import streamlit as st
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.preprocessing import normalize
from sklearn.decomposition import TruncatedSVD
from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd

# Load the dataset
dataset = pd.read_csv('avito_cars.csv')

# Combine relevant columns into 'content'
dataset['content'] = (
    dataset['Marque'] + " " +
    dataset['Modèle'] + " " +
    dataset['Type de carburant'] + " " +
    dataset['Boite de vitesses']
)
dataset = dataset.drop_duplicates(subset=['content'])  # Remove duplicates

# Preprocess and build TF-IDF and LSI
vectorizer = TfidfVectorizer(stop_words=None)
tfidf_matrix = vectorizer.fit_transform(dataset['content'])

n_components = 50  # Number of LSI dimensions
svd = TruncatedSVD(n_components=n_components)
lsi_matrix = svd.fit_transform(tfidf_matrix)
lsi_matrix = normalize(lsi_matrix)

# Search function
def search(query, top_n=100):
    query_tfidf = vectorizer.transform([query])
    query_lsi = svd.transform(query_tfidf)
    query_lsi = normalize(query_lsi)
    similarities = cosine_similarity(query_lsi, lsi_matrix).flatten()
    top_indices = similarities.argsort()[-top_n:][::-1]
    results = dataset.iloc[top_indices]
    return results, similarities[top_indices]

# Streamlit Interface
st.title("Moteur de recherche de voitures basé sur le LSI (Latent Semantic Indexing)")
st.write("Recherchez des voitures en utilisant des mots-clés (par ex. : 'Peugeot Diesel Manuelle').")

# User input
query = st.text_input("Entrez votre requête de recherche :")
top_n = st.slider("Nombre de résultats à afficher par page :", min_value=3, max_value=12, step=3, value=6)

# Pagination logic
if "page" not in st.session_state:
    st.session_state.page = 1

#if st.button("Previous Page"):
#    st.session_state.page = max(1, st.session_state.page - 1)

#if st.button("Next Page"):
#    st.session_state.page += 1

# Search and display
if st.button("Search") or query.strip():
    results, similarities = search(query)
    total_results = len(results)
    results_per_page = top_n
    total_pages = (total_results // results_per_page) + (1 if total_results % results_per_page != 0 else 0)

    # Paginate results
    start_idx = (st.session_state.page - 1) * results_per_page
    end_idx = start_idx + results_per_page
    paginated_results = results.iloc[start_idx:end_idx]
    st.write(f"Showing results {start_idx + 1}-{min(end_idx, total_results)} of {total_results} (Page {st.session_state.page}/{total_pages}):")

    # Start the grid layout
   # Display cards in rows using Streamlit's `st.columns()`
    for i, (index, row) in enumerate(paginated_results.iterrows()):
        if i % 3 == 0:  # Create a new row every 3 cards
            cols = st.columns(3)  # 3 cards per row
        
        # Use the appropriate column in the row
        with cols[i % 3]:
            link = row['Lien']
            st.markdown(
                f"""
                <div style="
                    border: 1px solid green;
                    border-radius: 10px;
                    padding: 10px;
                    background-color: #f9f9f9;
                    text-align: left;
                    height: auto;
                    margin-bottom: 20px;
                ">
                    <h5>{row['content']}</h5>
                    <p><strong>Année-Modèle:</strong> {row['Année-Modèle']}</p>
                    <p><strong>Price:</strong> {row['Prix']} MAD</p>
                    <p><strong>City:</strong> {row['Ville']}</p>
                    <p><strong>Kilométrage:</strong> {row['Kilométrage']} km</p>
                    <a href="{link}" target="_blank" style="
                        display: block;
                        margin: 10px auto 0 auto; 
                        background-color: #4CAF50; 
                        color: white; 
                        padding: 5px 10px; 
                        text-align: center; 
                        text-decoration: none; 
                        border-radius: 5px;">
                        View Details
                    </a>
                </div>
                """,
                unsafe_allow_html=True,
            )

        

    
    
    # Pagination controls
    st.write("Navigation:")
    col1, col2, col3 = st.columns(3)
    with col1:
        if st.button("Previous"):
            st.session_state.page = max(1, st.session_state.page - 1)
    with col3:
        if st.button("Next"):
            st.session_state.page += 1