import pandas as pd
from gradio import Interface, Textbox, Dropdown

# Load the prediction data for each algorithm and season
als_spring_data = pd.read_csv('als_pred_spring.csv')
als_fall_data = pd.read_csv('als_pred_fall.csv')
als_winter_data = pd.read_csv('als_pred_winter.csv')
als_summer_data = pd.read_csv('als_pred_summer.csv')

nmf_spring_data = pd.read_csv('nmf_pred_spring.csv')
nmf_fall_data = pd.read_csv('nmf_pred_fall.csv')
nmf_winter_data = pd.read_csv('nmf_pred_winter.csv')
nmf_summer_data = pd.read_csv('nmf_pred_summer.csv')

# Function to get recommendations based on customer ID, season, and algorithm
def get_recommendations(customer_id, season, algorithm):
    if algorithm == 'ALS':
        if season == 'Spring':
            data = als_spring_data
        elif season == 'Fall':
            data = als_fall_data
        elif season == 'Winter':
            data = als_winter_data
        elif season == 'Summer':
            data = als_summer_data
        else:
            return 'Invalid season'
    elif algorithm == 'NMF':
        if season == 'Spring':
            data = nmf_spring_data
        elif season == 'Fall':
            data = nmf_fall_data
        elif season == 'Winter':
            data = nmf_winter_data
        elif season == 'Summer':
            data = nmf_summer_data
        else:
            return 'Invalid season'
    else:
        return 'Invalid algorithm'
    
    if customer_id not in data['customer_id'].values:
        return 'Recommendation not found'
    
    recommendations = data[data['customer_id'] == customer_id]['article_id'].iloc[0]
    recommendations = recommendations.strip("[]").split(", ")
    recommendations = [f"Item {i}: {item}" for i, item in enumerate(recommendations, 1)]
    
    return '\n'.join(recommendations)

# Create the input and output interfaces
customer_id_input = Textbox(label="Customer ID")
season_input = Dropdown(choices=['Spring', 'Fall', 'Winter', 'Summer'], label="Season")
algorithm_input = Dropdown(choices=['ALS', 'NMF'], label="Algorithm")
output = Textbox(label="Recommendations")

# Create the interface with the title
title = "Collaborative Filtering for Recommendation System on Fashion Products"
interface = Interface(fn=get_recommendations, inputs=[customer_id_input, season_input, algorithm_input], outputs=output, title=title)

# Run the interface
interface.launch()