Update app.py

app.py

@@ -3,7 +3,7 @@ import os
 from PIL import Image
 import numpy as np
 import pickle
-import tensorflow
+import tensorflow as tf
 from tensorflow.keras.preprocessing import image
 from tensorflow.keras.layers import GlobalMaxPooling2D
 from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input
@@ -41,10 +41,12 @@ def save_uploaded_file(uploaded_file):
 def show_dashboard():
     st.header("Fashion Recommender System")
     chatbot = Chatbot()
+    chatbot.load_data()
+
     # Load ResNet model for image feature extraction
     model = ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
     model.trainable = False
-    model = tensorflow.keras.Sequential([
+    model = tf.keras.Sequential([
         model,
         GlobalMaxPooling2D()
     ])
@@ -66,18 +68,18 @@ def show_dashboard():
             # Recommendation
             indices = recommend(features, feature_list)
 
-            # Display recommended products
+            # Display recommended products using loaded images from the dataset
             col1, col2, col3, col4, col5 = st.columns(5)
             with col1:
-                st.image(filenames[indices[0][0]])
+                st.image(chatbot.images[indices[0][0]])
             with col2:
-                st.image(filenames[indices[0][1]])
+                st.image(chatbot.images[indices[0][1]])
             with col3:
-                st.image(filenames[indices[0][2]])
+                st.image(chatbot.images[indices[0][2]])
             with col4:
-                st.image(filenames[indices[0][3]])
+                st.image(chatbot.images[indices[0][3]])
             with col5:
-                st.image(filenames[indices[0][4]])
+                st.image(chatbot.images[indices[0][4]])
 
         else:
             st.header("Some error occurred in file upload")