import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics.pairwise import cosine_similarity
import matplotlib.pyplot as plt
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Embedding, Flatten, concatenate, Dense
from tensorflow.keras.optimizers import Adam

# Load datasets
books = pd.read_csv("../data/dataset/books.csv")
ratings = pd.read_csv("../data/dataset/ratings.csv")

# Preprocess data
user_encoder = LabelEncoder()
book_encoder = LabelEncoder()

ratings["user_id"] = user_encoder.fit_transform(ratings["user_id"])
ratings["book_id"] = book_encoder.fit_transform(ratings["book_id"])

# Split the data into training and testing sets
train, test = train_test_split(ratings, test_size=0.2, random_state=42)


# Define the neural network model
def build_model(num_users, num_books, embedding_size=50):
    """
    Build a recommendation model.

    Args:
        num_users (int): The number of users in the dataset.
        num_books (int): The number of books in the dataset.
        embedding_size (int, optional): The size of the embedding vectors. Defaults to 50.

    Returns:
        keras.Model: The compiled recommendation model.
    """
    user_input = Input(shape=(1,))
    book_input = Input(shape=(1,))

    user_embedding = Embedding(input_dim=num_users, output_dim=embedding_size)(
        user_input
    )
    book_embedding = Embedding(input_dim=num_books, output_dim=embedding_size)(
        book_input
    )

    user_flat = Flatten()(user_embedding)
    book_flat = Flatten()(book_embedding)

    merged = concatenate([user_flat, book_flat])
    dense1 = Dense(128, activation="relu")(merged)
    output = Dense(1)(dense1)

    model = Model(inputs=[user_input, book_input], outputs=output)
    model.compile(loss="mean_squared_error", optimizer=Adam(learning_rate=0.001))

    return model


# Train the model
model = build_model(
    num_users=len(ratings["user_id"].unique()),
    num_books=len(ratings["book_id"].unique()),
)
history = model.fit(
    [train["user_id"], train["book_id"]],
    train["rating"],
    epochs=5,
    batch_size=128,
    validation_split=0.1,
)

# Plot training and validation loss
plt.figure(figsize=(12, 6))
plt.plot(history.history["loss"], label="Training Loss")
plt.plot(history.history["val_loss"], label="Validation Loss")
plt.xlabel("Epoch")
plt.ylabel("Loss")
plt.legend()
plt.show()

# Save the model
model.save("recommendation_model.h5")

# Evaluate the model
test_loss = model.evaluate([test["user_id"], test["book_id"]], test["rating"])
print(f"Test Loss: {test_loss}")