app.py

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

This application provides a web interface for the RAG chatbot with OAuth authentication.
It uses Hugging Face Inference API with OAuth tokens for authentication.
"""

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

# Set up logging first (before any logger usage)
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Import from bot.py - wrap in try/except to handle import errors gracefully
try:
    from bot import RAGBot, parse_args, Chunk
    BOT_AVAILABLE = True
except ImportError as e:
    logger.error(f"Failed to import bot module: {e}")
    BOT_AVAILABLE = False
    # Create dummy classes so the module can still load
    class RAGBot:
        pass
    class Chunk:
        pass
    def parse_args():
        return None

# For Hugging Face Inference API
try:
    from huggingface_hub import InferenceClient
    HF_INFERENCE_AVAILABLE = True
except ImportError:
    HF_INFERENCE_AVAILABLE = False
    logger.warning("huggingface_hub not available, InferenceClient will not work")

# 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 InferenceAPIBot:
    """Wrapper that uses Hugging Face Inference API with OAuth token"""
    
    def __init__(self, bot: RAGBot):
        """Initialize with a RAGBot (for vector DB)"""
        self.bot = bot  # Use bot for vector DB and formatting
        self.current_model = bot.args.model
        logger.info(f"InferenceAPIBot initialized with model: {self.current_model}")
    
    def _get_client(self, hf_token: Optional[str] = None) -> InferenceClient:
        """Create InferenceClient with token (can be None for public models)"""
        if hf_token:
            return InferenceClient(token=hf_token)
        else:
            # Try without token (works for public models)
            return InferenceClient()
    
    @property
    def args(self):
        """Access args from the wrapped bot"""
        return self.bot.args
    
    def generate_answer(self, prompt: str, hf_token: Optional[str] = None, **kwargs) -> str:
        """Generate answer using Inference API"""
        try:
            max_tokens = kwargs.get('max_new_tokens', 512)
            temperature = kwargs.get('temperature', 0.2)
            top_p = kwargs.get('top_p', 0.9)
            
            # Create client with token
            client = self._get_client(hf_token)
            
            # Use text_generation API directly
            logger.info(f"Calling Inference API for model: {self.current_model} (token: {'provided' if hf_token else 'not provided'})")
            response = client.text_generation(
                prompt,
                model=self.current_model,
                max_new_tokens=max_tokens,
        temperature=temperature,
        top_p=top_p,
                return_full_text=False,
            )
            logger.info(f"Inference API response received (length: {len(response) if response else 0})")
            return response
        except Exception as e:
            error_msg = str(e).lower()
            logger.error(f"Error calling Inference API: {e}", exc_info=True)
            
            # Provide helpful error messages
            if 'authentication' in error_msg or 'token' in error_msg or '401' in error_msg or '403' in error_msg:
                return "⚠️ **Authentication Required**\n\nThis model requires authentication. Please log in using the Hugging Face login button in the sidebar, or ensure you have a valid HF_TOKEN set."
            elif 'not found' in error_msg or '404' in error_msg:
                return f"⚠️ **Model Not Found**\n\nThe model '{self.current_model}' could not be found. Please check the model name or try a different model."
            else:
                return f"⚠️ **Error generating answer**\n\n{str(e)}\n\nPlease check the logs for more details or try again."
    
    def enhance_readability(self, answer: str, target_level: str = "middle_school", hf_token: Optional[str] = None) -> Tuple[str, float]:
        """Enhance readability using Inference API"""
        try:
            # Define prompts for different reading levels
            if target_level == "middle_school":
                level_description = "middle school reading level (ages 12-14, 6th-8th grade)"
                instructions = """
- Use simpler medical terms or explain them
- Medium-length sentences
- Clear, structured explanations
- Keep important medical information accessible"""
            elif target_level == "high_school":
                level_description = "high school reading level (ages 15-18, 9th-12th grade)"
                instructions = """
- Use appropriate medical terminology with context
- Varied sentence length
- Comprehensive yet accessible explanations
- Maintain technical accuracy while ensuring clarity"""
            elif target_level == "college":
                level_description = "college reading level (undergraduate level, ages 18-22)"
                instructions = """
- Use standard medical terminology with brief explanations
- Professional and clear writing style
- Include relevant clinical context
- Maintain scientific accuracy and precision
- Appropriate for undergraduate students in health sciences"""
            elif target_level == "doctoral":
                level_description = "doctoral/professional reading level (graduate level, medical professionals)"
                instructions = """
- Use advanced medical and scientific terminology
- Include detailed clinical and research context
- Reference specific mechanisms, pathways, and evidence
- Provide comprehensive technical explanations
- Appropriate for medical professionals, researchers, and graduate students
- Include nuanced discussions of clinical implications and research findings"""
            else:
                raise ValueError(f"Unknown target_level: {target_level}")
            
            system_message = f"""You are a helpful medical assistant who specializes in explaining complex medical information at appropriate reading levels. Rewrite the following medical answer for {level_description}:
{instructions}
- Keep the same important information but adapt the complexity
- Provide context for technical terms
- Ensure the answer is informative yet understandable"""
            
            user_message = f"Please rewrite this medical answer for {level_description}:\n\n{answer}"
            
            # Combine system and user messages for text generation
            combined_prompt = f"{system_message}\n\n{user_message}"
            logger.info(f"Enhancing readability for {target_level} level")
            
            # Create client with token
            client = self._get_client(hf_token)
            
            max_tokens = 512 if target_level in ["college", "doctoral"] else 384
            temperature = 0.4 if target_level in ["college", "doctoral"] else 0.3
            
            enhanced_answer = client.text_generation(
                combined_prompt,
                model=self.current_model,
                max_new_tokens=max_tokens,
                temperature=temperature,
                return_full_text=False,
            )
            # Clean the answer (same as bot.py)
            cleaned = self.bot._clean_readability_answer(enhanced_answer, target_level)
            
            # Calculate Flesch score
            try:
                flesch_score = textstat.flesch_kincaid_grade(cleaned)
            except:
                flesch_score = 0.0
            
            return cleaned, flesch_score
        except Exception as e:
            logger.error(f"Error enhancing readability: {e}", exc_info=True)
            return answer, 0.0
    
    # Delegate other methods to bot
    def format_prompt(self, context_chunks: List[Chunk], question: str) -> str:
        return self.bot.format_prompt(context_chunks, question)
    
    def retrieve_with_scores(self, query: str, k: int) -> Tuple[List[Chunk], List[float]]:
        return self.bot.retrieve_with_scores(query, k)
    
    def _categorize_question(self, question: str) -> str:
        return self.bot._categorize_question(question)
    
    @property
    def vector_retriever(self):
        return self.bot.vector_retriever


class GradioRAGInterface:
    """Wrapper class to integrate RAGBot with Gradio using OAuth"""
    
    def __init__(self, initial_bot: RAGBot):
        # Always use Inference API on Spaces
        if HF_INFERENCE_AVAILABLE:
            self.bot = InferenceAPIBot(initial_bot)
            self.use_inference_api = True
            logger.info("Using Hugging Face Inference API with OAuth")
        else:
            self.bot = initial_bot
            self.use_inference_api = False
            logger.warning("Inference API not available, falling back to local model")
        
        # Get current model from bot args
        self.current_model = self.bot.args.model if hasattr(self.bot, 'args') else getattr(self.bot, 'current_model', None)
        if self.current_model is None and hasattr(self.bot, 'bot'):
            self.current_model = self.bot.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"Switching model from {self.current_model} to {new_model_path}")
            
            if self.use_inference_api:
                # For Inference API, just update the model name
                self.bot.current_model = new_model_path
                self.current_model = new_model_path
                return f"✓ Model switched to: {model_short_name} (using Inference API)"
            else:
                # For local model, reload it
                self.bot.args.model = new_model_path
                
                # Clear old model from memory
                if hasattr(self.bot, 'model') and 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,
        hf_token: Optional[str] = None
    ) -> 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", "", "", ""
        
        # Note: Token is optional for public models, but required for gated models
        # We'll try to proceed without token first (for public models)
        # If it fails, we'll show an error message
        
        try:
            start_time = time.time()
            logger.info(f"Processing question: {question[:50]}...")
            
            # Reload model if changed
            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
            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)):
                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),
                '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
            logger.info("Generating original answer...")
            gen_start = time.time()
            prompt = self.bot.format_prompt(context_chunks, question)
            original_answer = self.bot.generate_answer(prompt, hf_token=hf_token, **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":
                answer, flesch_score = self.bot.enhance_readability(original_answer, target_level="middle_school", hf_token=hf_token)
            elif education_level == "high_school":
                answer, flesch_score = self.bot.enhance_readability(original_answer, target_level="high_school", hf_token=hf_token)
            elif education_level == "college":
                answer, flesch_score = self.bot.enhance_readability(original_answer, target_level="college", hf_token=hf_token)
            elif education_level == "doctoral":
                answer, flesch_score = self.bot.enhance_readability(original_answer, target_level="doctoral", hf_token=hf_token)
            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:
                cleaned_answer = re.sub(re.escape(token), '', cleaned_answer, flags=re.IGNORECASE)
            
            # Remove any remaining special token patterns
            cleaned_answer = re.sub(r'<\|[^|]+\|>', '', cleaned_answer)
            cleaned_answer = re.sub(r'^\*\*.*?\*\*.*?\n', '', cleaned_answer, flags=re.MULTILINE)
            cleaned_answer = re.sub(r'\n\s*\n\s*\n+', '\n\n', cleaned_answer)
            cleaned_answer = re.sub(r'^\s+|\s+$', '', cleaned_answer, flags=re.MULTILINE)
            cleaned_answer = cleaned_answer.strip()
            
            return (
                cleaned_answer,
                f"{flesch_score:.1f}",
                sources,
                similarity_scores_str,
                question_group
            )
            
        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 with OAuth"""
    
    try:
        interface = GradioRAGInterface(initial_bot)
    except Exception as e:
        logger.error(f"Failed to create GradioRAGInterface: {e}")
        with gr.Blocks(title="CGT-LLM-Beta RAG Chatbot") as demo:
            gr.Markdown(f"""
            # ⚠️ Initialization Error
            
            Failed to initialize the chatbot interface.
            
            **Error:** {str(e)}
            
            Please check the logs for more details.
            """)
        return demo
    
    # 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]
    
    # Create the Gradio interface
    try:
        with gr.Blocks(title="CGT-LLM-Beta RAG Chatbot") as demo:
            with gr.Sidebar():
                gr.LoginButton()
                gr.Markdown("### 🔐 Authentication")
                gr.Markdown("Please log in with your Hugging Face account to use the Inference API.")
            
            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
            gr.Markdown("### 💡 Example Questions")
            gr.Markdown(f"Select a question below to use it in the chatbot ({len(EXAMPLE_QUESTIONS)} questions - scrollable dropdown):")
            
            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
            )
            
            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 with OAuth token
            # In Gradio 5.x with hf_oauth enabled, the token is automatically injected via gr.Request
            def process_with_education_level(question, model, education, k, temp, max_tok, request: gr.Request = None):
                # Get OAuth token from the request if available
                # When hf_oauth is enabled, Gradio provides the token in the request
                token = None
                
                # Try to get token from request (OAuth token from logged-in user)
                try:
                    if request is not None:
                        # Check if request has client with hf_token attribute
                        if hasattr(request, 'client') and request.client is not None:
                            if hasattr(request.client, 'hf_token') and request.client.hf_token:
                                token = request.client.hf_token
                            elif hasattr(request.client, 'token') and request.client.token:
                                token = request.client.token
                        # Also check request headers
                        if not token and hasattr(request, 'headers') and request.headers:
                            auth_header = request.headers.get('authorization', '') or request.headers.get('Authorization', '')
                            if auth_header and auth_header.startswith('Bearer '):
                                token = auth_header[7:]
                except Exception as e:
                    logger.debug(f"Could not get token from request: {e}")
                
                # Fallback to environment variable if OAuth token not available
                # This allows the app to work even without user login (for public models)
                if not token:
                    token = os.getenv("HF_TOKEN") or os.getenv("HUGGING_FACE_HUB_TOKEN")
                
                education_key = EDUCATION_LEVELS[education]
                return interface.process_question(question, model, education_key, k, temp, max_tok, hf_token=token)
            
            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
                ]
            )
    except Exception as interface_error:
        logger.error(f"Error setting up Gradio interface components: {interface_error}", exc_info=True)
        import traceback
        error_trace = traceback.format_exc()
        with gr.Blocks(title="CGT-LLM-Beta RAG Chatbot") as demo:
            gr.Markdown(f"""
            # ⚠️ Interface Setup Error
            
            An error occurred while setting up the interface components.
            
            **Error:** {str(interface_error)}
            
            **Traceback:**
            ```
            {error_trace[:1000]}...
            ```
            
            Please check the logs for more details.
            """)
        return demo
    
    logger.info("Gradio interface created successfully")
    logger.info(f"Demo type: {type(demo)}, Demo ID: {id(demo)}")
    return demo


# Check if we're on Spaces
IS_SPACES = (
    os.getenv("SPACE_ID") is not None or 
    os.getenv("SYSTEM") == "spaces" or
    os.getenv("HF_SPACE_ID") is not None
)

# Initialize demo variable
demo = None

def _create_demo():
    """Create the demo - separated into function for better error handling"""
    try:
        logger.info("=" * 80)
        logger.info("Starting demo creation...")
        logger.info(f"IS_SPACES: {IS_SPACES}")
        logger.info(f"BOT_AVAILABLE: {BOT_AVAILABLE}")
        
        if not BOT_AVAILABLE:
            raise ImportError("bot module is not available - cannot create demo")
        
        # Initialize with default args
        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('--seed', type=int, default=42)
        
        args = parser.parse_args([])  # Empty args
        args.skip_model_loading = IS_SPACES  # Skip model loading on Spaces, use Inference API
        
        logger.info("Creating RAGBot...")
        bot = RAGBot(args)
        
        if bot.vector_retriever is None:
            raise Exception("Vector database not available")
        
        # Check if vector database has documents
        collection_stats = bot.vector_retriever.get_collection_stats()
        if collection_stats.get('total_chunks', 0) == 0:
            logger.warning("Vector database is empty. The chatbot may not find relevant documents.")
        
        logger.info("Creating interface...")
        demo = create_interface(bot)
        logger.info(f"Demo created successfully: {type(demo)}")
        return demo
        
    except Exception as bot_error:
        logger.error(f"Error initializing: {bot_error}", exc_info=True)
        import traceback
        error_trace = traceback.format_exc()
        logger.error(f"Full traceback: {error_trace}")
        
        with gr.Blocks(title="CGT-LLM-Beta RAG Chatbot") as error_demo:
            gr.Markdown(f"""
            # ⚠️ Initialization Error
            
            The chatbot encountered an error during initialization:
            
            **Error:** {str(bot_error)}
            
            **Possible causes:**
            - Missing vector database (chroma_db directory)
            - Missing dependencies
            - Configuration issues
            
            **Error Details:**
            ```
            {error_trace[:1000]}...
            ```
            """)
        logger.info(f"Error demo created: {type(error_demo)}")
        return error_demo

# Create demo at module level
try:
    if IS_SPACES:
        logger.info("Creating demo directly at module level for Spaces...")
    else:
        logger.info("Creating demo for local execution...")
    
    demo = _create_demo()
    
    if demo is None or not isinstance(demo, (gr.Blocks, gr.Interface)):
        raise ValueError(f"Demo creation returned invalid result: {type(demo)}")
    
    logger.info("Demo creation completed successfully")
except Exception as e:
    logger.error(f"CRITICAL: Error creating demo: {e}", exc_info=True)
    import traceback
    error_trace = traceback.format_exc()
    logger.error(f"Full traceback: {error_trace}")
    with gr.Blocks(title="CGT-LLM-Beta RAG Chatbot") as demo:
        gr.Markdown(f"""
        # Error Initializing Chatbot
        
        A critical error occurred while initializing the chatbot.
        
        **Error:** {str(e)}
        
        **Traceback:**
        ```
        {error_trace[:1500]}...
        ```
        
        Please check the logs for more details.
        """)
    logger.info(f"Fallback error demo created: {type(demo)}")

# Final verification
if demo is None:
    logger.error("CRITICAL: Demo variable is None! Creating fallback demo.")
    with gr.Blocks(title="CGT-LLM-Beta RAG Chatbot") as demo:
        gr.Markdown("# Error: Demo was not created properly\n\nPlease check the logs for details.")
elif not isinstance(demo, (gr.Blocks, gr.Interface)):
    logger.error(f"CRITICAL: Demo is not a valid Gradio object: {type(demo)}")
    with gr.Blocks(title="CGT-LLM-Beta RAG Chatbot") as demo:
        gr.Markdown(f"# Error: Invalid demo type\n\nDemo type: {type(demo)}\n\nPlease check the logs for details.")
else:
    logger.info(f"✅ Final demo check passed: demo type={type(demo)}")
    # Explicitly ensure demo is accessible for Spaces
    if IS_SPACES:
        logger.info(f"Spaces mode: Demo is ready and accessible")
        # Print confirmation for debugging
        print(f"DEMO_READY: {type(demo)}")
        print(f"DEMO_VALID: {isinstance(demo, (gr.Blocks, gr.Interface))}")
        # Ensure demo is in globals for Spaces to find it
        import sys
        current_module = sys.modules[__name__]
        if not hasattr(current_module, 'demo') or current_module.demo is not demo:
            current_module.demo = demo
            logger.info("Demo explicitly set in module namespace")

# For local execution only (not on Spaces)
if __name__ == "__main__":
    if not IS_SPACES:
        # For local use, we can launch it
        demo.launch()