import os
import csv
import json
import logging
import gradio as gr
import pandas as pd
from tqdm import tqdm
import nltk
from nltk.tokenize import word_tokenize
from nltk.corpus import wordnet
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
from huggingface_hub import HfApi, login
from datasets import Dataset
from datetime import datetime
import secrets

nltk.download('all')

log_dir = "logs"
os.makedirs(log_dir, exist_ok=True)
logging.basicConfig(
    filename=os.path.join(log_dir, f"app_{datetime.now().strftime('%Y%m%d_%H%M%S')}.log"),
    level=logging.INFO,
    format='%(asctime)s - %(levelname)s - %(message)s'
)

error_dir = "errors"
os.makedirs(error_dir, exist_ok=True)
error_log_file = os.path.join(error_dir, f"errors_{datetime.now().strftime('%Y%m%d_%H%M%S')}.log")

def log_error(error_msg):
    with open(error_log_file, 'a') as f:
        f.write(f"{datetime.now().strftime('%Y-%m-%d %H:%M:%S')} - ERROR - {error_msg}\n")

HF_TOKEN = os.getenv("HF_TOKEN", secrets.token_hex(16))
login(token=HF_TOKEN)

tokenizer = AutoTokenizer.from_pretrained("amd/Instella-3B-Instruct", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("amd/Instella-3B-Instruct", trust_remote_code=True)
meaning_generator = pipeline("text2text-generation", model="google/flan-t5-large")

dataset_dir = "dataset"
os.makedirs(dataset_dir, exist_ok=True)
csv_file = os.path.join(dataset_dir, "deepfocus_data.csv")
parquet_file = os.path.join(dataset_dir, "deepfocus_data.parquet")

def process_text_to_csv(input_text):
    try:
        tokens = word_tokenize(input_text.lower())
        words = list(set(tokens))
        data = []

        existing_df = pd.read_parquet(parquet_file) if os.path.exists(parquet_file) else pd.DataFrame(columns=["words", "meaning"])
        existing_words = set(existing_df["words"].tolist())

        for word in tqdm(words, desc="Processing words"):
            if word in existing_words:
                continue

            meanings = [syn.definition() for syn in wordnet.synsets(word)[:3]] or \
                       [meaning_generator(f"Define the word '{word}'", max_length=100)[0]['generated_text']]

            data.append({"words": word, "meaning": meanings})

        if data:
            new_df = pd.DataFrame(data)
            combined_df = pd.concat([existing_df, new_df], ignore_index=True)
            combined_df.to_parquet(parquet_file, index=False)
            combined_df.to_csv(csv_file, index=False, encoding='utf-8')

        return data
    except Exception as e:
        log_error(f"Error in process_text_to_csv: {str(e)}")
        raise

def upload_to_huggingface():
    try:
        dataset = Dataset.from_parquet(parquet_file)
        dataset.push_to_hub("katsukiai/DeepFocus-X3", token=HF_TOKEN)
    except Exception as e:
        log_error(f"Error uploading to Hugging Face: {str(e)}")
        raise

def generate_output(input_text):
    try:
        data = process_text_to_csv(input_text)
        upload_to_huggingface()
        return json.dumps(data, indent=2)
    except Exception as e:
        log_error(f"Error in generate_output: {str(e)}")
        return f"Error: {str(e)}"

def view_logs():
    try:
        log_files = os.listdir(log_dir)
        log_content = "".join(f"\n\n--- {log_file} ---\n\n{open(os.path.join(log_dir, log_file), 'r').read()}" for log_file in log_files)
        return log_content
    except Exception as e:
        log_error(f"Error in view_logs: {str(e)}")
        return f"Error: {str(e)}"

with gr.Blocks(title="DeepFocus-X3") as demo:
    gr.Markdown("# DeepFocus-X3")
    
    with gr.Tabs():
        with gr.TabItem("About"):
            gr.Markdown("## About DeepFocus-X3\nThis application processes text, tokenizes it, extracts unique words, generates meanings, and uploads the dataset to Hugging Face.")
        
        with gr.TabItem("Generate all"):
            input_text = gr.Textbox(label="Input Text", lines=10)
            output_json = gr.Textbox(label="Output JSON", lines=10)
            generate_btn = gr.Button("Generate and Upload")
            generate_btn.click(fn=generate_output, inputs=input_text, outputs=output_json)
        
        with gr.TabItem("Logs"):
            gr.Markdown("## Report using Logs")
            log_output = gr.Textbox(label="Log Content", lines=20)
            view_logs_btn = gr.Button("View Logs")
            view_logs_btn.click(fn=view_logs, inputs=None, outputs=log_output)

demo.launch()