import os
import pandas as pd
import math
import pickle as pkl
import torch
from torch_geometric.data import Data

# Get the directory of the current script
script_dir = os.path.dirname(os.path.abspath(__file__))
base_dir = os.path.dirname(script_dir)
raw_dir = os.path.join(base_dir, 'processed/original')

# Define the file path
reddit_path = os.path.join(raw_dir, 'reddit_1m.csv')

# Read the Reddit data
df = pd.read_csv(reddit_path)
print(df.shape)

# Select required columns
df_graph = df[['subreddit_id', 'subreddit', 'name', 'body', 'score', 'author', 'author_flair_text', 'distinguished']]
df_graph.rename(columns={'name': 'post_id',
                         'body': 'post',
                         'author': 'user',
                         'author_flair_text': 'user_flair'},
                inplace=True, errors='raise')

# Drop duplicates, deleted posts, and rows with NaN post_id
df_graph = df_graph.drop_duplicates()
df_graph = df_graph[df_graph['post'] != '[deleted]']
df_graph = df_graph.dropna(subset=['post_id'])
print(df_graph.shape)
print(df_graph['post_id'].nunique())

# Encode distinguished and user_flair
df_graph['distinguished'] = df_graph['distinguished'].apply(lambda x: 0 if pd.isna(x) else 1)
df_graph['user_flair'] = df_graph['user_flair'].apply(lambda x: "" if pd.isna(x) else x)

text_nodes = []

# Create sub_id2idx
sub_id2idx = {}
sub_nodes = []
for _, row in df_graph.iterrows():
    sub_id = row['subreddit_id']
    if sub_id not in sub_nodes:
        sub_id2idx[sub_id] = len(sub_nodes)
        sub_nodes.append(sub_id)
        text_nodes.append(row['subreddit'])
node_labels = [-1] * len(sub_nodes)  # No labels

print("Length of sub nodes:", len(sub_nodes))
print("Sample sub node labels:", node_labels[:5])
print("Sample sub node texts:", text_nodes[:5])

# Create user_n2idx
user_n2idx = {}  # Username to index mapping
user_nodes = []
for _, row in df_graph.iterrows():
    user_n = row['user']
    if user_n in user_nodes:  # Existing user: add new flair and update label
        if row['user_flair'] not in text_nodes[user_n2idx[user_n]]:
            text_nodes[user_n2idx[user_n]] += "\n" + row['user_flair']
        node_labels[user_n2idx[user_n]] = max(row['distinguished'], node_labels[user_n2idx[user_n]])
    else:  # New user: add the user to user_n2idx
        user_n2idx[user_n] = len(user_nodes) + len(sub_nodes)
        user_nodes.append(user_n)
        text_nodes.append(row['user_flair'])
        node_labels.append(row['distinguished'])

print("Length of user nodes:", len(user_nodes))
print("Sample user node labels:", node_labels[-10:])
print("Sample user node texts:", text_nodes[-10:])

# Record edge information
edge_index = [[], []]
text_edges = []
edge_scr_labels = []  # Continuous score
edge_spe_labels = []  # Binary special label
all_edges = set()

for _, row in df_graph.iterrows():
    user_idx = user_n2idx[row['user']]
    sub_idx = sub_id2idx[row['subreddit_id']]

    if (user_idx, sub_idx) not in all_edges:  # Only keep one edge between two nodes
        edge_index[0].append(user_idx)
        edge_index[1].append(sub_idx)

        text_edges.append(row['post'])
        edge_scr_labels.append(row['score'])
        edge_spe_labels.append(row['distinguished'])

        all_edges.add((user_idx, sub_idx))

print("Length of edges:", len(edge_index[0]))
print("Sample edge score labels:", edge_scr_labels[-10:])
print("Sample edge special labels:", edge_spe_labels[-10:])
print("Sample edge texts:", text_edges[-10:])

edge_scr_labels = [0 if math.isnan(x) else x for x in edge_scr_labels]
edge_spe_labels = [0 if math.isnan(x) else x for x in edge_spe_labels]

# Save as torch data
graph = Data(
    text_nodes=text_nodes,
    text_edges=text_edges,
    node_labels=torch.tensor(node_labels, dtype=torch.long),
    edge_index=torch.tensor(edge_index, dtype=torch.long),
    edge_score_labels=torch.tensor(edge_scr_labels, dtype=torch.long),
    edge_special_labels=torch.tensor(edge_spe_labels, dtype=torch.long),
)

output_file = os.path.join(base_dir, 'output/reddit_graph.pkl')
with open(output_file, 'wb') as file:
    pkl.dump(graph, file)

print(f"Data processing complete. Processed data saved to: {output_file}")