import gradio as gr
from gradio_client import Client
import os
import zipfile
from huggingface_hub import HfApi
import logging
import time  # Import time module for adding delays

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

# Function to call the API and get the result
def call_api(prompt):
    try:
        # Reload the Gradio client for each chunk
        client = Client("MiniMaxAI/MiniMax-Text-01")
        logger.info(f"Calling API with prompt: {prompt[:100]}...")  # Log the first 100 chars of the prompt
        result = client.predict(
            message=prompt,
            max_tokens=12800,
            temperature=0.1,
            top_p=0.9,
            api_name="/chat"
        )
        logger.info("API call successful.")
        return result
    except Exception as e:
        logger.error(f"API call failed: {e}")
        raise gr.Error(f"API call failed: {str(e)}")

# Function to segment the text file into chunks of 1500 words
def segment_text(file_path):
    try:
        logger.info(f"Reading file: {file_path}")
        # Try reading with UTF-8 encoding first
        with open(file_path, "r", encoding="utf-8") as f:
            text = f.read()
        logger.info("File read successfully with UTF-8 encoding.")
    except UnicodeDecodeError:
        logger.warning("UTF-8 encoding failed. Trying latin-1 encoding.")
        # Fallback to latin-1 encoding if UTF-8 fails
        with open(file_path, "r", encoding="latin-1") as f:
            text = f.read()
        logger.info("File read successfully with latin-1 encoding.")
    except Exception as e:
        logger.error(f"Failed to read file: {e}")
        raise gr.Error(f"Failed to read file: {str(e)}")
    
    # Split the text into chunks of 1500 words
    words = text.split()
    chunks = [" ".join(words[i:i + 1500]) for i in range(0, len(words), 1250)]
    logger.info(f"Segmented text into {len(chunks)} chunks.")
    return chunks

# Function to process the text file and make API calls with rate limiting
def process_text(file, prompt):
    try:
        logger.info("Starting text processing...")
        
        # Segment the text file into chunks
        file_path = file.name if hasattr(file, "name") else file
        chunks = segment_text(file_path)
        
        # Initialize Hugging Face API
        hf_api = HfApi(token=os.environ.get("HUGGINGFACE_TOKEN"))
        if not hf_api.token:
            raise ValueError("Hugging Face token not found in environment variables.")
        
        # Repository name on Hugging Face Hub
        repo_name = "TeacherPuffy/book4"
        
        # Process each chunk with a 15-second delay between API calls
        results = []
        for idx, chunk in enumerate(chunks):
            logger.info(f"Processing chunk {idx + 1}/{len(chunks)}")
            try:
                # Call the API
                result = call_api(f"{prompt}\n\n{chunk}")
                results.append(result)
                logger.info(f"Chunk {idx + 1} processed successfully.")
                
                # Save the result to a file
                os.makedirs("outputs", exist_ok=True)
                output_file = f"outputs/output_{idx}.txt"
                with open(output_file, "w", encoding="utf-8") as f:
                    f.write(result)
                logger.info(f"Saved result to {output_file}")
                
                # Upload the chunk as an individual text file to Hugging Face
                try:
                    logger.info(f"Uploading chunk {idx + 1} to Hugging Face...")
                    hf_api.upload_file(
                        path_or_fileobj=output_file,
                        path_in_repo=f"output_{idx}.txt",  # File name in the repository
                        repo_id=repo_name,
                        repo_type="dataset",
                    )
                    logger.info(f"Chunk {idx + 1} uploaded to Hugging Face successfully.")
                except Exception as e:
                    logger.error(f"Failed to upload chunk {idx + 1} to Hugging Face: {e}")
                    raise gr.Error(f"Failed to upload chunk {idx + 1} to Hugging Face: {str(e)}")
                
                # Wait 15 seconds before the next API call
                if idx < len(chunks) - 1:  # No need to wait after the last chunk
                    logger.info("Waiting 15 seconds before the next API call...")
                    time.sleep(15)
                
            except Exception as e:
                logger.error(f"Failed to process chunk {idx + 1}: {e}")
                raise gr.Error(f"Failed to process chunk {idx + 1}: {str(e)}")
        
        # Create a ZIP file of all outputs
        try:
            logger.info("Creating ZIP file...")
            with zipfile.ZipFile("outputs.zip", "w") as zipf:
                for root, dirs, files in os.walk("outputs"):
                    for file in files:
                        zipf.write(os.path.join(root, file), file)
            logger.info("ZIP file created successfully.")
        except Exception as e:
            logger.error(f"Failed to create ZIP file: {e}")
            raise gr.Error(f"Failed to create ZIP file: {str(e)}")
        
        return "outputs.zip", "All chunks processed and uploaded to Hugging Face. ZIP file created."
    
    except Exception as e:
        logger.error(f"An error occurred during processing: {e}")
        raise gr.Error(f"An error occurred: {str(e)}")

# Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("## Text File Processor with Rate-Limited API Calls")
    with gr.Row():
        file_input = gr.File(label="Upload Text File")
        prompt_input = gr.Textbox(label="Enter Prompt")
    with gr.Row():
        output_zip = gr.File(label="Download ZIP File")
        output_message = gr.Textbox(label="Status Message")
    submit_button = gr.Button("Submit")
    
    submit_button.click(
        process_text,
        inputs=[file_input, prompt_input],
        outputs=[output_zip, output_message]
    )

# Launch the Gradio app with a public link
demo.launch(share=True)