#!/usr/bin/env python3
"""
Textilindo AI Assistant - Simple Hugging Face Spaces Version
"""

import gradio as gr
import os
import json
import logging

# Setup logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class TextilindoAI:
    def __init__(self):
        self.dataset = []
        self.load_all_datasets()
        logger.info(f"Total examples loaded: {len(self.dataset)}")

    def load_all_datasets(self):
        """Load all JSONL datasets from the data directory"""
        base_dir = os.path.dirname(__file__)
        data_dir = os.path.join(base_dir, "data")
        
        if not os.path.exists(data_dir):
            logger.warning(f"Data directory not found: {data_dir}")
            return
        
        logger.info(f"Found data directory: {data_dir}")
        
        # Load all JSONL files
        for filename in os.listdir(data_dir):
            if filename.endswith('.jsonl'):
                filepath = os.path.join(data_dir, filename)
                file_examples = 0
                try:
                    with open(filepath, 'r', encoding='utf-8') as f:
                        for line in f:
                            line = line.strip()
                            if line:
                                try:
                                    data = json.loads(line)
                                    data['source'] = filename
                                    self.dataset.append(data)
                                    file_examples += 1
                                except json.JSONDecodeError as e:
                                    logger.warning(f"Invalid JSON in {filename}: {e}")
                                    continue
                    
                    logger.info(f"Loaded {filename}: {file_examples} examples")
                except Exception as e:
                    logger.error(f"Error loading {filename}: {e}")

    def chat(self, message):
        """Simple chat function"""
        if not message:
            return "Please enter a message."
        
        # Simple response based on dataset
        if len(self.dataset) > 0:
            return f"Hello! I have {len(self.dataset)} examples in my knowledge base. You asked: '{message}'. How can I help you with Textilindo?"
        else:
            return "I'm sorry, I don't have access to my knowledge base right now."

# Initialize AI assistant
ai = TextilindoAI()

# Create simple interface
def create_interface():
    with gr.Blocks(title="Textilindo AI Assistant") as interface:
        gr.Markdown("# 🤖 Textilindo AI Assistant")
        gr.Markdown("AI-powered customer service for Textilindo")
        
        with gr.Row():
            with gr.Column():
                message_input = gr.Textbox(
                    label="Your Message",
                    placeholder="Ask me anything about Textilindo...",
                    lines=3
                )
                submit_btn = gr.Button("Send Message", variant="primary")
            
            with gr.Column():
                response_output = gr.Textbox(
                    label="AI Response",
                    lines=10,
                    interactive=False
                )
        
        # Event handlers
        submit_btn.click(
            fn=ai.chat,
            inputs=message_input,
            outputs=response_output
        )
        
        message_input.submit(
            fn=ai.chat,
            inputs=message_input,
            outputs=response_output
        )
        
        # Add examples
        gr.Examples(
            examples=[
                "Dimana lokasi Textilindo?",
                "Apa saja produk yang dijual di Textilindo?",
                "Jam berapa Textilindo buka?",
                "Bagaimana cara menghubungi Textilindo?"
            ],
            inputs=message_input
        )
        
        # Add footer with stats
        gr.Markdown(f"**Dataset loaded:** {len(ai.dataset)} examples")
    
    return interface

# Launch the interface
if __name__ == "__main__":
    logger.info("Starting Textilindo AI Assistant...")
    logger.info(f"Dataset loaded: {len(ai.dataset)} examples")
    
    interface = create_interface()
    interface.launch()