Delete main.py

main.py

@@ -1,152 +0,0 @@
-import streamlit as st
-import os
-from PIL import Image
-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 numpy.linalg import norm
-from chatbot import Chatbot  # Assuming you have a chatbot module
-import zipfile
-
-# Define the path to the zip file and the directory to extract to
-zip_file_path = 'images.zip'
-extract_to = 'images'
-
-# Check if the images directory already exists to avoid re-extracting
-if not os.path.exists(extract_to):
-    with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:
-        zip_ref.extractall(extract_to)
-
-# Define function for feature extraction
-def feature_extraction(img_path, model):
-    img = image.load_img(img_path, target_size=(224, 224))
-    img_array = image.img_to_array(img)
-    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)
-    return normalized_result
-
-# Define function for recommendation
-def recommend(features, feature_list):
-    neighbors = NearestNeighbors(n_neighbors=6, algorithm='brute', metric='euclidean')
-    neighbors.fit(feature_list)
-    distances, indices = neighbors.kneighbors([features])
-    return indices
-
-# Function to save uploaded file
-def save_uploaded_file(uploaded_file):
-    try:
-        # Ensure the uploads directory exists
-        if not os.path.exists('uploads'):
-            os.makedirs('uploads')
-        
-        file_path = os.path.join('uploads', uploaded_file.name)
-        with open(file_path, 'wb') as f:
-            f.write(uploaded_file.getbuffer())
-        st.success(f"File saved to {file_path}")
-        return file_path
-    except Exception as e:
-        st.error(f"Error saving file: {e}")
-        return None
-
-# Function to show dashboard content
-def show_dashboard():
-    st.header("Fashion Recommender System")
-    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 = tensorflow.keras.Sequential([
-        model,
-        GlobalMaxPooling2D()
-    ])
-
-    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")
-    if uploaded_file is not None:
-        file_path = save_uploaded_file(uploaded_file)
-        if file_path:
-            # Display the uploaded image
-            try:
-                display_image = Image.open(file_path)
-                st.image(display_image)
-            except Exception as e:
-                st.error(f"Error displaying uploaded image: {e}")
-
-            # Feature extraction
-            try:
-                features = feature_extraction(file_path, model)
-            except Exception as e:
-                st.error(f"Error extracting features: {e}")
-                return
-
-            # Recommendation
-            try:
-                indices = recommend(features, feature_list)
-            except Exception as e:
-                st.error(f"Error in recommendation: {e}")
-                return
-
-            # Display recommended products
-            col1, col2, col3, col4, col5 = st.columns(5)
-            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):
-                        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}")
-        else:
-            st.error("Some error occurred in file upload")
-
-    # Chatbot section
-    user_question = st.text_input("Ask a question:")
-    if user_question:
-        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]
-            st.write("Product Name:", product_info['productDisplayName'])
-            st.write("Category:", product_info['masterCategory'])
-            st.write("Article Type:", product_info['articleType'])
-            st.write("Usage:", product_info['usage'])
-            st.write("Season:", product_info['season'])
-            st.write("Gender:", product_info['gender'])
-            st.image(chatbot.images[pid])
-
-# 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()