import streamlit as st
import os
from PIL import Image
import numpy as np
from chatbot import Chatbot  # Assuming you have a chatbot module

# Function to save uploaded file
def save_uploaded_file(uploaded_file):
    try:
        if not os.path.exists('uploads'):
            os.makedirs('uploads')
        with open(os.path.join('uploads', uploaded_file.name), 'wb') as f:
            f.write(uploaded_file.getbuffer())
        return True
    except Exception as e:
        st.error(f"Error: {e}")
        return False

# Function to show dashboard content
def show_dashboard():
    st.title("Fashion Recommender System")
    st.write("Welcome to our Fashion Recommender System! Upload an image and get personalized product recommendations based on your image and queries.")

    chatbot = Chatbot()
    chatbot.load_data()

    # Load and set up the ResNet model
    uploaded_file = st.file_uploader("Upload an Image", type=['jpg', 'jpeg', 'png'])
    
    if uploaded_file:
        if save_uploaded_file(uploaded_file):
            st.sidebar.header("Uploaded Image")
            display_image = Image.open(uploaded_file)
            st.sidebar.image(display_image, caption='Uploaded Image', use_column_width=True)
            
            # Generate image caption
            image_path = os.path.join("uploads", uploaded_file.name)
            caption = chatbot.generate_image_caption(image_path)
            st.write("### Generated Caption")
            st.write(caption)
            
            # Use caption to get product recommendations
            _, recommended_products = chatbot.generate_response(caption)

            st.write("### Recommended Products")
            col1, col2, col3, col4, col5 = st.columns(5)
            for i, idx in enumerate(recommended_products[:5]):
                with col1 if i == 0 else col2 if i == 1 else col3 if i == 2 else col4 if i == 3 else col5:
                    product_image = chatbot.images[idx['corpus_id']]
                    st.image(product_image, caption=f"Product {i+1}", width=150)
        else:
            st.error("Error in uploading the file.")

    # Chatbot section
    st.write("### Chat with our Fashion Assistant")
    user_question = st.text_input("Ask a question about fashion:")
    if user_question:
        bot_response, recommended_products = chatbot.generate_response(user_question)
        st.write("**Chatbot Response:**")
        st.write(bot_response)

        # Display recommended products based on the user question
        st.write("**Recommended Products:**")
        for result in recommended_products:
            pid = result['corpus_id']
            product_info = chatbot.product_data[pid]
            st.markdown("""
                <div style='border: 1px solid #ddd; padding: 10px; margin: 10px 0; border-radius: 5px;'>
                    <p><strong>Product Name:</strong> {product_name}</p>
                    <p><strong>Category:</strong> {category}</p>
                    <p><strong>Article Type:</strong> {article_type}</p>
                    <p><strong>Usage:</strong> {usage}</p>
                    <p><strong>Season:</strong> {season}</p>
                    <p><strong>Gender:</strong> {gender}</p>
                    <img src="{image_url}" width="150" />
                </div>
                """.format(
                    product_name=product_info['productDisplayName'],
                    category=product_info['masterCategory'],
                    article_type=product_info['articleType'],
                    usage=product_info['usage'],
                    season=product_info['season'],
                    gender=product_info['gender'],
                    image_url="uploads/" + uploaded_file.name  # assuming images are saved in uploads folder
                ), unsafe_allow_html=True)

# Main Streamlit app
def main():
    # Set page configuration
    st.set_page_config(
        page_title="Fashion Recommender System",
        page_icon=":dress:",
        layout="wide",
        initial_sidebar_state="expanded"
    )

    # Show dashboard content directly
    show_dashboard()

# Run the main app
if __name__ == "__main__":
    main()