Update app.py

app.py

@@ -1,53 +1,66 @@
-import numpy as np
-import pickle as pkl
-import tensorflow as tf
-from tensorflow.keras.applications.resnet50 import ResNet50,preprocess_input
-from tensorflow.keras.preprocessing import image
-from tensorflow.keras.layers import GlobalMaxPool2D
-
-from sklearn.neighbors import NearestNeighbors
-import os
-from numpy.linalg import norm
-import streamlit as st 
-
-st.header('Fashion Recommendation System')
-
-Image_features = pkl.load(open('Images_features.pkl','rb'))
-filenames = pkl.load(open('filenames.pkl','rb'))
-
-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
-model = ResNet50(weights='imagenet', include_top=False, input_shape=(224,224,3))
-model.trainable = False
-
-model = tf.keras.models.Sequential([model,
-                                   GlobalMaxPool2D()
-                                   ])
-neighbors = NearestNeighbors(n_neighbors=6, algorithm='brute', metric='euclidean')
-neighbors.fit(Image_features)
-upload_file = st.file_uploader("Upload Image")
-if upload_file is not None:
-    with open(os.path.join('upload', upload_file.name), 'wb') as f:
-        f.write(upload_file.getbuffer())
-    st.subheader('Uploaded Image')
-    st.image(upload_file)
-    input_img_features = extract_features_from_images(upload_file, model)
-    distance,indices = neighbors.kneighbors([input_img_features])
-    st.subheader('Recommended Images')
-    col1,col2,col3,col4,col5 = st.columns(5)
-    with col1:
-        st.image(filenames[indices[0][1]])
-    with col2:
-        st.image(filenames[indices[0][2]])
-    with col3:
-        st.image(filenames[indices[0][3]])
-    with col4:
-        st.image(filenames[indices[0][4]])
-    with col5:
-        st.image(filenames[indices[0][5]])
+import numpy as np
+import pickle as pkl
+import tensorflow as tf
+from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input
+from tensorflow.keras.preprocessing import image
+from tensorflow.keras.layers import GlobalMaxPool2D
+from sklearn.neighbors import NearestNeighbors
+import os
+from numpy.linalg import norm
+import streamlit as st 
+
+st.header('Fashion Recommendation System')
+
+# Load precomputed features
+Image_features = pkl.load(open('Images_features.pkl', 'rb'))
+filenames = pkl.load(open('filenames.pkl', 'rb'))
+
+# 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
+
+# Load ResNet50 model
+model = ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
+model.trainable = False
+model = tf.keras.models.Sequential([model, GlobalMaxPool2D()])
+
+# Nearest Neighbors Model
+neighbors = NearestNeighbors(n_neighbors=6, algorithm='brute', metric='euclidean')
+neighbors.fit(Image_features)
+
+# Upload Image
+upload_file = st.file_uploader("Upload Image")
+if upload_file is not None:
+    upload_path = os.path.join('/tmp', upload_file.name)  # Use /tmp instead of 'upload/'
+    
+    with open(upload_path, 'wb') as f:
+        f.write(upload_file.getbuffer())
+    
+    st.subheader('Uploaded Image')
+    st.image(upload_file)
+
+    # Extract features
+    input_img_features = extract_features_from_images(upload_path, model)
+
+    # Get recommendations
+    distance, indices = neighbors.kneighbors([input_img_features])
+
+    # Display Recommended Images
+    st.subheader('Recommended Images')
+    col1, col2, col3, col4, col5 = st.columns(5)
+    with col1:
+        st.image(filenames[indices[0][1]])
+    with col2:
+        st.image(filenames[indices[0][2]])
+    with col3:
+        st.image(filenames[indices[0][3]])
+    with col4:
+        st.image(filenames[indices[0][4]])
+    with col5:
+        st.image(filenames[indices[0][5]])