Update app.py

app.py

@@ -1,16 +1,16 @@
 import os
 import pickle
 import numpy as np
-import tensorflow as tf
+import pandas as pd
 import streamlit as st
 from PIL import Image
+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
 from sklearn.neighbors import NearestNeighbors
-from numpy.linalg import norm
-from chatbot import Chatbot
 from datasets import load_dataset
+import zipfile
 
 # Define function for feature extraction
 def feature_extraction(img_path, model):
@@ -19,7 +19,7 @@ def feature_extraction(img_path, model):
     expanded_img_array = np.expand_dims(img_array, axis=0)
     preprocessed_img = preprocess_input(expanded_img_array)
     result = model.predict(preprocessed_img).flatten()
-    normalized_result = result / norm(result)
+    normalized_result = result / np.linalg.norm(result)
     return normalized_result
 
 # Define function for recommendation
@@ -45,6 +45,14 @@ def save_uploaded_file(uploaded_file):
         st.error(f"Error saving file: {e}")
         return None
 
+# Function to load image data from dataset
+def load_image_data():
+    dataset = load_dataset("ashraq/fashion-product-images-small", split="train")
+    images = dataset["image"]
+    product_frame = dataset.remove_columns("image").to_pandas()
+    product_data = product_frame.reset_index(drop=True).to_dict(orient='index')
+    return images, product_data
+
 # Function to show similar images
 def display_similar_images(indices, filenames, images):
     col1, col2, col3, col4, col5 = st.columns(5)
@@ -58,15 +66,7 @@ def display_similar_images(indices, filenames, images):
         except Exception as e:
             st.error(f"Error displaying image {idx}: {e}")
 
-# Load the dataset
-def load_image_data():
-    dataset = load_dataset("ashraq/fashion-product-images-small", split="train")
-    images = dataset["image"]
-    product_frame = dataset.remove_columns("image").to_pandas()
-    product_data = product_frame.reset_index(drop=True).to_dict(orient='index')
-    return images, product_data
-
-# Show dashboard content
+# Function to show dashboard content
 def show_dashboard():
     st.header("Fashion Recommender System")
 
@@ -113,10 +113,12 @@ def show_dashboard():
     # Chatbot section
     user_question = st.text_input("Ask a question:")
     if user_question:
+        from chatbot import Chatbot
         chatbot = Chatbot()
         bot_response, recommended_products = chatbot.generate_response(user_question)
         st.write("Chatbot:", bot_response)
 
+        # Display recommended products
         for result in recommended_products:
             pid = result['corpus_id']
             product_info = chatbot.product_data[pid]
@@ -128,10 +130,14 @@ def show_dashboard():
             st.write("Gender:", product_info['gender'])
             st.image(chatbot.images[pid])
 
-if __name__ == "__main__":
-    st.set_page_config(
-        page_title="Fashion Recommender System",
-        page_icon="🗣️",
-        menu_items={'About': "# Made by Prathamesh Khade"}
-    )
+# Main Streamlit app
+def main():
+    # Give title to the app
+    st.title("Fashion Recommender System")
+
+    # Show dashboard content directly
     show_dashboard()
+
+# Run the main app
+if __name__ == "__main__":
+    main()