Update app.py

app.py

@@ -1,16 +1,15 @@
-import os
-import pickle
-import numpy as np
-import pandas as pd
 import streamlit as st
+import os
 from PIL import Image
-import tensorflow as tf
+import numpy as np
+import pickle
+import tensorflow
 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 datasets import load_dataset
-import zipfile
+from numpy.linalg import norm
+from chatbot import Chatbot  # Assuming you have a chatbot module
 
 # Define function for feature extraction
 def feature_extraction(img_path, model):
@@ -19,7 +18,7 @@ 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 / np.linalg.norm(result)
+    normalized_result = result / norm(result)
     return normalized_result
 
 # Define function for recommendation
@@ -40,41 +39,19 @@ def save_uploaded_file(uploaded_file):
         with open(file_path, 'wb') as f:
             f.write(uploaded_file.getbuffer())
         st.success(f"File saved to {file_path}")
-        return file_path
+        return True
     except Exception as e:
         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)
-    columns = [col1, col2, col3, col4, col5]
-
-    for col, idx in zip(columns, indices[0]):
-        try:
-            img = images[idx]
-            with col:
-                st.image(img)
-        except Exception as e:
-            st.error(f"Error displaying image {idx}: {e}")
+        return False
 
 # Function to show dashboard content
 def show_dashboard():
     st.header("Fashion Recommender System")
-
-    # Load the dataset and models
-    images, product_data = load_image_data()
+    chatbot = Chatbot()
+    # Load ResNet model for image feature extraction
     model = ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
     model.trainable = False
-    model = tf.keras.Sequential([
+    model = tensorflow.keras.Sequential([
         model,
         GlobalMaxPooling2D()
     ])
@@ -89,32 +66,35 @@ def show_dashboard():
     # File upload section
     uploaded_file = st.file_uploader("Choose an image")
     if uploaded_file is not None:
-        file_path = save_uploaded_file(uploaded_file)
-        if file_path:
-            try:
-                display_image = Image.open(file_path)
-                st.image(display_image)
-            except Exception as e:
-                st.error(f"Error displaying uploaded image: {e}")
-
-            try:
-                features = feature_extraction(file_path, model)
-            except Exception as e:
-                st.error(f"Error extracting features: {e}")
-                return
-
-            try:
-                indices = recommend(features, feature_list)
-                display_similar_images(indices, filenames, images)
-            except Exception as e:
-                st.error(f"Error in recommendation: {e}")
-                return
+        if save_uploaded_file(uploaded_file):
+            # Display the uploaded image
+            display_image = Image.open(uploaded_file)
+            st.image(display_image)
+
+            # Feature extraction
+            features = feature_extraction(os.path.join("uploads", uploaded_file.name), model)
+
+            # Recommendation
+            indices = recommend(features, feature_list)
+
+            # Display recommended products
+            col1, col2, col3, col4, col5 = st.columns(5)
+            with col1:
+                st.image(filenames[indices[0][0]])
+            with col2:
+                st.image(filenames[indices[0][1]])
+            with col3:
+                st.image(filenames[indices[0][2]])
+            with col4:
+                st.image(filenames[indices[0][3]])
+            with col5:
+                st.image(filenames[indices[0][4]])
+        else:
+            st.error("Some error occurred in file upload")
 
     # 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)