import logging
from transformers import pipeline, T5Tokenizer, T5ForConditionalGeneration

# Logging Ayarları
logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
logger = logging.getLogger(__name__)

pipe = pipeline("text2text-generation", model="google-t5/t5-base", device="cpu")
pipe.model.config.pad_token_id = pipe.tokenizer.eos_token_id 

def generate_lesson_from_transcript(doc_text):
    try:
        logger.info("Generating lesson from transcript.")
        generated_text = pipe(doc_text, max_length=100, truncation=True)[0]['generated_text']
        output_path = "/tmp/generated_output.txt"
        
        with open(output_path, "w") as file:
            file.write(generated_text)
        
        logger.info(f"Lesson generation successful. Output saved at: {output_path}")
        return generated_text, output_path

    except Exception as e:
        logger.error(f"Error occurred during lesson generation: {str(e)}")
        return "An error occurred", None

def split_text_into_chunks(text, chunk_size=1000):
    words = text.split()
    chunks = []
    for i in range(0, len(words), chunk_size):
        chunk = ' '.join(words[i:i+chunk_size])
        chunks.append(chunk)
    return chunks

def generate_lesson_from_chunks(chunks):
    generated_texts = []
    for chunk in chunks:
        try:
            generated_text = pipe(chunk, max_length=500, truncation=True)[0]['generated_text']
            generated_texts.append(generated_text)
        except Exception as e:
            print(f"Error in chunk processing: {str(e)}")
            continue  # Hata durumunda işlemi sürdür
    return ' '.join(generated_texts)

def process_large_text(text):
    chunks = split_text_into_chunks(text, chunk_size=1000)
    generated_text = generate_lesson_from_chunks(chunks)
    return generated_text