app.py

"""
Gradio Chatbot Interface for CGT-LLM-Beta RAG System

This application provides a web interface for the RAG chatbot, allowing users to:
- Select different LLM models from a dropdown
- Choose education level for personalized answers (Middle School, High School, Professional, Improved)
- View answers with Flesch-Kincaid grade level scores
- See source documents and similarity scores for every answer

Usage:
    python app.py

IMPORTANT: Before using, update the MODEL_MAP dictionary with correct HuggingFace paths
for models that currently have placeholder paths (Llama-4-Scout, MediPhi, Phi-4-reasoning).

For Hugging Face Spaces:
    - Ensure vector database is built (run bot.py with indexing first)
    - Model will be loaded on startup
    - Access via the Gradio interface
"""

import gradio as gr
import argparse
import sys
import os
from typing import Tuple, Optional
import logging
import textstat
import torch

# Import from bot.py
from bot import RAGBot, parse_args

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

# Model mapping: short name -> full HuggingFace path
MODEL_MAP = {
    "Llama-3.2-3B-Instruct": "meta-llama/Llama-3.2-3B-Instruct",
    "Mistral-7B-Instruct-v0.2": "mistralai/Mistral-7B-Instruct-v0.2",
    "Llama-4-Scout-17B-16E-Instruct": "meta-llama/Llama-4-Scout-17B-16E-Instruct",  
    "MediPhi-Instruct": "microsoft/MediPhi-Instruct",  
    "MediPhi": "microsoft/MediPhi",  
    "Phi-4-reasoning": "microsoft/Phi-4-reasoning",  
}

# Education level mapping
EDUCATION_LEVELS = {
    "Middle School": "middle_school",
    "High School": "high_school",
    "College": "college",
    "Doctoral": "doctoral"
}

# Example questions from the results CSV (hardcoded for easy access)
EXAMPLE_QUESTIONS = [
    "Can a BRCA2 variant skip a generation?",
    "Can a PMS2 variant skip a generation?",
    "Can an EPCAM/MSH2 variant skip a generation?",
    "Can an MLH1 variant skip a generation?",
    "Can an MSH2 variant skip a generation?",
    "Can an MSH6 variant skip a generation?",
    "Can I pass this MSH2 variant to my kids?",
    "Can only women carry a BRCA inherited mutation?",
    "Does GINA cover life or disability insurance?",
    "Does having a BRCA1 mutation mean I will definitely have cancer?",
    "Does having a BRCA2 mutation mean I will definitely have cancer?",
    "Does having a PMS2 mutation mean I will definitely have cancer?",
    "Does having an EPCAM/MSH2 mutation mean I will definitely have cancer?",
    "Does having an MLH1 mutation mean I will definitely have cancer?",
    "Does having an MSH2 mutation mean I will definitely have cancer?",
    "Does having an MSH6 mutation mean I will definitely have cancer?",
    "Does this BRCA1 genetic variant affect my cancer treatment?",
    "Does this BRCA2 genetic variant affect my cancer treatment?",
    "Does this EPCAM/MSH2 genetic variant affect my cancer treatment?",
    "Does this MLH1 genetic variant affect my cancer treatment?",
    "Does this MSH2 genetic variant affect my cancer treatment?",
    "Does this MSH6 genetic variant affect my cancer treatment?",
    "Does this PMS2 genetic variant affect my cancer treatment?",
    "How can I cope with this diagnosis?",
    "How can I get my kids tested?",
    "How can I help others with my condition?",
    "How might my genetic test results change over time?",
    "I don't talk to my family/parents/sister/brother. How can I share this with them?",
    "I have a BRCA pathogenic variant and I want to have children, what are my options?",
    "Is genetic testing for my family members covered by insurance?",
    "Is new research being done on my condition?",
    "Is this BRCA1 variant something I inherited?",
    "Is this BRCA2 variant something I inherited?",
    "Is this EPCAM/MSH2 variant something I inherited?",
    "Is this MLH1 variant something I inherited?",
    "Is this MSH2 variant something I inherited?",
    "Is this MSH6 variant something I inherited?",
    "Is this PMS2 variant something I inherited?",
    "My relative doesn't have insurance. What should they do?",
    "People who test positive for a genetic mutation are they at risk of losing their health insurance?",
    "Should I contact my male and female relatives?",
    "Should my family members get tested?",
    "What are the Risks and Benefits of Risk-Reducing Surgeries for Lynch Syndrome?",
    "What are the recommendations for my family members if I have a BRCA1 mutation?",
    "What are the recommendations for my family members if I have a BRCA2 mutation?",
    "What are the recommendations for my family members if I have a PMS2 mutation?",
    "What are the recommendations for my family members if I have an EPCAM/MSH2 mutation?",
    "What are the recommendations for my family members if I have an MLH1 mutation?",
    "What are the recommendations for my family members if I have an MSH2 mutation?",
    "What are the recommendations for my family members if I have an MSH6 mutation?",
    "What are the surveillance and preventions I can take to reduce my risk of cancer or detecting cancer early if I have a BRCA mutation?",
    "What are the surveillance and preventions I can take to reduce my risk of cancer or detecting cancer early if I have an EPCAM/MSH2 mutation?",
    "What are the surveillance and preventions I can take to reduce my risk of cancer or detecting cancer early if I have an MSH2 mutation?",
    "What does a BRCA1 genetic variant mean for me?",
    "What does a BRCA2 genetic variant mean for me?",
    "What does a PMS2 genetic variant mean for me?",
    "What does an EPCAM/MSH2 genetic variant mean for me?",
    "What does an MLH1 genetic variant mean for me?",
    "What does an MSH2 genetic variant mean for me?",
    "What does an MSH6 genetic variant mean for me?",
    "What if I feel overwhelmed?",
    "What if I want to have children and have a hereditary cancer gene? What are my reproductive options?",
    "What if a family member doesn't want to get tested?",
    "What is Lynch Syndrome?",
    "What is my cancer risk if I have BRCA1 Hereditary Breast and Ovarian Cancer syndrome?",
    "What is my cancer risk if I have BRCA2 Hereditary Breast and Ovarian Cancer syndrome?",
    "What is my cancer risk if I have MLH1 Lynch syndrome?",
    "What is my cancer risk if I have MSH2 or EPCAM-associated Lynch syndrome?",
    "What is my cancer risk if I have MSH6 Lynch syndrome?",
    "What is my cancer risk if I have PMS2 Lynch syndrome?",
    "What other resources are available to help me?",
    "What screening tests do you recommend for BRCA1 carriers?",
    "What screening tests do you recommend for BRCA2 carriers?",
    "What screening tests do you recommend for EPCAM/MSH2 carriers?",
    "What screening tests do you recommend for MLH1 carriers?",
    "What screening tests do you recommend for MSH2 carriers?",
    "What screening tests do you recommend for MSH6 carriers?",
    "What screening tests do you recommend for PMS2 carriers?",
    "What steps can I take to manage my cancer risk if I have Lynch syndrome?",
    "What types of cancers am I at risk for with a BRCA1 mutation?",
    "What types of cancers am I at risk for with a BRCA2 mutation?",
    "What types of cancers am I at risk for with a PMS2 mutation?",
    "What types of cancers am I at risk for with an EPCAM/MSH2 mutation?",
    "What types of cancers am I at risk for with an MLH1 mutation?",
    "What types of cancers am I at risk for with an MSH2 mutation?",
    "What types of cancers am I at risk for with an MSH6 mutation?",
    "Where can I find a genetic counselor?",
    "Which of my relatives are at risk?",
    "Who are my first-degree relatives?",
    "Who do my family members call to have genetic testing?",
    "Why do some families with Lynch syndrome have more cases of cancer than others?",
    "Why should I share my BRCA1 genetic results with family?",
    "Why should I share my BRCA2 genetic results with family?",
    "Why should I share my EPCAM/MSH2 genetic results with family?",
    "Why should I share my MLH1 genetic results with family?",
    "Why should I share my MSH2 genetic results with family?",
    "Why should I share my MSH6 genetic results with family?",
    "Why should I share my PMS2 genetic results with family?",
    "Why would my relatives want to know if they have this? What can they do about it?",
    "Will my insurance cover testing for my parents/brother/sister?",
    "Will this affect my health insurance?",
]


class GradioRAGInterface:
    """Wrapper class to integrate RAGBot with Gradio"""
    
    def __init__(self, initial_bot: RAGBot):
        self.bot = initial_bot
        self.current_model = initial_bot.args.model
        self.data_dir = initial_bot.args.data_dir
        logger.info("GradioRAGInterface initialized")
    
    def _find_file_path(self, filename: str) -> str:
        """Find the full file path for a given filename"""
        from pathlib import Path
        data_path = Path(self.data_dir)
        
        if not data_path.exists():
            return ""
        
        # Search for the file recursively
        for file_path in data_path.rglob(filename):
            return str(file_path)
        
        return ""
    
    def reload_model(self, model_short_name: str) -> str:
        """Reload the model when user selects a different one"""
        if model_short_name not in MODEL_MAP:
            return f"Error: Unknown model '{model_short_name}'"
        
        new_model_path = MODEL_MAP[model_short_name]
        
        # If same model, no need to reload
        if new_model_path == self.current_model:
            return f"Model already loaded: {model_short_name}"
        
        try:
            logger.info(f"Reloading model from {self.current_model} to {new_model_path}")
            
            # Update args
            self.bot.args.model = new_model_path
            
            # Clear old model from memory
            if self.bot.model is not None:
                del self.bot.model
                del self.bot.tokenizer
                torch.cuda.empty_cache() if torch.cuda.is_available() else None
            
            # Load new model
            self.bot._load_model()
            self.current_model = new_model_path
            
            return f"✓ Model loaded: {model_short_name}"
        except Exception as e:
            logger.error(f"Error reloading model: {e}", exc_info=True)
            return f"✗ Error loading model: {str(e)}"
    
    def process_question(
        self,
        question: str,
        model_name: str,
        education_level: str,
        k: int,
        temperature: float,
        max_tokens: int
    ) -> Tuple[str, str, str, str, str]:
        """
        Process a single question and return formatted results
        
        Returns:
            Tuple of (answer, flesch_score, sources, similarity_scores, question_category)
        """
        import time
        
        if not question or not question.strip():
            return "Please enter a question.", "N/A", "", "", ""
        
        try:
            start_time = time.time()
            logger.info(f"Processing question: {question[:50]}...")
            
            # Reload model if changed (this can take 1-3 minutes)
            if model_name in MODEL_MAP:
                model_path = MODEL_MAP[model_name]
                if model_path != self.current_model:
                    logger.info(f"Model changed, reloading from {self.current_model} to {model_path}")
                    reload_status = self.reload_model(model_name)
                    if reload_status.startswith("✗"):
                        return f"Error: {reload_status}", "N/A", "", "", ""
                    logger.info(f"Model reloaded in {time.time() - start_time:.1f}s")
            
            # Update bot args for this query
            self.bot.args.k = k
            self.bot.args.temperature = temperature
            # Limit max_tokens for faster generation in Gradio
            self.bot.args.max_new_tokens = min(max_tokens, 512)  # Cap at 512 for faster responses
            
            # Categorize question
            logger.info("Categorizing question...")
            question_group = self.bot._categorize_question(question)
            
            # Retrieve relevant chunks with similarity scores
            logger.info("Retrieving relevant documents...")
            retrieve_start = time.time()
            context_chunks, similarity_scores = self.bot.retrieve_with_scores(question, k)
            logger.info(f"Retrieved {len(context_chunks)} chunks in {time.time() - retrieve_start:.2f}s")
            
            if not context_chunks:
                return (
                    "I don't have enough information to answer this question. Please try rephrasing or asking about a different topic.",
                    "N/A",
                    "No sources found",
                    "No matches found",
                    question_group
                )
            
            # Format similarity scores
            similarity_scores_str = ", ".join([f"{score:.3f}" for score in similarity_scores])
            
            # Format sources with chunk text and file paths
            sources_list = []
            for i, (chunk, score) in enumerate(zip(context_chunks, similarity_scores)):
                # Try to find the file path
                file_path = self._find_file_path(chunk.filename)
                
                source_info = f"""
{'='*80}
SOURCE {i+1} | Similarity: {score:.3f}
{'='*80}
📄 File: {chunk.filename}
📍 Path: {file_path if file_path else 'File path not found (search in Data Resources directory)'}
📊 Chunk: {chunk.chunk_id + 1}/{chunk.total_chunks} (Position: {chunk.start_pos}-{chunk.end_pos})

📝 Full Chunk Text:
{chunk.text}

"""
                sources_list.append(source_info)
            
            sources = "\n".join(sources_list)
            
            # Generation kwargs
            gen_kwargs = {
                'max_new_tokens': min(max_tokens, 512),  # Cap for faster responses
                'temperature': temperature,
                'top_p': self.bot.args.top_p,
                'repetition_penalty': self.bot.args.repetition_penalty
            }
            
            # Generate answer based on education level
            answer = ""
            flesch_score = 0.0
            
            # Generate original answer first (needed for all enhancement levels)
            logger.info("Generating original answer...")
            gen_start = time.time()
            prompt = self.bot.format_prompt(context_chunks, question)
            original_answer = self.bot.generate_answer(prompt, **gen_kwargs)
            logger.info(f"Original answer generated in {time.time() - gen_start:.1f}s")
            
            # Enhance based on education level
            logger.info(f"Enhancing answer for {education_level} level...")
            enhance_start = time.time()
            if education_level == "middle_school":
                # Simplify to middle school level
                answer, flesch_score = self.bot.enhance_readability(original_answer, target_level="middle_school")
                
            elif education_level == "high_school":
                # Simplify to high school level
                answer, flesch_score = self.bot.enhance_readability(original_answer, target_level="high_school")
                
            elif education_level == "college":
                # Enhance to college level
                answer, flesch_score = self.bot.enhance_readability(original_answer, target_level="college")
                
            elif education_level == "doctoral":
                # Enhance to doctoral/professional level
                answer, flesch_score = self.bot.enhance_readability(original_answer, target_level="doctoral")
            else:
                answer = "Invalid education level selected."
                flesch_score = 0.0
            
            logger.info(f"Answer enhanced in {time.time() - enhance_start:.1f}s")
            total_time = time.time() - start_time
            logger.info(f"Total processing time: {total_time:.1f}s")
            
            # Clean the answer - remove special tokens and formatting
            import re
            cleaned_answer = answer
            
            # Remove special tokens (case-insensitive)
            special_tokens = [
                "<|end|>",
                "<|endoftext|>",
                "<|end_of_text|>",
                "<|eot_id|>",
                "<|start_header_id|>",
                "<|end_header_id|>",
                "<|assistant|>",
                "<|endoftext|>",
                "<|end_of_text|>",
            ]
            for token in special_tokens:
                # Remove case-insensitive
                cleaned_answer = re.sub(re.escape(token), '', cleaned_answer, flags=re.IGNORECASE)
            
            # Remove any remaining special token patterns like <|...|>
            cleaned_answer = re.sub(r'<\|[^|]+\|>', '', cleaned_answer)
            
            # Remove any markdown-style headers that might have been added
            cleaned_answer = re.sub(r'^\*\*.*?\*\*.*?\n', '', cleaned_answer, flags=re.MULTILINE)
            
            # Clean up extra whitespace and newlines
            cleaned_answer = re.sub(r'\n\s*\n\s*\n+', '\n\n', cleaned_answer)  # Multiple newlines to double
            cleaned_answer = re.sub(r'^\s+|\s+$', '', cleaned_answer, flags=re.MULTILINE)  # Trim lines
            cleaned_answer = cleaned_answer.strip()
            
            # Return just the clean answer (no headers or metadata)
            return (
                cleaned_answer,
                f"{flesch_score:.1f}",
                sources,
                similarity_scores_str,
                question_group  # Add question category as 5th return value
            )
            
        except Exception as e:
            logger.error(f"Error processing question: {e}", exc_info=True)
            return (
                f"An error occurred while processing your question: {str(e)}",
                "N/A",
                "",
                "",
                "Error"
            )


def create_interface(initial_bot: RAGBot) -> gr.Blocks:
    """Create and configure the Gradio interface"""
    
    interface = GradioRAGInterface(initial_bot)
    
    # Get initial model name from bot
    initial_model_short = None
    for short_name, full_path in MODEL_MAP.items():
        if full_path == initial_bot.args.model:
            initial_model_short = short_name
            break
    if initial_model_short is None:
        initial_model_short = list(MODEL_MAP.keys())[0]
    
    with gr.Blocks(title="CGT-LLM-Beta RAG Chatbot") as demo:
        gr.Markdown("""
        # 🧬 CGT-LLM-Beta: Genetic Counseling RAG Chatbot
        
        Ask questions about genetic counseling, cascade genetic testing, hereditary cancer syndromes, and related topics.
        
        The chatbot uses a Retrieval-Augmented Generation (RAG) system to provide evidence-based answers from medical literature.
        """)
        
        with gr.Row():
            with gr.Column(scale=2):
                question_input = gr.Textbox(
                    label="Your Question",
                    placeholder="e.g., What is Lynch Syndrome? What screening is recommended for BRCA1 carriers?",
                    lines=3
                )
                
                with gr.Row():
                    model_dropdown = gr.Dropdown(
                        choices=list(MODEL_MAP.keys()),
                        value=initial_model_short,
                        label="Select Model",
                        info="Choose which LLM model to use for generating answers"
                    )
                    
                    education_dropdown = gr.Dropdown(
                        choices=list(EDUCATION_LEVELS.keys()),
                        value=list(EDUCATION_LEVELS.keys())[0],
                        label="Education Level",
                        info="Select your education level for personalized answers"
                    )
                
                with gr.Accordion("Advanced Settings", open=False):
                    k_slider = gr.Slider(
                        minimum=1,
                        maximum=10,
                        value=5,
                        step=1,
                        label="Number of document chunks to retrieve (k)"
                    )
                    temperature_slider = gr.Slider(
                        minimum=0.1,
                        maximum=1.0,
                        value=0.2,
                        step=0.1,
                        label="Temperature (lower = more focused)"
                    )
                    max_tokens_slider = gr.Slider(
                        minimum=128,
                        maximum=1024,
                        value=512,
                        step=128,
                        label="Max Tokens (lower = faster responses)"
                    )
                
                submit_btn = gr.Button("Ask Question", variant="primary", size="lg")
                
            with gr.Column(scale=3):
                answer_output = gr.Textbox(
                    label="Answer",
                    lines=20,
                    interactive=False,
                    elem_classes=["answer-box"]
                )
                
                with gr.Row():
                    flesch_output = gr.Textbox(
                        label="Flesch-Kincaid Grade Level",
                        value="N/A",
                        interactive=False,
                        scale=1
                    )
                    
                    similarity_output = gr.Textbox(
                        label="Similarity Scores",
                        value="",
                        interactive=False,
                        scale=1
                    )
                    
                    category_output = gr.Textbox(
                        label="Question Category",
                        value="",
                        interactive=False,
                        scale=1
                    )
                
                sources_output = gr.Textbox(
                    label="Source Documents (with Chunk Text)",
                    lines=15,
                    interactive=False,
                    info="Shows the retrieved document chunks with full text. File paths are shown for easy access."
                )
        
        # Example questions - all questions from the results CSV (scrollable)
        gr.Markdown("### 💡 Example Questions")
        gr.Markdown(f"Select a question below to use it in the chatbot ({len(EXAMPLE_QUESTIONS)} questions - scrollable dropdown):")
        
        # Use Dropdown which is naturally scrollable with many options
        example_questions_dropdown = gr.Dropdown(
            choices=EXAMPLE_QUESTIONS,
            label="Example Questions",
            value=None,
            info="Open the dropdown and scroll through all questions. Select one to use it.",
            interactive=True,
            container=True,
            scale=1
        )
        
        # Update question input when dropdown selection changes
        def update_question_from_dropdown(selected_question):
            return selected_question if selected_question else ""
        
        example_questions_dropdown.change(
            fn=update_question_from_dropdown,
            inputs=example_questions_dropdown,
            outputs=question_input
        )
        
        # Footer
        gr.Markdown("""
        ---
        **Note:** This chatbot provides informational answers based on medical literature. 
        It is not a substitute for professional medical advice, diagnosis, or treatment. 
        Always consult with qualified healthcare providers for medical decisions.
        """)
        
        # Connect the submit button
        def process_with_education_level(question, model, education, k, temp, max_tok):
            education_key = EDUCATION_LEVELS[education]
            return interface.process_question(question, model, education_key, k, temp, max_tok)
        
        submit_btn.click(
            fn=process_with_education_level,
            inputs=[
                question_input,
                model_dropdown,
                education_dropdown,
                k_slider,
                temperature_slider,
                max_tokens_slider
            ],
            outputs=[
                answer_output,
                flesch_output,
                sources_output,
                similarity_output,
                category_output
            ]
        )
        
        # Also allow Enter key to submit
        question_input.submit(
            fn=process_with_education_level,
            inputs=[
                question_input,
                model_dropdown,
                education_dropdown,
                k_slider,
                temperature_slider,
                max_tokens_slider
            ],
            outputs=[
                answer_output,
                flesch_output,
                sources_output,
                similarity_output,
                category_output
            ]
        )
    
    return demo


def main():
    """Main function to launch the Gradio app"""
    # Parse arguments with defaults suitable for Gradio
    parser = argparse.ArgumentParser(description="Gradio Interface for CGT-LLM-Beta RAG Chatbot")
    
    # Model and database settings
    parser.add_argument('--model', type=str, default='meta-llama/Llama-3.2-3B-Instruct',
                       help='HuggingFace model name')
    parser.add_argument('--vector-db-dir', default='./chroma_db',
                       help='Directory for ChromaDB persistence')
    parser.add_argument('--data-dir', default='./Data Resources',
                       help='Directory containing documents (for indexing if needed)')
    
    # Generation parameters
    parser.add_argument('--max-new-tokens', type=int, default=1024,
                       help='Maximum new tokens to generate')
    parser.add_argument('--temperature', type=float, default=0.2,
                       help='Generation temperature')
    parser.add_argument('--top-p', type=float, default=0.9,
                       help='Top-p sampling parameter')
    parser.add_argument('--repetition-penalty', type=float, default=1.1,
                       help='Repetition penalty')
    
    # Retrieval parameters
    parser.add_argument('--k', type=int, default=5,
                       help='Number of chunks to retrieve per question')
    
    # Other settings
    parser.add_argument('--skip-indexing', action='store_true',
                       help='Skip document indexing (use existing vector DB)')
    parser.add_argument('--verbose', action='store_true',
                       help='Enable verbose logging')
    parser.add_argument('--share', action='store_true',
                       help='Create a public Gradio share link')
    parser.add_argument('--server-name', type=str, default='127.0.0.1',
                       help='Server name (0.0.0.0 for public access)')
    parser.add_argument('--server-port', type=int, default=7860,
                       help='Server port')
    
    args = parser.parse_args()
    
    # Set logging level
    if args.verbose:
        logging.getLogger().setLevel(logging.DEBUG)
    
    logger.info("Initializing RAGBot for Gradio interface...")
    logger.info(f"Model: {args.model}")
    logger.info(f"Vector DB: {args.vector_db_dir}")
    
    try:
        # Initialize bot
        bot = RAGBot(args)
        
        # Check if vector database exists and has documents
        collection_stats = bot.vector_retriever.get_collection_stats()
        if collection_stats.get('total_chunks', 0) == 0:
            logger.warning("Vector database is empty. You may need to run indexing first:")
            logger.warning("  python bot.py --data-dir './Data Resources' --vector-db-dir './chroma_db'")
            logger.warning("Continuing anyway - the chatbot will work but may not find relevant documents.")
        
        # Create and launch Gradio interface
        demo = create_interface(bot)
        
        # For Hugging Face Spaces, just return the demo (they handle launching)
        # For local use, launch it
        if os.getenv("SPACE_ID") or os.getenv("SYSTEM") == "spaces":
            # Running on Hugging Face Spaces - return demo for Spaces to launch
            return demo
        else:
            # Running locally
            logger.info(f"Launching Gradio interface on http://{args.server_name}:{args.server_port}")
            demo.launch(
                server_name=args.server_name,
                server_port=args.server_port,
                share=args.share
            )
        
    except KeyboardInterrupt:
        logger.info("Interrupted by user")
        sys.exit(0)
    except Exception as e:
        logger.error(f"Error launching Gradio app: {e}", exc_info=True)
        sys.exit(1)


# For Hugging Face Spaces: create demo at module level
# Spaces will import this module and look for a 'demo' variable
def create_demo_for_spaces():
    """Create demo for Hugging Face Spaces"""
    try:
        # Initialize with default args for Spaces
        parser = argparse.ArgumentParser()
        parser.add_argument('--model', type=str, default='meta-llama/Llama-3.2-3B-Instruct')
        parser.add_argument('--vector-db-dir', default='./chroma_db')
        parser.add_argument('--data-dir', default='./Data Resources')
        parser.add_argument('--max-new-tokens', type=int, default=1024)
        parser.add_argument('--temperature', type=float, default=0.2)
        parser.add_argument('--top-p', type=float, default=0.9)
        parser.add_argument('--repetition-penalty', type=float, default=1.1)
        parser.add_argument('--k', type=int, default=5)
        parser.add_argument('--skip-indexing', action='store_true', default=True)
        parser.add_argument('--verbose', action='store_true', default=False)
        parser.add_argument('--share', action='store_true', default=False)
        parser.add_argument('--server-name', type=str, default='0.0.0.0')
        parser.add_argument('--server-port', type=int, default=7860)
        parser.add_argument('--seed', type=int, default=42)
        
        args = parser.parse_args([])  # Empty args for Spaces
        bot = RAGBot(args)
        return create_interface(bot)
    except Exception as e:
        logger.error(f"Error creating demo for Spaces: {e}", exc_info=True)
        # Return a simple error demo
        with gr.Blocks() as error_demo:
            gr.Markdown(f"# Error Initializing Chatbot\n\nAn error occurred: {str(e)}")
        return error_demo

# Create demo at module level for Hugging Face Spaces
# This is what Spaces will import and use
demo = create_demo_for_spaces()

# For local execution
if __name__ == "__main__":
    main()