import base64
import gradio as gr
import pandas as pd
from datasets import load_dataset
from datasets import Dataset
import os
from huggingface_hub import login

# Access the secret token
hf_token = os.getenv("WRITE_TOKEN")

# Authenticate using the secret token
login(token=hf_token)

# Load the dataset from Hugging Face Datasets
data = load_dataset('moizmoizmoizmoiz/MovieRatingDB', split='train')

# Convert dataset to a pandas DataFrame for easier manipulation
data_df = data.to_pandas()

# Add a default column for ratings if not already present
for profile in ['moiz', 'udisha', 'musab']:
    if profile not in data_df.columns:
        data_df[profile] = ""  # Default rating is empty

# Save the current index and selected profile
current_index = [0]
current_profile = ["moiz"]  # Default profile

def encode_image(image_path):
    with open(image_path, "rb") as img_file:
        return f"data:image/png;base64,{base64.b64encode(img_file.read()).decode('utf-8')}"

# Encode images as base64
imdb_logo = encode_image("assets/imdblogo.png")
rotten_logo = encode_image("assets/rotten.png")
metacritic_logo = encode_image("assets/metacritic.png")

def display_movie(action, profile):
    # Update the current profile
    current_profile[0] = profile

    # Update the index based on action
    if action == "next" and current_index[0] < len(data_df) - 1:
        current_index[0] += 1
    elif action == "prev" and current_index[0] > 0:
        current_index[0] -= 1

    # Extract movie details
    movie = data_df.iloc[current_index[0]]
    
    # Get the IMDb ID and Poster URL
    movie_id = movie.get('id', 'Unknown')  # Use 'Unknown' if 'id' doesn't exist
    poster_url = movie.get("Poster", None)  # Default to None if Poster is missing

    # Use default text if no poster URL is available
    poster_content = (
        f'<img src="{poster_url}" alt="Poster" style="width: 100%; max-width: 500px; height: auto; border-radius: 10px;"/>'
        if poster_url
        else "Poster not available"
    )

    details = {
        "title": f'<a href="https://www.imdb.com/title/{movie_id}/" target="_blank" style="font-size: 36px; text-decoration: none; color: inherit; font-family: Arial, sans-serif;">{movie["Title"]}</a>',
        "poster_content": poster_content,
        "ratings": f"""
        <div style="display: flex; gap: 15px; align-items: center; text-align: center;">
            <div>
                <img src="{imdb_logo}" alt="IMDb" style="width: 30px; height: auto;"/>
                <p>{movie['IMDb']}</p>
            </div>
            <div>
                <img src="{rotten_logo}" alt="Rotten Tomatoes" style="width: 30px; height: auto;"/>
                <p>{movie['Rotten Tomatoes']}</p>
            </div>
            <div>
                <img src="{metacritic_logo}" alt="Metascore" style="width: 30px; height: auto;"/>
                <p>{movie['Metascore']}</p>
            </div>
        </div>
        """,
        "details": f"""
        **Year:** {movie['Year']}  
        **Rated:** {movie['Rated']}  
        **Runtime:** {movie['Runtime']}  
        **Genre:** {movie['Genre1']}, {movie['Genre2']}, {movie['Genre3']}  
        **Director:** {movie['Director']}  
        **Writer:** {movie['Writer']}  
        **Plot:** {movie['Plot']}  
        **Awards:** {movie['Awards']}  
        **Box Office:** {movie['BoxOffice']}
        """,
        "current_rating": f"Your Rating: {movie[profile]}",
        "index_display": f'{current_index[0] + 1}'
    }
    return details["title"], details["poster_content"], details["ratings"], details["details"], details["current_rating"], details["index_display"]

def submit_rating(rating):
    # Update the rating for the current profile and movie
    movie_index = current_index[0]
    profile = current_profile[0]
    data_df.at[movie_index, profile] = rating

    # Save the changes to the dataset
    updated_data = Dataset.from_pandas(data_df)
    updated_data.push_to_hub("moizmoizmoizmoiz/MovieRatingDB")

    return display_movie("stay", profile)

def not_watched():
    # Mark the movie as "N/W" for the current profile
    movie_index = current_index[0]
    profile = current_profile[0]
    data_df.at[movie_index, profile] = 99

    # Save the changes to the dataset
    updated_data = Dataset.from_pandas(data_df)
    updated_data.push_to_hub("moizmoizmoizmoiz/MovieRatingDB")

    return display_movie("stay", profile)

def jump_to_index(index, profile):
    # Ensure the input index is valid
    try:
        index = int(index) - 1  # Convert to zero-based index
    except ValueError:
        index = current_index[0]  # If invalid, keep the current index

    # Update the index only if within range
    if 0 <= index < len(data_df):
        current_index[0] = index

    # Display the movie details for the selected index
    return display_movie("stay", profile)

# Define Gradio interface with external CSS file
with gr.Blocks(css_paths="styles.css") as app:
    gr.Markdown("## 🎬 Movie Database Viewer 🎬")

    with gr.Row():
        # Profile selector and movie index
        movie_index_input = gr.Textbox(
            value="1", label="Go to Movie Index", interactive=True, elem_id="movie-index-input"
        )

        blank_column_1 = gr.Column(scale=2, elem_id="blank-column-1")
        blank_column_2 = gr.Column(scale=1, elem_id="blank-column-2")

        profile_selector = gr.Dropdown(
            choices=['moiz', 'udisha', 'musab'], 
            value='moiz', 
            label="Profile", 
            interactive=True
        )

    with gr.Row():
        with gr.Column(scale=1):
            poster = gr.Markdown("🎥 Movie Poster Placeholder 🎥", elem_id="poster")
        with gr.Column(scale=2):
            title = gr.Markdown("Title Placeholder", elem_id="title")
            ratings = gr.HTML("<div style='text-align: center;'>Ratings Placeholder</div>", elem_id="ratings")
            movie_details = gr.Markdown("Details will appear here.", elem_id="details")
            user_rating = gr.Markdown("Your Rating: 0", elem_id="current-rating")

    with gr.Row():
        # Slider in its own row
        rating_slider = gr.Slider(0, 10, step=0.25, label="Rate this movie:", elem_id="rating-slider", interactive=True, scale=2)

    with gr.Row():
        not_watched_button = gr.Button("🚫 Not Watched")
        blank_column_1 = gr.Column(scale=1, elem_id="blank-column-1")
        submit_button = gr.Button("✅ Submit Rating")

    with gr.Row():
        prev_button = gr.Button("⬅️ Previous", scale=1)
        blank_column_1 = gr.Column(scale=2, elem_id="blank-column-1")
        blank_column_2 = gr.Column(scale=2, elem_id="blank-column-2")
        next_button = gr.Button("Next ➡️", scale=1)

    # Interactivity for buttons
    profile_selector.change(
        display_movie,
        inputs=[gr.Text(value="stay", visible=False), profile_selector],
        outputs=[title, poster, ratings, movie_details, user_rating, movie_index_input]
    )

    prev_button.click(
        display_movie,
        inputs=[gr.Text(value="prev", visible=False), profile_selector],
        outputs=[title, poster, ratings, movie_details, user_rating, movie_index_input]
    )
    next_button.click(
        display_movie,
        inputs=[gr.Text(value="next", visible=False), profile_selector],
        outputs=[title, poster, ratings, movie_details, user_rating, movie_index_input]
    )

    submit_button.click(
        submit_rating,
        inputs=rating_slider,
        outputs=[title, poster, ratings, movie_details, user_rating, movie_index_input]
    )

    not_watched_button.click(
        not_watched,
        outputs=[title, poster, ratings, movie_details, user_rating, movie_index_input]
    )

    movie_index_input.submit(
        jump_to_index,
        inputs=[movie_index_input, profile_selector],
        outputs=[title, poster, ratings, movie_details, user_rating, movie_index_input]
    )

app.launch()