Update app.py

app.py

@@ -1,14 +1,13 @@
-import streamlit as st
 import os
 import pickle
 import numpy as np
-import tensorflow
+import streamlit as st
+from PIL import Image
 from tensorflow.keras.preprocessing import image
 from tensorflow.keras.layers import GlobalMaxPooling2D
 from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input
 from sklearn.neighbors import NearestNeighbors
 from numpy.linalg import norm
-from PIL import Image
 from chatbot import Chatbot  # Assuming you have a chatbot module
 
 # Define function for feature extraction
@@ -47,8 +46,8 @@ def save_uploaded_file(uploaded_file):
 # Function to show dashboard content
 def show_dashboard():
     st.header("Fashion Recommender System")
-    chatbot = Chatbot()  # Load the chatbot, which will also load the dataset
-
+    chatbot = Chatbot()
+    
     # Load ResNet model for image feature extraction
     model = ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
     model.trainable = False
@@ -57,9 +56,16 @@ def show_dashboard():
         GlobalMaxPooling2D()
     ])
 
-    # Feature list and filenames should be loaded from the same source as the chatbot
-    feature_list = np.array(chatbot.image_embeddings)  # Assuming you have embeddings loaded in the chatbot
-    filenames = chatbot.product_frame['productImageURL'].tolist()  # Adjust according to your dataset
+    try:
+        feature_list = np.array(pickle.load(open('embeddings.pkl', 'rb')))
+        filenames = pickle.load(open('filenames.pkl', 'rb'))
+    except Exception as e:
+        st.error(f"Error loading pickle files: {e}")
+        return
+
+    # Print the filenames to verify
+    st.write("List of filenames loaded:")
+    st.write(filenames)
 
     # File upload section
     uploaded_file = st.file_uploader("Choose an image")
@@ -92,16 +98,14 @@ def show_dashboard():
             columns = [col1, col2, col3, col4, col5]
 
             for col, idx in zip(columns, indices[0]):
-                file_path = filenames[idx]
-                st.write(f"Trying to open file: {file_path}")  # Add debug info
-                try:
-                    if os.path.exists(file_path):
+                # Directly access images from the dataset instead of file paths
+                image_data = chatbot.images[idx]
+                if image_data is not None:
+                    try:
                         with col:
-                            st.image(file_path)
-                    else:
-                        st.error(f"File does not exist: {file_path}")
-                except Exception as e:
-                    st.error(f"Error opening file {file_path}: {e}")
+                            st.image(image_data)
+                    except Exception as e:
+                        st.error(f"Error opening image index {idx}: {e}")
         else:
             st.error("Some error occurred in file upload")