not None:
+    # Display uploaded image with reduced size
+    img = Image.open(file)
+    st.image(img, caption='Uploaded Image', width=200, use_column_width=False)
+
+    # Extract features from the uploaded image
+    input_image = extract_features_from_images(file, model)
+
+    # Get recommendations
+    distance, indices = neighbors.kneighbors([input_image])
+    st.subheader("Recommended Products:")
+
+    # Display recommendations in a grid layout
+    cols = st.columns(3)  # 3 images per row
+    for i, idx in enumerate(indices[0]):
+        product_path = filenames[idx]
+        st.write(product_path)
+        product_image = Image.open(product_path)
+        
+        # Assign columns dynamically
+        with cols[i % 3]:
+            st.image(product_image, caption=f"Product {i + 1}", use_column_width=True)

filenames2.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f7076602ce29da942074fab43bd8af025e5f0f7fe3ec5b7ed0166bd15f8d212b
+size 837463

model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7cf01a6bcea34afc501604243ab531ae715c4ffed4d9d974041fc0751df909b4
+size 94737040

requirements.txt

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+streamlit
+tensorflow
+scikit-learn