# Acknowledgement: This demo code is adapted from the original Hugging Face Space "ContextCite"
# (https://huggingface.co/spaces/contextcite/context-cite).
import os
from enum import Enum
from dataclasses import dataclass
from typing import Dict, List, Any, Optional
import gradio as gr
import numpy as np
import spaces
import nltk
import base64
import traceback
from src.utils import split_into_sentences as split_into_sentences_utils
# --- AttnTrace imports (from app_full.py) ---
from src.models import create_model
from src.attribution import AttnTraceAttribution
from src.prompts import wrap_prompt
from gradio_highlightedtextbox import HighlightedTextbox
from examples import run_example_1, run_example_2, run_example_3, run_example_4, run_example_5, run_example_6
from functools import partial


from nltk.tokenize import sent_tokenize


# Load original app constants
APP_TITLE = '<div class="app-title"><span class="brand">AttnTrace: </span><span class="subtitle">Attention-based Context Traceback for Long-Context LLMs</span></div>'
APP_DESCRIPTION = """AttnTrace traces a model's generated statements back to specific parts of the context using attention-based traceback. Try it out with Meta-Llama-3.1-8B-Instruct here! See the [[paper](https://arxiv.org/abs/2508.03793)] and [[code](https://github.com/Wang-Yanting/AttnTrace)] for more!
Maintained by the AttnTrace team."""
# NEW_TEXT = """Long-context large language models (LLMs), such as Gemini-2.5-Pro and Claude-Sonnet-4, are increasingly used to empower advanced AI systems, including retrieval-augmented generation (RAG) pipelines and autonomous agents. In these systems, an LLM receives an instruction along with a context—often consisting of texts retrieved from a knowledge database or memory—and generates a response that is contextually grounded by following the instruction. Recent studies have designed solutions to trace back to a subset of texts in the context that contributes most to the response generated by the LLM. These solutions have numerous real-world applications, including performing post-attack forensic analysis and improving the interpretability and trustworthiness of LLM outputs. While significant efforts have been made, state-of-the-art solutions such as TracLLM often lead to a high computation cost, e.g., it takes TracLLM hundreds of seconds to perform traceback for a single response-context pair. In this work, we propose {\name}, a new context traceback method based on the attention weights produced by an LLM for a prompt. To effectively utilize attention weights, we introduce two techniques designed to enhance the effectiveness of {\name}, and we provide theoretical insights for our design choice. %Moreover, we perform both theoretical analysis and empirical evaluation to demonstrate their effectiveness. 
# We also perform a systematic evaluation for {\name}. The results demonstrate that {\name} is more accurate and efficient than existing state-of-the-art context traceback methods. We also show {\name} can improve state-of-the-art methods in detecting prompt injection under long contexts through the attribution-before-detection paradigm. As a real-world application, we demonstrate that {\name} can effectively pinpoint injected instructions in a paper designed to manipulate LLM-generated reviews.
# The code and data will be open-sourced. """
# EDIT_TEXT = "Feel free to edit!"
GENERATE_CONTEXT_TOO_LONG_TEXT = (
    '<em style="color: red;">Context is too long for the current model.</em>'
)
ATTRIBUTE_CONTEXT_TOO_LONG_TEXT = '<em style="color: red;">Context is too long for the current traceback method.</em>'
CONTEXT_LINES = 20
CONTEXT_MAX_LINES = 40
SELECTION_DEFAULT_TEXT = "Click on a sentence in the response to traceback!"
SELECTION_DEFAULT_VALUE = [(SELECTION_DEFAULT_TEXT, None)]
SOURCES_INFO = 'These are the texts that contribute most to the response.'
# SOURCES_IN_CONTEXT_INFO = (
#     "This shows the important sentences highlighted within their surrounding context from the text above. Colors indicate ranking: Red (1st), Orange (2nd), Golden (3rd), Yellow (4th-5th), Light (6th+)."
# )

MODEL_PATHS = [
    "meta-llama/Meta-Llama-3.1-8B-Instruct",
]
MAX_TOKENS = {
    "meta-llama/Meta-Llama-3.1-8B-Instruct": 131072,
}
DEFAULT_MODEL_PATH = MODEL_PATHS[0]
EXPLANATION_LEVELS = ["sentence", "paragraph", "text segment"]
DEFAULT_EXPLANATION_LEVEL = "sentence"

class WorkflowState(Enum):
    WAITING_TO_GENERATE = 0
    WAITING_TO_SELECT = 1
    READY_TO_ATTRIBUTE = 2

@dataclass
class State:
    workflow_state: WorkflowState
    context: str
    query: str
    response: str
    start_index: int
    end_index: int
    scores: np.ndarray
    answer: str
    highlighted_context: str
    full_response: str
    explained_response_part: str
    last_query_used: str = ""

# --- Dynamic Model and Attribution Management ---
current_llm = None
current_attr = None
current_model_path = None
current_explanation_level = None
current_api_key = None
current_top_k = 3  # Add top-k tracking
current_B = 30  # Add B parameter tracking
current_q = 0.4  # Add q parameter tracking

def update_configuration(explanation_level, top_k, B, q):
    """Update the global configuration and reinitialize attribution if needed"""
    global current_explanation_level, current_top_k, current_B, current_q, current_attr, current_llm
    
    # Convert parameters to appropriate types
    top_k = int(top_k)
    B = int(B)
    q = float(q)
    
    # Check if configuration has changed
    config_changed = (current_explanation_level != explanation_level or 
                     current_top_k != top_k or
                     current_B != B or
                     current_q != q)
    
    if config_changed:
        print(f"🔄 Updating configuration: explanation_level={explanation_level}, top_k={top_k}, B={B}, q={q}")
        current_explanation_level = explanation_level
        current_top_k = top_k
        current_B = B
        current_q = q
        
        # Reset both model and attribution to force complete reinitialization
        current_llm = None
        current_attr = None
        
        # Reinitialize with new configuration
        try:
            llm, attr, error_msg = initialize_model_and_attr()
            if llm is not None and attr is not None:
                return gr.update(value=f"✅ Configuration updated: {explanation_level} level, top-{top_k}, B={B}, q={q}")
            else:
                return gr.update(value=f"❌ Error reinitializing: {error_msg}")
        except Exception as e:
            return gr.update(value=f"❌ Error updating configuration: {str(e)}")
    else:
        return gr.update(value="ℹ️ Configuration unchanged")

def initialize_model_and_attr():
    """Initialize model and attribution with default configuration"""
    global current_llm, current_attr, current_model_path, current_explanation_level, current_api_key, current_top_k, current_B, current_q
    
    try:
        # Check if we need to reinitialize the model
        need_model_update = (current_llm is None or 
                           current_model_path != DEFAULT_MODEL_PATH or 
                           current_api_key != os.getenv("HF_TOKEN"))
        
        # Check if we need to update attribution
        need_attr_update = (current_attr is None or 
                          current_explanation_level != (current_explanation_level or DEFAULT_EXPLANATION_LEVEL) or 
                          need_model_update)
        
        if need_model_update:
            print(f"Initializing model: {DEFAULT_MODEL_PATH}")
            effective_api_key = os.getenv("HF_TOKEN")
            current_llm = create_model(model_path=DEFAULT_MODEL_PATH, api_key=effective_api_key, device="cuda")
            current_model_path = DEFAULT_MODEL_PATH
            current_api_key = effective_api_key
            
        if need_attr_update:
            # Use current configuration or defaults
            explanation_level = current_explanation_level or DEFAULT_EXPLANATION_LEVEL
            top_k = current_top_k or 3
            B = current_B or 30
            q = current_q or 0.4
            if "segment" in explanation_level:
                explanation_level = "segment"
            print(f"Initializing context traceback with explanation level: {explanation_level}, top_k: {top_k}, B: {B}, q: {q}")
            current_attr = AttnTraceAttribution(
                current_llm, 
                explanation_level= explanation_level, 
                K=top_k, 
                q=q, 
                B=B
            )
            current_explanation_level = explanation_level
            
        return current_llm, current_attr, None
        
    except Exception as e:
        error_msg = f"Error initializing model/traceback: {str(e)}"
        print(error_msg)
        traceback.print_exc()
        return None, None, error_msg

# Remove immediate initialization - let lazy initialization work
llm, attr, error_msg = initialize_model_and_attr()  # Commented out to avoid main-thread CUDA initialization

# Images replaced with CSS textures and gradients - no longer needed

def clear_state():
    return State(
        workflow_state=WorkflowState.WAITING_TO_GENERATE,
        context="",
        query="",
        response="",
        start_index=0,
        end_index=0,
        scores=np.array([]),
        answer="",
        highlighted_context="",
        full_response="",
        explained_response_part="",
        last_query_used=""
    )

def load_an_example(example_loader_func, state: State):
    context, query = example_loader_func()
    # Update both UI and state
    state.context = context
    state.query = query
    state.workflow_state = WorkflowState.WAITING_TO_GENERATE
    # Clear previous results
    state.response = ""
    state.answer = ""
    state.full_response = ""
    state.explained_response_part = ""
    print(f"Loaded example - Context: {len(context)} chars, Query: {query[:50]}...")
    return (
        context,  # basic_context_box
        query,    # basic_query_box
        state, 
        "",       # response_input_box - clear it
        gr.update(value=[("Click the 'Generate/Use Response' button above to see response text here for traceback analysis.", None)]),  # basic_response_box - keep visible
        gr.update(selected=0)  # basic_context_tabs - switch to first tab
    )


def get_max_tokens(model_path: str):
    return MAX_TOKENS.get(model_path, 2048)  # Default fallback


def get_scroll_js_code(elem_id):
    return f"""
    function scrollToElement() {{
        const element = document.getElementById("{elem_id}");
        element.scrollIntoView({{ behavior: "smooth", block: "nearest" }});
    }}
    """

def basic_update(context: str, query: str, state: State):
    state.context = context
    state.query = query
    state.workflow_state = WorkflowState.WAITING_TO_GENERATE
    return (
        gr.update(value=[("Click the 'Generate/Use Response' button above to see response text here for traceback analysis.", None)]),  # basic_response_box - keep visible
        gr.update(selected=0),  # basic_context_tabs - switch to first tab
        state,
    )


    


@spaces.GPU
def generate_model_response(state: State):
    # Validate inputs first with debug info
    print(f"Validation - Context length: {len(state.context) if state.context else 0}")
    print(f"Validation - Query: {state.query[:50] if state.query else 'empty'}...")
    
    if not state.context or not state.context.strip():
        print("❌ Validation failed: No context")
        return state, gr.update(value=[("❌ Please enter context before generating response! If you just changed configuration, try reloading an example.", None)], visible=True)
    
    if not state.query or not state.query.strip():
        print("❌ Validation failed: No query")
        return state, gr.update(value=[("❌ Please enter a query before generating response! If you just changed configuration, try reloading an example.", None)], visible=True)
    
    # Initialize model and attribution with current configuration
    print(f"🔧 Generating response with explanation_level: {current_explanation_level or DEFAULT_EXPLANATION_LEVEL}, top_k: {current_top_k or 3}")
    llm, attr, error_msg = initialize_model_and_attr()
    
    if llm is None or attr is None:
        error_text = error_msg if error_msg else "Model initialization failed!"
        return state, gr.update(value=[(f"❌ {error_text}", None)], visible=True)
    
    prompt = wrap_prompt(state.query, [state.context])
    print(f"Generated prompt for {DEFAULT_MODEL_PATH}: {prompt[:200]}...")  # Debug log
    
    # Check context length
    if len(prompt.split()) > get_max_tokens(DEFAULT_MODEL_PATH) - 512:
        return state, gr.update(value=[(GENERATE_CONTEXT_TOO_LONG_TEXT, None)], visible=True)
    
    answer = llm.query(prompt)
    print(f"Model response: {answer}")  # Debug log
    
    state.response = answer
    state.answer = answer
    state.full_response = answer
    state.workflow_state = WorkflowState.WAITING_TO_SELECT
    return state, gr.update(visible=False)

def split_into_sentences(text: str):
    def rule_based_split(text):
        sentences = []
        start = 0
        for i, char in enumerate(text):
            if char in ".?。":
                if i + 1 == len(text) or text[i + 1] == " ":
                    sentences.append(text[start:i + 1].strip())
                    start = i + 1
        if start < len(text):
            sentences.append(text[start:].strip())
        return sentences
        
    lines = text.splitlines()
    sentences = []
    for line in lines:
        #sentences.extend(sent_tokenize(line))
        sentences.extend(rule_based_split(line))
    separators = []
    cur_start = 0
    for sentence in sentences:
        cur_end = text.find(sentence, cur_start)
        separators.append(text[cur_start:cur_end])
        cur_start = cur_end + len(sentence)
    return sentences, separators


def basic_highlight_response(
    response: str, selected_index: int, num_sources: int = -1
):
    sentences, separators = split_into_sentences(response)
    ht = []
    if num_sources == -1:
        citations_text = "Traceback!"
    elif num_sources == 0:
        citations_text = "No important text!"
    else:
        citations_text = f"[{','.join(str(i) for i in range(1, num_sources + 1))}]"
    for i, (sentence, separator) in enumerate(zip(sentences, separators)):
        label = citations_text if i == selected_index else "Traceback"
        # Hack to ignore punctuation
        if len(sentence) >= 4:
            ht.append((separator + sentence, label))
        else:
            ht.append((separator + sentence, None))
    color_map = {"Click to cite!": "blue", citations_text: "yellow"}
    return gr.HighlightedText(value=ht, color_map=color_map)

def basic_highlight_response_with_visibility(
    response: str, selected_index: int, num_sources: int = -1, visible: bool = True
):
    """Version of basic_highlight_response that also sets visibility"""
    sentences, separators = split_into_sentences(response)
    ht = []
    if num_sources == -1:
        citations_text = "Traceback!"
    elif num_sources == 0:
        citations_text = "No important text!"
    else:
        citations_text = f"[{','.join(str(i) for i in range(1, num_sources + 1))}]"
    for i, (sentence, separator) in enumerate(zip(sentences, separators)):
        label = citations_text if i == selected_index else "Traceback"
        # Hack to ignore punctuation
        if len(sentence) >= 4:
            ht.append((separator + sentence, label))
        else:
            ht.append((separator + sentence, None))
    color_map = {"Click to cite!": "blue", citations_text: "yellow"}
    return gr.update(value=ht, color_map=color_map, visible=visible)



def basic_update_highlighted_response(evt: gr.SelectData, state: State):
    response_update = basic_highlight_response(state.response, evt.index)
    return response_update, state

def unified_response_handler(response_text: str, state: State):
    """Handle both LLM generation and manual input based on whether text is provided"""
    
    # Check if instruction has changed from what was used to generate current response
    instruction_changed = hasattr(state, 'last_query_used') and state.last_query_used != state.query
    
    # If response_text is empty, whitespace, or instruction changed, generate from LLM
    if not response_text or not response_text.strip() or instruction_changed:
        if instruction_changed:
            print("📝 Instruction changed, generating new response from LLM...")
        else:
            print("🤖 Generating response from LLM...")
        
        # Validate inputs first
        if not state.context or not state.context.strip():
            return (
                state, 
                response_text,  # Keep current text box content
                gr.update(visible=False),  # Keep response box hidden
                gr.update(value=[("❌ Please enter context before generating response!", None)], visible=True)
            )
        
        if not state.query or not state.query.strip():
            return (
                state, 
                response_text,  # Keep current text box content
                gr.update(visible=False),  # Keep response box hidden
                gr.update(value=[("❌ Please enter a query before generating response!", None)], visible=True)
            )
        
        # Initialize model and generate response
        llm, attr, error_msg = initialize_model_and_attr()
        
        if llm is None:
            error_text = error_msg if error_msg else "Model initialization failed!"
            return (
                state, 
                response_text,  # Keep current text box content
                gr.update(visible=False),  # Keep response box hidden
                gr.update(value=[(f"❌ {error_text}", None)], visible=True)
            )
        
        prompt = wrap_prompt(state.query, [state.context])
        
        # Check context length
        if len(prompt.split()) > get_max_tokens(DEFAULT_MODEL_PATH) - 512:
            return (
                state, 
                response_text,  # Keep current text box content
                gr.update(visible=False),  # Keep response box hidden
                gr.update(value=[(GENERATE_CONTEXT_TOO_LONG_TEXT, None)], visible=True)
            )
        
        # Generate response
        answer = llm.query(prompt)
        print(f"Generated response: {answer[:100]}...")
        
        # Update state and UI
        state.response = answer
        state.answer = answer
        state.full_response = answer
        state.last_query_used = state.query  # Track which query was used for this response
        state.workflow_state = WorkflowState.WAITING_TO_SELECT
        
        # Create highlighted response and show it
        response_update = basic_highlight_response_with_visibility(state.response, -1, visible=True)
        
        return (
            state,
            answer,  # Put generated response in text box
            response_update,  # Update clickable response content
            gr.update(visible=False)  # Hide error box
        )
    
    else:
        # Use provided text as manual response
        print("✏️ Using manual response...")
        manual_text = response_text.strip()
        
        # Update state with manual response
        state.response = manual_text
        state.answer = manual_text
        state.full_response = manual_text
        state.last_query_used = state.query  # Track current query for this response
        state.workflow_state = WorkflowState.WAITING_TO_SELECT
        
        # Create highlighted response for selection
        response_update = basic_highlight_response_with_visibility(state.response, -1, visible=True)
        
        return (
            state,
            manual_text,  # Keep text in text box
            response_update,  # Update clickable response content
            gr.update(visible=False)  # Hide error box
        )

def get_color_by_rank(rank, total_items):
    """Get color based purely on rank position for better visual distinction"""
    if total_items == 0:
        return "#F0F0F0", "rgba(240, 240, 240, 0.8)"
    
    # Pure ranking-based color assignment for clear visual hierarchy
    if rank == 1:  # Highest importance - Strong Red
        bg_color = "#FF4444"  # Bright red
        rgba_color = "rgba(255, 68, 68, 0.9)"
    elif rank == 2:  # Second highest - Orange  
        bg_color = "#FF8C42"  # Bright orange
        rgba_color = "rgba(255, 140, 66, 0.8)"
    elif rank == 3:  # Third highest - Golden Yellow
        bg_color = "#FFD93D"  # Golden yellow
        rgba_color = "rgba(255, 217, 61, 0.8)"
    elif rank <= 5:  # 4th-5th - Light Yellow
        bg_color = "#FFF280"  # Standard yellow
        rgba_color = "rgba(255, 242, 128, 0.7)"
    else:  # Lower importance - Very Light Yellow
        bg_color = "#FFF9C4"  # Very light yellow
        rgba_color = "rgba(255, 249, 196, 0.6)"
    
    return bg_color, rgba_color

@spaces.GPU
def basic_get_scores_and_sources_full_response(state: State):
    """Traceback the entire response instead of a selected segment"""
    
    
    # Use the entire response as the explained part
    state.explained_response_part = state.full_response

    # Attribution using default configuration
    llm, attr, error_msg = initialize_model_and_attr()
    
    if attr is None:
        error_text = error_msg if error_msg else "Traceback initialization failed!"
        return (
            gr.update(value=[("", None)], visible=False),
            gr.update(selected=0),
            gr.update(visible=False),
            gr.update(value=""),
            gr.update(value=[(f"❌ {error_text}", None)], visible=True),
            state,
        )
    try:
        # Validate attribution inputs
        if not state.context or not state.context.strip():
            return (
                gr.update(value=[("", None)], visible=False),
                gr.update(selected=0),
                gr.update(visible=False),
                gr.update(value=""),
                gr.update(value=[("❌ No context available for traceback!", None)], visible=True),
                state,
            )
            
        if not state.query or not state.query.strip():
            return (
                gr.update(value=[("", None)], visible=False),
                gr.update(selected=0),
                gr.update(visible=False),
                gr.update(value=""),
                gr.update(value=[("❌ No query available for traceback!", None)], visible=True),
                state,
            )
            
        if not state.full_response or not state.full_response.strip():
            return (
                gr.update(value=[("", None)], visible=False),
                gr.update(selected=0),
                gr.update(visible=False),
                gr.update(value=""),
                gr.update(value=[("❌ No response available for traceback!", None)], visible=True),
                state,
            )
        
        print(f"start full response traceback with explanation_level: {DEFAULT_EXPLANATION_LEVEL}")
        print(f"context length: {len(state.context)}, query: {state.query[:100]}...")
        print(f"full response: {state.full_response[:100]}...")
        print(f"tracing entire response (length: {len(state.full_response)} chars)")
        
        texts, important_ids, importance_scores, _, _ = attr.attribute(
            state.query, [state.context], state.full_response, state.full_response
        )
        print("end full response traceback")
        print(f"explanation_level: {DEFAULT_EXPLANATION_LEVEL}")
        print(f"texts count: {len(texts)} (how context was segmented)")
        if len(texts) > 0:
            print(f"sample text segments: {[text[:50] + '...' if len(text) > 50 else text for text in texts[:3]]}")
        print(f"important_ids: {important_ids}")
        print("importance_scores: ", importance_scores)
        
        if not importance_scores:
            return (
                gr.update(value=[("", None)], visible=False),
                gr.update(selected=0),
                gr.update(visible=False),
                gr.update(value=""),
                gr.update(value=[("❌ No traceback scores generated for full response!", None)], visible=True),
                state,
            )
            
        state.scores = np.array(importance_scores)
        
        # Highlighted sources with ranking-based colors
        highlighted_text = []
        sorted_indices = np.argsort(state.scores)[::-1]
        total_sources = len(important_ids)
        
        for rank, i in enumerate(sorted_indices):
            source_text = texts[important_ids[i]]
            _ = get_color_by_rank(rank + 1, total_sources)
            
            highlighted_text.append(
                (
                    source_text,
                    f"rank_{rank+1}",
                )
            )
        
        # In-context highlights with ranking-based colors - show ALL text
        in_context_highlighted_text = []
        ranks = {important_ids[i]: rank for rank, i in enumerate(sorted_indices)}
        
        for i in range(len(texts)):
            source_text = texts[i]
            
            # Skip or don't highlight segments that are only newlines or whitespace
            if source_text.strip() == "":
                # For whitespace-only segments, add them without highlighting
                in_context_highlighted_text.append((source_text, None))
            elif i in important_ids:
                # Only highlight if the segment has actual content (not just newlines)
                if source_text.strip():  # Has non-whitespace content
                    rank = ranks[i] + 1
                    
                    # Split the segment to separate leading/trailing newlines from content
                    # This prevents newlines from being highlighted
                    leading_whitespace = ""
                    trailing_whitespace = ""
                    content = source_text
                    
                    # Extract leading newlines/whitespace
                    while content and content[0] in ['\n', '\r', '\t', ' ']:
                        leading_whitespace += content[0]
                        content = content[1:]
                    
                    # Extract trailing newlines/whitespace
                    while content and content[-1] in ['\n', '\r', '\t', ' ']:
                        trailing_whitespace = content[-1] + trailing_whitespace
                        content = content[:-1]
                    
                    # Add the parts separately: whitespace unhighlighted, content highlighted
                    if leading_whitespace:
                        in_context_highlighted_text.append((leading_whitespace, None))
                    if content:
                        in_context_highlighted_text.append((content, f"rank_{rank}"))
                    if trailing_whitespace:
                        in_context_highlighted_text.append((trailing_whitespace, None))
                else:
                    # Even if marked as important, don't highlight whitespace-only segments
                    in_context_highlighted_text.append((source_text, None))
            else:
                # Add unhighlighted text for non-important segments
                in_context_highlighted_text.append((source_text, None))
        
        # Enhanced color map with ranking-based colors
        color_map = {}
        for rank in range(len(important_ids)):
            _, rgba_color = get_color_by_rank(rank + 1, total_sources)
            color_map[f"rank_{rank+1}"] = rgba_color
        dummy_update = gr.update(
            value=f"AttnTrace_{state.response}_{state.start_index}_{state.end_index}"
        )
        attribute_error_update = gr.update(visible=False)
        
        # Combine sources and highlighted context into a single display
        # Sources at the top
        combined_display = []
        
        # Add sources header (no highlighting for UI elements)
        combined_display.append(("═══ FULL RESPONSE TRACEBACK RESULTS ═══\n", None))
        combined_display.append(("These are the text segments that contribute most to the entire response:\n\n", None))
        
        # Add sources using available data
        for rank, i in enumerate(sorted_indices):
            if i < len(important_ids):
                source_text = texts[important_ids[i]]
                
                # Strip leading/trailing whitespace from source text to avoid highlighting newlines
                clean_source_text = source_text.strip()
                
                if clean_source_text:  # Only add if there's actual content
                    # Add the source text with highlighting, then add spacing without highlighting
                    combined_display.append((clean_source_text, f"rank_{rank+1}"))
                    combined_display.append(("\n\n", None))
        
        # Add separator (no highlighting for UI elements)
        combined_display.append(("\n" + "═"*50 + "\n", None))
        combined_display.append(("FULL CONTEXT WITH HIGHLIGHTS\n", None))
        combined_display.append(("Scroll down to see the complete context with important segments highlighted:\n\n", None))
        
        # Add highlighted context using in_context_highlighted_text
        combined_display.extend(in_context_highlighted_text)
        
        # Use only the ranking colors (no highlighting for UI elements)
        enhanced_color_map = color_map.copy()
        
        combined_sources_update = HighlightedTextbox(
            value=combined_display, color_map=enhanced_color_map, visible=True
        )
        
        # Switch to the highlighted context tab and show results
        basic_context_tabs_update = gr.update(selected=1)
        basic_sources_in_context_tab_update = gr.update(visible=True)
        
        return (
            combined_sources_update,
            basic_context_tabs_update,
            basic_sources_in_context_tab_update,
            dummy_update,
            attribute_error_update,
            state,
        )
    except Exception as e:
        traceback.print_exc()
        return (
            gr.update(value=[("", None)], visible=False),
            gr.update(selected=0),
            gr.update(visible=False),
            gr.update(value=""),
            gr.update(value=[(f"❌ Error: {str(e)}", None)], visible=True),
            state,
        )

def basic_get_scores_and_sources(
    evt: gr.SelectData,
    highlighted_response: List[Dict[str, str]],
    state: State,
):
    
    # Get the selected sentence
    print("highlighted_response: ", highlighted_response[evt.index])
    selected_text = highlighted_response[evt.index]['token']
    state.explained_response_part = selected_text

    # Attribution using default configuration
    llm, attr, error_msg = initialize_model_and_attr()
    
    if attr is None:
        error_text = error_msg if error_msg else "Traceback initialization failed!"
        return (
            gr.update(value=[("", None)], visible=False),
            gr.update(selected=0),
            gr.update(visible=False),
            gr.update(value=""),
            gr.update(value=[(f"❌ {error_text}", None)], visible=True),
            state,
        )
    try:
        # Validate attribution inputs
        if not state.context or not state.context.strip():
            return (
                gr.update(value=[("", None)], visible=False),
                gr.update(selected=0),
                gr.update(visible=False),
                gr.update(value=""),
                gr.update(value=[("❌ No context available for traceback!", None)], visible=True),
                state,
            )
            
        if not state.query or not state.query.strip():
            return (
                gr.update(value=[("", None)], visible=False),
                gr.update(selected=0),
                gr.update(visible=False),
                gr.update(value=""),
                gr.update(value=[("❌ No query available for traceback!", None)], visible=True),
                state,
            )
            
        if not state.full_response or not state.full_response.strip():
            return (
                gr.update(value=[("", None)], visible=False),
                gr.update(selected=0),
                gr.update(visible=False),
                gr.update(value=""),
                gr.update(value=[("❌ No response available for traceback!", None)], visible=True),
                state,
            )
        
        print(f"start traceback with explanation_level: {DEFAULT_EXPLANATION_LEVEL}")
        print(f"context length: {len(state.context)}, query: {state.query[:100]}...")
        print(f"response: {state.full_response[:100]}...")
        print(f"selected part: {state.explained_response_part[:100]}...")
        
        texts, important_ids, importance_scores, _, _ = attr.attribute(
            state.query, [state.context], state.full_response, state.explained_response_part
        )
        print("end traceback")
        print(f"explanation_level: {DEFAULT_EXPLANATION_LEVEL}")
        print(f"texts count: {len(texts)} (how context was segmented)")
        if len(texts) > 0:
            print(f"sample text segments: {[text[:50] + '...' if len(text) > 50 else text for text in texts[:3]]}")
        print(f"important_ids: {important_ids}")
        print("importance_scores: ", importance_scores)
        
        if not importance_scores:
            return (
                gr.update(value=[("", None)], visible=False),
                gr.update(selected=0),
                gr.update(visible=False),
                gr.update(value=""),
                gr.update(value=[("❌ No traceback scores generated! Try a different text segment.", None)], visible=True),
                state,
            )
            
        state.scores = np.array(importance_scores)
        
        # Highlighted sources with ranking-based colors
        highlighted_text = []
        sorted_indices = np.argsort(state.scores)[::-1]
        total_sources = len(important_ids)
        
        for rank, i in enumerate(sorted_indices):
            source_text = texts[important_ids[i]]
            _ = get_color_by_rank(rank + 1, total_sources)
            
            highlighted_text.append(
                (
                    source_text,
                    f"rank_{rank+1}",
                )
            )
        
        # In-context highlights with ranking-based colors - show ALL text
        in_context_highlighted_text = []
        ranks = {important_ids[i]: rank for rank, i in enumerate(sorted_indices)}
        
        for i in range(len(texts)):
            source_text = texts[i]
            
            # Skip or don't highlight segments that are only newlines or whitespace
            if source_text.strip() == "":
                # For whitespace-only segments, add them without highlighting
                in_context_highlighted_text.append((source_text, None))
            elif i in important_ids:
                # Only highlight if the segment has actual content (not just newlines)
                if source_text.strip():  # Has non-whitespace content
                    rank = ranks[i] + 1
                    
                    # Split the segment to separate leading/trailing newlines from content
                    # This prevents newlines from being highlighted
                    leading_whitespace = ""
                    trailing_whitespace = ""
                    content = source_text
                    
                    # Extract leading newlines/whitespace
                    while content and content[0] in ['\n', '\r', '\t', ' ']:
                        leading_whitespace += content[0]
                        content = content[1:]
                    
                    # Extract trailing newlines/whitespace
                    while content and content[-1] in ['\n', '\r', '\t', ' ']:
                        trailing_whitespace = content[-1] + trailing_whitespace
                        content = content[:-1]
                    
                    # Add the parts separately: whitespace unhighlighted, content highlighted
                    if leading_whitespace:
                        in_context_highlighted_text.append((leading_whitespace, None))
                    if content:
                        in_context_highlighted_text.append((content, f"rank_{rank}"))
                    if trailing_whitespace:
                        in_context_highlighted_text.append((trailing_whitespace, None))
                else:
                    # Even if marked as important, don't highlight whitespace-only segments
                    in_context_highlighted_text.append((source_text, None))
            else:
                # Add unhighlighted text for non-important segments
                in_context_highlighted_text.append((source_text, None))
        
        # Enhanced color map with ranking-based colors
        color_map = {}
        for rank in range(len(important_ids)):
            _, rgba_color = get_color_by_rank(rank + 1, total_sources)
            color_map[f"rank_{rank+1}"] = rgba_color
        dummy_update = gr.update(
            value=f"AttnTrace_{state.response}_{state.start_index}_{state.end_index}"
        )
        attribute_error_update = gr.update(visible=False)
        
        # Combine sources and highlighted context into a single display
        # Sources at the top
        combined_display = []
        
        # Add sources header (no highlighting for UI elements)
        combined_display.append(("═══ TRACEBACK RESULTS ═══\n", None))
        combined_display.append(("These are the text segments that contribute most to the response:\n\n", None))
        
        # Add sources using available data
        for rank, i in enumerate(sorted_indices):
            if i < len(important_ids):
                source_text = texts[important_ids[i]]
                
                # Strip leading/trailing whitespace from source text to avoid highlighting newlines
                clean_source_text = source_text.strip()
                
                if clean_source_text:  # Only add if there's actual content
                    # Add the source text with highlighting, then add spacing without highlighting
                    combined_display.append((clean_source_text, f"rank_{rank+1}"))
                    combined_display.append(("\n\n", None))
        
        # Add separator (no highlighting for UI elements)
        combined_display.append(("\n" + "═"*50 + "\n", None))
        combined_display.append(("FULL CONTEXT WITH HIGHLIGHTS\n", None))
        combined_display.append(("Scroll down to see the complete context with important segments highlighted:\n\n", None))
        
        # Add highlighted context using in_context_highlighted_text
        combined_display.extend(in_context_highlighted_text)
        
        # Use only the ranking colors (no highlighting for UI elements)
        enhanced_color_map = color_map.copy()
        
        combined_sources_update = HighlightedTextbox(
            value=combined_display, color_map=enhanced_color_map, visible=True
        )
        
        # Switch to the highlighted context tab and show results
        basic_context_tabs_update = gr.update(selected=1)
        basic_sources_in_context_tab_update = gr.update(visible=True)
        
        return (
            combined_sources_update,
            basic_context_tabs_update,
            basic_sources_in_context_tab_update,
            dummy_update,
            attribute_error_update,
            state,
        )
    except Exception as e:
        traceback.print_exc()
        return (
            gr.update(value=[("", None)], visible=False),
            gr.update(selected=0),
            gr.update(visible=False),
            gr.update(value=""),
            gr.update(value=[(f"❌ Error: {str(e)}", None)], visible=True),
            state,
        )

def load_custom_css():
    """Load CSS from external file"""
    try:
        with open("assets/app_styles.css", "r") as f:
            css_content = f.read()
        return css_content
    except FileNotFoundError:
        print("Warning: CSS file not found, using minimal CSS")
        return ""
    except Exception as e:
        print(f"Error loading CSS: {e}")
        return ""

# Load CSS from external file
custom_css = load_custom_css()
theme = gr.themes.Citrus(
    text_size="lg",
    spacing_size="md",
)
with gr.Blocks(theme=theme, css=custom_css) as demo:
    gr.Markdown(f"# {APP_TITLE}")
    gr.Markdown(APP_DESCRIPTION, elem_classes="app-description")
    # gr.Markdown(NEW_TEXT, elem_classes="app-description-2")

    gr.Markdown("""
    <div style="font-size: 18px;">          
    AttnTrace is an efficient context traceback method for long contexts (e.g., full papers). It is over 15× faster than the state-of-the-art context traceback method TracLLM. Compared to previous attention-based approaches, AttnTrace is more accurate, reliable, and memory-efficient.
    """, elem_classes="feature-highlights")
    # Feature highlights
    gr.Markdown("""
    <div style="font-size: 18px;">    
    AttnTrace can be used in many real-world applications, such as tracing back to: 
    
    - 📄 prompt injection instructions that manipulate LLM-generated paper reviews.
    - 💻 malicious comment & code hiding in the codebase that misleads the AI coding assistant.
    - 🤖 malicious instructions that mislead the action of the LLM agent.
    - 🖋 source texts in the context from an AI summary.
    - 🔍 evidence that supports the LLM-generated answer for a question.
    - ❌ misinformation (corrupted knowledge) that manipulates LLM output for a question.
    - And a lot more...
    
    </div>
    """, elem_classes="feature-highlights")
    
    # Example buttons with topic-relevant images - moved here for better positioning
    gr.Markdown("### 🚀 Try These Examples!", elem_classes="example-title")
    with gr.Row(elem_classes=["example-button-container"]):
        with gr.Column(scale=1):
            example_1_btn = gr.Button(
                "📄 Prompt Injection Attacks in AI Paper Review",
                elem_classes=["example-button", "example-paper"],
                elem_id="example_1_button",
                scale=None,
                size="sm"
            )
        with gr.Column(scale=1):
            example_2_btn = gr.Button(
                "💻 Malicious Comments & Code in Codebase",
                elem_classes=["example-button", "example-movie"],
                elem_id="example_2_button"
            )
        with gr.Column(scale=1):
            example_3_btn = gr.Button(
                "🤖 Malicious Instructions Misleading the LLM Agent",
                elem_classes=["example-button", "example-code"],
                elem_id="example_3_button"
            )
    
    with gr.Row(elem_classes=["example-button-container"]):
        with gr.Column(scale=1):
            example_4_btn = gr.Button(
                "🖋 Source Texts for an AI Summary",
                elem_classes=["example-button", "example-paper-alt"],
                elem_id="example_4_button"
            )
        with gr.Column(scale=1):
            example_5_btn = gr.Button(
                "🔍 Evidence that Support Question Answering",
                elem_classes=["example-button", "example-movie-alt"],
                elem_id="example_5_button"
            )
        with gr.Column(scale=1):
            example_6_btn = gr.Button(
                "❌ Misinformation (Corrupted Knowledge) in Question Answering",
                elem_classes=["example-button", "example-code-alt"],
                elem_id="example_6_button"
            )

    state = gr.State(
        value=clear_state()
    )

    # Create tabs for Demo and Configuration
    with gr.Tabs() as main_tabs:
        # Demo Tab
        with gr.Tab("Demo", id="demo_tab"):
            gr.Markdown(
                "Enter your context and instruction below to try out AttnTrace! You can also click on the example buttons above to load pre-configured examples."
            )
            
            gr.Markdown(
                '**Color Legend for Context Traceback (by ranking):** <span style="background-color: #FF4444; color: black; padding: 2px 6px; border-radius: 4px; font-weight: 600;">Red</span> = 1st (most important) | <span style="background-color: #FF8C42; color: black; padding: 2px 6px; border-radius: 4px; font-weight: 600;">Orange</span> = 2nd | <span style="background-color: #FFD93D; color: black; padding: 2px 6px; border-radius: 4px; font-weight: 600;">Golden</span> = 3rd | <span style="background-color: #FFF280; color: black; padding: 2px 6px; border-radius: 4px; font-weight: 600;">Yellow</span> = 4th-5th | <span style="background-color: #FFF9C4; color: black; padding: 2px 6px; border-radius: 4px; font-weight: 600;">Light</span> = 6th+'
            )
            
            # Top section: Wide Context box with tabs
            with gr.Row():
                with gr.Column(scale=1):
                    with gr.Tabs() as basic_context_tabs:
                        with gr.TabItem("Context", id=0):
                            basic_context_box = gr.Textbox(
                                placeholder="Enter context...",
                                show_label=False,
                                value="",
                                lines=6,
                                max_lines=6,
                                elem_id="basic_context_box",
                                autoscroll=False,
                            )
                        with gr.TabItem("Context with highlighted traceback results", id=1, visible=True) as basic_sources_in_context_tab:
                            basic_sources_in_context_box = HighlightedTextbox(
                                value=[("Click on a sentence in the response below to see highlighted traceback results here.", None)],
                                show_legend_label=False,
                                show_label=False,
                                show_legend=False,
                                interactive=False,
                                elem_id="basic_sources_in_context_box",
                            )
            
            # Error messages
            basic_generate_error_box = HighlightedTextbox(
                show_legend_label=False,
                show_label=False,
                show_legend=False,
                visible=False,
                interactive=False,
                container=False,
            )

            # Bottom section: Left (instruction + button + response), Right (response selection)
            with gr.Row(equal_height=True):
                # Left: Instruction + Button + Response
                with gr.Column(scale=1):
                    basic_query_box = gr.Textbox(
                        label="Instruction",
                        placeholder="Enter an instruction...",
                        value="",
                        lines=3,
                        max_lines=3,
                    )
                    
                    unified_response_button = gr.Button(
                        "Generate/Use Response", 
                        variant="primary", 
                        size="lg"
                    )
                    
                    response_input_box = gr.Textbox(
                        label="Response (Editable)",
                        placeholder="Response will appear here after generation, or type your own response for traceback...",
                        lines=8,
                        max_lines=8,
                        info="Leave empty and click button to generate from LLM, or type your own response to use for traceback"
                    )
                
                # Right: Response for attribution selection
                with gr.Column(scale=1):
                    basic_response_box = gr.HighlightedText(
                        label="Click to select text for traceback!",
                        value=[("Click the 'Generate/Use Response' button on the left to see response text here for traceback analysis.", None)],
                        interactive=False,
                        combine_adjacent=False,
                        show_label=True,
                        show_legend=False,
                        elem_id="basic_response_box",
                        visible=True,
                    )
                    
                    # Button for full response traceback
                    full_response_traceback_button = gr.Button(
                        "🔍 Traceback Entire Response",
                        variant="secondary",
                        size="sm"
                    )
            
            # Hidden error box and dummy elements
            basic_attribute_error_box = HighlightedTextbox(
                show_legend_label=False,
                show_label=False,
                show_legend=False,
                visible=False,
                interactive=False,
                container=False,
            )
            dummy_basic_sources_box = gr.Textbox(
                visible=False, interactive=False, container=False
            )

        # Configuration Tab
        with gr.Tab("Config", id="config_tab"):
            gr.Markdown("## ⚙️ AttnTrace Configuration")
            gr.Markdown("Configure the traceback analysis parameters to customize how AttnTrace processes your context and generates results.")
            
            with gr.Row():
                with gr.Column(scale=1):
                    explanation_level_dropdown = gr.Dropdown(
                        choices=["sentence", "paragraph", "text segment"],
                        value="sentence",
                        label="Explanation Level",
                        info="How to segment the context for traceback analysis"
                    )
                with gr.Column(scale=1):
                    top_k_dropdown = gr.Dropdown(
                        choices=["3", "5", "10"],
                        value="3",
                        label="Top-N Value",
                        info="Number of most important text segments to highlight"
                    )
            
            with gr.Row():
                with gr.Column(scale=1):
                    B_slider = gr.Slider(
                        minimum=1,
                        maximum=100,
                        value=30,
                        step=5,
                        label="B Parameter",
                        info="Number of subsamples (higher = more accurate but slower)"
                    )
                with gr.Column(scale=1):
                    q_slider = gr.Slider(
                        minimum=0.1,
                        maximum=1.0,
                        value=0.4,
                        step=0.1,
                        label="ρ Parameter",
                        info="Sub-sampling ratio (0.1-1.0)"
                    )
            
            with gr.Row():
                with gr.Column(scale=1):
                    apply_config_button = gr.Button(
                        "Apply Configuration",
                        variant="primary",
                        size="lg"
                    )
                with gr.Column(scale=2):
                    config_status_text = gr.Textbox(
                        label="Configuration Status",
                        value="Ready to apply configuration",
                        interactive=False,
                        lines=2
                    )
            
            gr.Markdown("### 📋 Current Configuration")
            gr.Markdown("""
            - **Explanation Level**: Determines how the context is segmented for analysis
              - `sentence`: Analyze at sentence level (recommended for most cases)
              - `paragraph`: Analyze at paragraph level (good for longer documents)
              - `text segment`: Analyze at the level of 100-word text segments (ideal for non-standard document formats)
            
            - **Top-N Value**: Number of most important text segments to highlight in results
              - Higher values show more context but may be less focused
              - Lower values provide more focused results but may miss some context
            
            - **B Parameter**: Number of subsamples
              - Higher values (50-100): More thorough analysis but slower
              - Lower values (10-30): Faster analysis but may miss some important segments
              - Default: 30 (good balance of speed and accuracy)
            
            - **ρ Parameter**: Sub-sampling ratio (0.1-1.0)

            
            **Note**: Configuration changes will take effect immediately for new traceback operations.
            """)
            
            gr.Markdown("### 🔄 Model Information")
            gr.Markdown(f"""
            - **Current Model**: {DEFAULT_MODEL_PATH}
            - **Max Tokens**: {get_max_tokens(DEFAULT_MODEL_PATH):,}
            - **Device**: CUDA (GPU accelerated)
            """)

    # Only a single (AttnTrace) method and model in this simplified version

    def basic_clear_state():
        state = clear_state()
        return (
            "",  # basic_context_box
            "",  # basic_query_box
            "",  # response_input_box
            gr.update(value=[("Click the 'Generate/Use Response' button above to see response text here for traceback analysis.", None)]),  # basic_response_box - keep visible
            gr.update(selected=0),  # basic_context_tabs - switch to first tab
            state,
        )

    # Defining behavior of various interactions for the demo tab only
    def handle_demo_tab_selection(evt: gr.SelectData):
        """Handle tab selection - only clear state when switching to demo tab"""
        if evt.index == 0:  # Demo tab
            return basic_clear_state()
        else:  # Configuration tab - no state change needed
            return (
                gr.update(),  # basic_context_box
                gr.update(),  # basic_query_box
                gr.update(),  # response_input_box
                gr.update(),  # basic_response_box
                gr.update(),  # basic_context_tabs
                gr.update(),  # state
            )
    
    main_tabs.select(
        fn=handle_demo_tab_selection,
        inputs=[],
        outputs=[
            basic_context_box,
            basic_query_box,
            response_input_box,
            basic_response_box,
            basic_context_tabs,
            state,
        ],
    )
    for component in [basic_context_box, basic_query_box]:
        component.change(
            basic_update,
            [basic_context_box, basic_query_box, state],
            [
                basic_response_box,
                basic_context_tabs,
                state,
            ],
        )
    # Example button event handlers - now update both UI and state
    outputs_for_examples = [
        basic_context_box,
        basic_query_box,
        state,
        response_input_box,
        basic_response_box,
        basic_context_tabs,
    ]
    example_1_btn.click(
        fn=partial(load_an_example, run_example_1),
        inputs=[state],
        outputs=outputs_for_examples
    )
    example_2_btn.click(
        fn=partial(load_an_example, run_example_2),
        inputs=[state],
        outputs=outputs_for_examples
    )
    example_3_btn.click(
        fn=partial(load_an_example, run_example_3),
        inputs=[state],
        outputs=outputs_for_examples
    )
    example_4_btn.click(
        fn=partial(load_an_example, run_example_4),
        inputs=[state],
        outputs=outputs_for_examples
    )
    example_5_btn.click(
        fn=partial(load_an_example, run_example_5),
        inputs=[state],
        outputs=outputs_for_examples
    )
    example_6_btn.click(
        fn=partial(load_an_example, run_example_6),
        inputs=[state],
        outputs=outputs_for_examples
    )
    
    unified_response_button.click(
        fn=lambda: None,
        inputs=[],
        outputs=[],
        js=get_scroll_js_code("basic_response_box"),
    )
    basic_response_box.change(
        fn=lambda: None,
        inputs=[],
        outputs=[],
        js=get_scroll_js_code("basic_sources_in_context_box"),
    )
    # Add immediate tab switch on response selection
    def immediate_tab_switch():
        return (
            gr.update(value=[("🔄 Processing traceback... Please wait...", None)]),  # Show progress message
            gr.update(selected=1),  # Switch to annotation tab immediately
        )
    
    basic_response_box.select(
        fn=immediate_tab_switch,
        inputs=[],
        outputs=[basic_sources_in_context_box, basic_context_tabs],
        queue=False,  # Execute immediately without queue
    )
    
    basic_response_box.select(
        fn=basic_get_scores_and_sources,
        inputs=[basic_response_box, state],
        outputs=[
            basic_sources_in_context_box,
            basic_context_tabs,
            basic_sources_in_context_tab,
            dummy_basic_sources_box,
            basic_attribute_error_box,
            state,
        ],
        show_progress="full",
    )
    basic_response_box.select(
        fn=basic_update_highlighted_response,
        inputs=[state],
        outputs=[basic_response_box, state],
    )
    
    # Full response traceback button
    full_response_traceback_button.click(
        fn=immediate_tab_switch,
        inputs=[],
        outputs=[basic_sources_in_context_box, basic_context_tabs],
        queue=False,  # Execute immediately without queue
    )
    
    full_response_traceback_button.click(
        fn=basic_get_scores_and_sources_full_response,
        inputs=[state],
        outputs=[
            basic_sources_in_context_box,
            basic_context_tabs,
            basic_sources_in_context_tab,
            dummy_basic_sources_box,
            basic_attribute_error_box,
            state,
        ],
        show_progress="full",
    )
    
    dummy_basic_sources_box.change(
        fn=lambda: None,
        inputs=[],
        outputs=[],
        js=get_scroll_js_code("basic_sources_in_context_box"),
    )

    # Unified response handler
    unified_response_button.click(
        fn=unified_response_handler,
        inputs=[response_input_box, state],
        outputs=[state, response_input_box, basic_response_box, basic_generate_error_box]
    )
    
    # Configuration update handler
    apply_config_button.click(
        fn=update_configuration,
        inputs=[explanation_level_dropdown, top_k_dropdown, B_slider, q_slider],
        outputs=[config_status_text]
    )

    # gr.Markdown(
    #     "Please do not interact with elements while generation/attribution is in progress. This may cause errors. You can refresh the page if you run into issues because of this."
    # )

demo.launch(show_api=False, share=True)