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, List
import logging
import textstat
import torch

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

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

# 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 instead of loading models locally"""
    
    def __init__(self, bot: RAGBot, hf_token: str):
        """Initialize with a RAGBot (for vector DB) and HF token for Inference API"""
        self.bot = bot  # Use bot for vector DB and formatting
        self.client = InferenceClient(api_key=hf_token)
        self.current_model = bot.args.model
        # Don't set args as attribute - access via bot.args instead
        logger.info(f"InferenceAPIBot initialized with model: {self.current_model}")
    
    @property
    def args(self):
        """Access args from the wrapped bot"""
        return self.bot.args
    
    def generate_answer(self, prompt: str, **kwargs) -> str:
        """Generate answer using Inference API"""
        try:
            # Convert prompt to chat format
            messages = [{"role": "user", "content": prompt}]
            
            # Call Inference API
            completion = self.client.chat.completions.create(
                model=self.current_model,
                messages=messages,
                max_tokens=kwargs.get('max_new_tokens', 512),
                temperature=kwargs.get('temperature', 0.2),
                top_p=kwargs.get('top_p', 0.9),
            )
            
            answer = completion.choices[0].message.content
            return answer
        except Exception as e:
            logger.error(f"Error calling Inference API: {e}", exc_info=True)
            return f"Error generating answer: {str(e)}"
    
    def enhance_readability(self, answer: str, target_level: str = "middle_school") -> Tuple[str, float]:
        """Enhance readability using Inference API"""
        try:
            # Define prompts for different reading levels (same as bot.py)
            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}")
            
            # Create messages for chat API
            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}"
            
            messages = [
                {"role": "system", "content": system_message},
                {"role": "user", "content": user_message}
            ]
            
            # Call Inference API
            completion = self.client.chat.completions.create(
                model=self.current_model,
                messages=messages,
                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 = completion.choices[0].message.content
            # 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 args(self):
        return self.bot.args
    
    @property
    def vector_retriever(self):
        return self.bot.vector_retriever


class GradioRAGInterface:
    """Wrapper class to integrate RAGBot with Gradio"""
    
    def __init__(self, initial_bot: RAGBot, use_inference_api: bool = False):
        # Check if we should use Inference API (on Spaces)
        if use_inference_api and HF_INFERENCE_AVAILABLE:
            hf_token = os.getenv("HF_TOKEN") or os.getenv("HUGGING_FACE_HUB_TOKEN")
            if hf_token:
                self.bot = InferenceAPIBot(initial_bot, hf_token)
                self.use_inference_api = True
                logger.info("Using Hugging Face Inference API")
            else:
                logger.warning("HF_TOKEN not found, falling back to local model")
                self.bot = initial_bot
                self.use_inference_api = False
        else:
            self.bot = initial_bot
            self.use_inference_api = False
        
        # Get current model from bot args (not a direct attribute)
        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'):
            # If using InferenceAPIBot, get from the wrapped 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
                # Update args
                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
    ) -> 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, use_inference_api: bool = False) -> gr.Blocks:
    """Create and configure the Gradio interface"""
    
    # Use Inference API on Spaces, local model otherwise
    if use_inference_api is None:
        use_inference_api = os.getenv("SPACE_ID") is not None or os.getenv("SYSTEM") == "spaces"
    
    interface = GradioRAGInterface(initial_bot, use_inference_api=use_inference_api)
    
    # 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 local use, launch it
        # (On Spaces, the demo is already created at module level)
        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
# Following the HF Spaces pattern: create the Gradio app directly at module level
# Spaces will import this module and look for a Gradio Blocks/Interface object
# Pattern: demo = gr.Interface(...) or demo = gr.Blocks(...)
# DO NOT call demo.launch() - Spaces handles that automatically

# Check if we're on Spaces (be more permissive - check multiple env vars)
IS_SPACES = (
    os.getenv("SPACE_ID") is not None or 
    os.getenv("SYSTEM") == "spaces" or
    os.getenv("HF_SPACE_ID") is not None
)

# Create demo at module level (like HF docs example)
# Initialize demo variable to None first (safety measure)  
demo = None

# Create demo at module level (like HF docs example)
# This ensures Spaces can always find it when importing the module
try:
    if IS_SPACES:
        logger.info("Initializing for Hugging Face Spaces...")
    else:
        logger.info("Initializing for local execution...")
    
    # 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('--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
    args.skip_model_loading = IS_SPACES  # Skip model loading on Spaces, use Inference API
    
    # Create bot
    bot = RAGBot(args)
    
    if bot.vector_retriever is None:
        raise Exception("Vector database not available")
    
    # Create the demo interface directly at module level (like HF docs example)
    demo = create_interface(bot, use_inference_api=IS_SPACES)
    logger.info(f"Demo created successfully: {type(demo)}")
    # Explicitly verify it's a valid Gradio object
    if not isinstance(demo, (gr.Blocks, gr.Interface)):
        raise TypeError(f"Demo is not a valid Gradio object: {type(demo)}")
    logger.info("Demo validation passed - ready for Spaces")
except Exception as e:
    logger.error(f"Error creating demo: {e}", exc_info=True)
    import traceback
    logger.error(f"Traceback: {traceback.format_exc()}")
    # Create a fallback error demo so Spaces doesn't show blank
    with gr.Blocks() as demo:
        gr.Markdown(f"# Error Initializing Chatbot\n\nAn error occurred while initializing the chatbot.\n\nError: {str(e)}\n\nPlease check the logs for details.")
    logger.info(f"Error demo created: {type(demo)}")

# Final verification - ensure demo exists and is valid
if demo is None:
    logger.error("CRITICAL: Demo variable is None!")
    with gr.Blocks() 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() 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)}")
    # Explicit print to ensure demo is accessible (Spaces might check this)
    print(f"DEMO_VARIABLE_SET: {type(demo)}")

# For local execution only (not on Spaces)
if __name__ == "__main__":
    if not IS_SPACES:
        main()