app.py

import gradio as gr
import json
import matplotlib.pyplot as plt
import pandas as pd
import io
import base64
import math
import ast
import logging
import numpy as np
import plotly.graph_objects as go
import asyncio
import anyio

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

# Function to safely parse JSON or Python dictionary input
def parse_input(json_input):
    logger.debug("Attempting to parse input: %s", json_input)
    try:
        # Try to parse as JSON first
        data = json.loads(json_input)
        logger.debug("Successfully parsed as JSON")
        return data
    except json.JSONDecodeError as e:
        logger.error("JSON parsing failed: %s (Input: %s)", str(e), json_input[:100] + "..." if len(json_input) > 100 else json_input)
        raise ValueError(f"Malformed input: {str(e)}. Ensure property names are in double quotes (e.g., \"content\") and the format matches JSON (e.g., {{\"content\": [...]}}).")

# Function to ensure a value is a float, converting from string if necessary
def ensure_float(value):
    if value is None:
        logger.debug("Replacing None logprob with 0.0")
        return 0.0  # Default to 0.0 for None to ensure visualization
    if isinstance(value, str):
        try:
            return float(value)
        except ValueError:
            logger.error("Failed to convert string '%s' to float", value)
            return 0.0  # Default to 0.0 for invalid strings
    if isinstance(value, (int, float)):
        return float(value)
    return 0.0  # Default for any other type

# Function to get or generate a token value (default to "Unknown" if missing)
def get_token(entry):
    token = entry.get("token", "Unknown")
    if token == "Unknown":
        logger.warning("Missing 'token' key for entry: %s, using 'Unknown'", entry)
    return token

# Function to create an empty Plotly figure
def create_empty_figure(title):
    return go.Figure().update_layout(title=title, xaxis_title="", yaxis_title="", showlegend=False)

# Precompute the next chunk asynchronously
async def precompute_chunk(json_input, chunk_size, current_chunk):
    try:
        data = parse_input(json_input)
        content = data.get("content", []) if isinstance(data, dict) else data
        if not isinstance(content, list):
            raise ValueError("Content must be a list of entries")

        tokens = []
        logprobs = []
        top_alternatives = []
        for entry in content:
            if not isinstance(entry, dict):
                logger.warning("Skipping non-dictionary entry: %s", entry)
                continue
            logprob = ensure_float(entry.get("logprob", None))
            if logprob >= -100000:  # Include all entries with default 0.0
                tokens.append(get_token(entry))
                logprobs.append(logprob)
                top_probs = entry.get("top_logprobs", {})
                if top_probs is None:
                    logger.debug("top_logprobs is None for token: %s, using empty dict", get_token(entry))
                    top_probs = {}
                finite_top_probs = []
                for key, value in top_probs.items():
                    float_value = ensure_float(value)
                    if float_value is not None and math.isfinite(float_value):
                        finite_top_probs.append((key, float_value))
                sorted_probs = sorted(finite_top_probs, key=lambda x: x[1], reverse=True)
                top_alternatives.append(sorted_probs)

        if not tokens or not logprobs:
            return None, None, None

        next_chunk = current_chunk + 1
        start_idx = next_chunk * chunk_size
        end_idx = min((next_chunk + 1) * chunk_size, len(tokens))
        if start_idx >= len(tokens):
            return None, None, None

        paginated_tokens = tokens[start_idx:end_idx]
        paginated_logprobs = logprobs[start_idx:end_idx]
        paginated_alternatives = top_alternatives[start_idx:end_idx]

        return paginated_tokens, paginated_logprobs, paginated_alternatives
    except Exception as e:
        logger.error("Precomputation failed for chunk %d: %s", current_chunk + 1, str(e))
        return None, None, None

# Function to process and visualize a chunk of log probs with dynamic top_logprobs
def visualize_logprobs(json_input, chunk=0, chunk_size=100):
    try:
        # Parse the input (handles JSON only)
        data = parse_input(json_input)
        
        # Ensure data is a dictionary with 'content' key containing a list
        if isinstance(data, dict) and "content" in data:
            content = data["content"]
            if not isinstance(content, list):
                raise ValueError("Content must be a list of entries")
        elif isinstance(data, list):
            content = data  # Handle direct list input (though only JSON is expected)
        else:
            raise ValueError("Input must be a dictionary with 'content' key or a list of entries")

        # Extract tokens, log probs, and top alternatives, skipping non-finite values with fixed filter of -100000
        tokens = []
        logprobs = []
        top_alternatives = []  # List to store all top_logprobs (dynamic length)
        for entry in content:
            if not isinstance(entry, dict):
                logger.warning("Skipping non-dictionary entry: %s", entry)
                continue
            logprob = ensure_float(entry.get("logprob", None))
            if logprob >= -100000:  # Include all entries with default 0.0
                tokens.append(get_token(entry))
                logprobs.append(logprob)
                # Get top_logprobs, default to empty dict if None
                top_probs = entry.get("top_logprobs", {})
                if top_probs is None:
                    logger.debug("top_logprobs is None for token: %s, using empty dict", get_token(entry))
                    top_probs = {}  # Default to empty dict for None
                # Ensure all values in top_logprobs are floats and create a list of tuples
                finite_top_probs = []
                for key, value in top_probs.items():
                    float_value = ensure_float(value)
                    if float_value is not None and math.isfinite(float_value):
                        finite_top_probs.append((key, float_value))
                # Sort by log probability (descending) to get all alternatives
                sorted_probs = sorted(finite_top_probs, key=lambda x: x[1], reverse=True)
                top_alternatives.append(sorted_probs)  # Store all alternatives, dynamic length
            else:
                logger.debug("Skipping entry with logprob: %s (type: %s)", entry.get("logprob"), type(entry.get("logprob", None)))

        # Check if there's valid data after filtering
        if not logprobs or not tokens:
            return (create_empty_figure("Log Probabilities of Generated Tokens"), None, "No tokens to display.", create_empty_figure("Top Token Log Probabilities"), create_empty_figure("Significant Probability Drops"), 1, 0)

        # Paginate data for chunks of 100 tokens
        total_chunks = max(1, (len(logprobs) + chunk_size - 1) // chunk_size)
        start_idx = chunk * chunk_size
        end_idx = min((chunk + 1) * chunk_size, len(logprobs))
        paginated_tokens = tokens[start_idx:end_idx]
        paginated_logprobs = logprobs[start_idx:end_idx]
        paginated_alternatives = top_alternatives[start_idx:end_idx] if top_alternatives else []

        # 1. Main Log Probability Plot (Interactive Plotly)
        main_fig = go.Figure()
        main_fig.add_trace(go.Scatter(x=list(range(len(paginated_logprobs))), y=paginated_logprobs, mode='markers+lines', name='Log Prob', marker=dict(color='blue')))
        main_fig.update_layout(
            title="Log Probabilities of Generated Tokens (Chunk %d)" % (chunk + 1),
            xaxis_title="Token Position (within chunk)",
            yaxis_title="Log Probability",
            hovermode="closest",
            clickmode='event+select'
        )
        main_fig.update_traces(
            customdata=[f"Token: {tok}, Log Prob: {prob:.4f}, Position: {i+start_idx}" for i, (tok, prob) in enumerate(zip(paginated_tokens, paginated_logprobs))],
            hovertemplate='<b>%{customdata}</b><extra></extra>'
        )

        # 2. Probability Drop Analysis (Interactive Plotly)
        if len(paginated_logprobs) < 2:
            drops_fig = create_empty_figure("Significant Probability Drops (Chunk %d)" % (chunk + 1))
        else:
            drops = [paginated_logprobs[i+1] - paginated_logprobs[i] for i in range(len(paginated_logprobs)-1)]
            drops_fig = go.Figure()
            drops_fig.add_trace(go.Bar(x=list(range(len(drops))), y=drops, name='Drop', marker_color='red'))
            drops_fig.update_layout(
                title="Significant Probability Drops (Chunk %d)" % (chunk + 1),
                xaxis_title="Token Position (within chunk)",
                yaxis_title="Log Probability Drop",
                hovermode="closest",
                clickmode='event+select'
            )
            drops_fig.update_traces(
                customdata=[f"Drop: {drop:.4f}, From: {paginated_tokens[i]} to {paginated_tokens[i+1]}, Position: {i+start_idx}" for i, drop in enumerate(drops)],
                hovertemplate='<b>%{customdata}</b><extra></extra>'
            )

        # Create DataFrame for the table with dynamic top_logprobs
        table_data = []
        max_alternatives = max(len(alts) for alts in paginated_alternatives) if paginated_alternatives else 0
        for i, entry in enumerate(content[start_idx:end_idx]):
            if not isinstance(entry, dict):
                continue
            logprob = ensure_float(entry.get("logprob", None))
            if logprob >= -100000 and "top_logprobs" in entry:  # Include all entries with default 0.0
                token = get_token(entry)
                top_logprobs = entry.get("top_logprobs", {})
                if top_logprobs is None:
                    logger.debug("top_logprobs is None for token: %s, using empty dict", token)
                    top_logprobs = {}  # Default to empty dict for None
                # Ensure all values in top_logprobs are floats
                finite_top_probs = []
                for key, value in top_logprobs.items():
                    float_value = ensure_float(value)
                    if float_value is not None and math.isfinite(float_value):
                        finite_top_probs.append((key, float_value))
                # Sort by log probability (descending)
                sorted_probs = sorted(finite_top_probs, key=lambda x: x[1], reverse=True)
                row = [token, f"{logprob:.4f}"]
                for alt_token, alt_logprob in sorted_probs[:max_alternatives]:  # Use max number of alternatives
                    row.append(f"{alt_token}: {alt_logprob:.4f}")
                # Pad with empty strings if fewer alternatives than max
                while len(row) < 2 + max_alternatives:
                    row.append("")
                table_data.append(row)

        df = (
            pd.DataFrame(
                table_data,
                columns=["Token", "Log Prob"] + [f"Alt {i+1}" for i in range(max_alternatives)],
            )
            if table_data
            else None
        )

        # Generate colored text (for the current chunk)
        if paginated_logprobs:
            min_logprob = min(paginated_logprobs)
            max_logprob = max(paginated_logprobs)
            if max_logprob == min_logprob:
                normalized_probs = [0.5] * len(paginated_logprobs)
            else:
                normalized_probs = [
                    (lp - min_logprob) / (max_logprob - min_logprob) for lp in paginated_logprobs
                ]

            colored_text = ""
            for i, (token, norm_prob) in enumerate(zip(paginated_tokens, normalized_probs)):
                r = int(255 * (1 - norm_prob))  # Red for low confidence
                g = int(255 * norm_prob)        # Green for high confidence
                b = 0
                color = f"rgb({r}, {g}, {b})"
                colored_text += f'<span style="color: {color}; font-weight: bold;">{token}</span>'
                if i < len(paginated_tokens) - 1:
                    colored_text += " "
            colored_text_html = f"<p>{colored_text}</p>"
        else:
            colored_text_html = "No tokens to display in this chunk."

        # Top Token Log Probabilities (Interactive Plotly, dynamic length, for the current chunk)
        alt_viz_fig = create_empty_figure("Top Token Log Probabilities (Chunk %d)" % (chunk + 1)) if not paginated_logprobs or not paginated_alternatives else go.Figure()
        if paginated_logprobs and paginated_alternatives:
            for i, (token, probs) in enumerate(zip(paginated_tokens, paginated_alternatives)):
                for j, (alt_tok, prob) in enumerate(probs):
                    alt_viz_fig.add_trace(go.Bar(x=[f"{token} (Pos {i+start_idx})"], y=[prob], name=f"{alt_tok}", marker_color=['blue', 'green', 'red', 'purple', 'orange'][:len(probs)]))
            alt_viz_fig.update_layout(
                title="Top Token Log Probabilities (Chunk %d)" % (chunk + 1),
                xaxis_title="Token (Position)",
                yaxis_title="Log Probability",
                barmode='stack',
                hovermode="closest",
                clickmode='event+select'
            )
            alt_viz_fig.update_traces(
                customdata=[f"Token: {tok}, Alt: {alt}, Log Prob: {prob:.4f}, Position: {i+start_idx}" for i, (tok, alts) in enumerate(zip(paginated_tokens, paginated_alternatives)) for alt, prob in alts],
                hovertemplate='<b>%{customdata}</b><extra></extra>'
            )

        return (main_fig, df, colored_text_html, alt_viz_fig, drops_fig, total_chunks, chunk)

    except Exception as e:
        logger.error("Visualization failed: %s (Input: %s)", str(e), json_input[:100] + "..." if len(json_input) > 100 else json_input)
        return (create_empty_figure("Log Probabilities of Generated Tokens"), None, "No finite log probabilities to display.", create_empty_figure("Top Token Log Probabilities"), create_empty_figure("Significant Probability Drops"), 1, 0)

# Analysis functions for detecting correct vs. incorrect traces
def analyze_confidence_signature(logprobs, tokens):
    if not logprobs or not tokens:
        return "No data for confidence signature analysis.", None
    # Track moving average of top token probability
    top_probs = [lps[0][1] if lps else -float('inf') for lps in logprobs]  # Extract top probability, handle empty
    moving_avg = np.convolve(
        top_probs,
        np.ones(20) / 20,  # 20-token window
        mode='valid'
    )
    
    # Detect significant drops (potential error points)
    drops = np.where(np.diff(moving_avg) < -0.15)[0]
    if not drops.size:
        return "No significant confidence drops detected.", None
    drop_positions = [(i, tokens[i + 19] if i + 19 < len(tokens) else "End of trace") for i in drops]  # Adjust for convolution window
    return "Significant confidence drops detected at positions:", drop_positions

def detect_interpretation_pivots(logprobs, tokens):
    if not logprobs or not tokens:
        return "No data for interpretation pivot detection.", None
    pivots = []
    reconsideration_tokens = ["wait", "but", "actually", "however", "hmm"]
    
    for i, (token, lps) in enumerate(zip(tokens, logprobs)):
        # Check if reconsideration tokens have unusually high probability
        for rt in reconsideration_tokens:
            for t, p in lps:
                if t.lower() == rt and p > -2.5:  # High probability
                    # Look back to find what's being reconsidered
                    context = tokens[max(0, i-50):i]
                    pivots.append((i, rt, context))
    
    if not pivots:
        return "No interpretation pivots detected.", None
    return "Interpretation pivots detected:", pivots

def calculate_decision_entropy(logprobs):
    if not logprobs:
        return "No data for entropy spike detection.", None
    # Calculate entropy at each token position
    entropies = []
    for lps in logprobs:
        if not lps:
            entropies.append(0.0)
            continue
        # Calculate entropy: -sum(p * log(p)) for each probability
        probs = [math.exp(p) for _, p in lps]  # Convert log probs to probabilities
        if not probs or sum(probs) == 0:
            entropies.append(0.0)
            continue
        entropy = -sum(p * math.log(p) for p in probs if p > 0)
        entropies.append(entropy)
    
    # Detect significant entropy spikes
    baseline = np.percentile(entropies, 75) if entropies else 0.0
    spikes = [i for i, e in enumerate(entropies) if e > baseline * 1.5 if baseline > 0]
    
    if not spikes:
        return "No entropy spikes detected at decision points.", None
    return "Entropy spikes detected at positions:", spikes

def analyze_conclusion_competition(logprobs, tokens):
    if not logprobs or not tokens:
        return "No data for conclusion competition analysis.", None
    # Find tokens related to conclusion
    conclusion_indices = [i for i, t in enumerate(tokens) 
                        if any(marker in t.lower() for marker in 
                              ["therefore", "thus", "boxed", "answer"])]
    
    if not conclusion_indices:
        return "No conclusion markers found in trace.", None
    
    # Analyze probability gap between top and second choices near conclusion
    gaps = []
    conclusion_idx = conclusion_indices[-1]
    end_range = min(conclusion_idx + 50, len(logprobs))
    for idx in range(conclusion_idx, end_range):
        if idx < len(logprobs) and len(logprobs[idx]) >= 2:
            top_prob = logprobs[idx][0][1] if logprobs[idx] else -float('inf')
            second_prob = logprobs[idx][1][1] if len(logprobs[idx]) > 1 else -float('inf')
            gap = top_prob - second_prob if top_prob != -float('inf') and second_prob != -float('inf') else 0.0
            gaps.append(gap)
    
    if not gaps:
        return "No conclusion competition data available.", None
    mean_gap = np.mean(gaps)
    return f"Mean probability gap at conclusion: {mean_gap:.4f} (higher indicates more confident conclusion)", None

def analyze_verification_signals(logprobs, tokens):
    if not logprobs or not tokens:
        return "No data for verification signal analysis.", None
    verification_terms = ["verify", "check", "confirm", "ensure", "double"]
    verification_probs = []
    
    for lps in logprobs:
        # Look for verification terms in top-k tokens
        max_v_prob = -float('inf')
        for token, prob in lps:
            if any(v_term in token.lower() for v_term in verification_terms):
                max_v_prob = max(max_v_prob, prob)
        
        if max_v_prob > -float('inf'):
            verification_probs.append(max_v_prob)
    
    if not verification_probs:
        return "No verification signals detected.", None
    count, mean_prob = len(verification_probs), np.mean(verification_probs)
    return f"Verification signals found: {count} instances, mean probability: {mean_prob:.4f}", None

def detect_semantic_inversions(logprobs, tokens):
    if not logprobs or not tokens:
        return "No data for semantic inversion detection.", None
    inversion_pairs = [
        ("more", "less"), ("larger", "smaller"), 
        ("winning", "losing"), ("increase", "decrease"),
        ("greater", "lesser"), ("positive", "negative")
    ]
    
    inversions = []
    for i, (token, lps) in enumerate(zip(tokens, logprobs)):
        for pos, neg in inversion_pairs:
            if token.lower() == pos:
                # Check if negative term has high probability
                for t, p in lps:
                    if t.lower() == neg and p > -3.0:  # High competitor
                        inversions.append((i, pos, neg, p))
            elif token.lower() == neg:
                # Check if positive term has high probability
                for t, p in lps:
                    if t.lower() == pos and p > -3.0:  # High competitor
                        inversions.append((i, neg, pos, p))
    
    if not inversions:
        return "No semantic inversions detected.", None
    return "Semantic inversions detected:", inversions

# Function to perform full trace analysis
def analyze_full_trace(json_input):
    try:
        data = parse_input(json_input)
        content = data.get("content", []) if isinstance(data, dict) else data
        if not isinstance(content, list):
            raise ValueError("Content must be a list of entries")

        tokens = []
        logprobs = []
        for entry in content:
            if not isinstance(entry, dict):
                logger.warning("Skipping non-dictionary entry: %s", entry)
                continue
            logprob = ensure_float(entry.get("logprob", None))
            if logprob >= -100000:  # Include all entries with default 0.0
                tokens.append(get_token(entry))
                top_probs = entry.get("top_logprobs", {})
                if top_probs is None:
                    top_probs = {}
                finite_top_probs = []
                for key, value in top_probs.items():
                    float_value = ensure_float(value)
                    if float_value is not None and math.isfinite(float_value):
                        finite_top_probs.append((key, float_value))
                logprobs.append(finite_top_probs)

        if not logprobs or not tokens:
            return "No valid data for trace analysis.", None, None, None, None, None

        # Perform all analyses
        confidence_result, confidence_data = analyze_confidence_signature(logprobs, tokens)
        pivot_result, pivot_data = detect_interpretation_pivots(logprobs, tokens)
        entropy_result, entropy_data = calculate_decision_entropy(logprobs)
        conclusion_result, conclusion_data = analyze_conclusion_competition(logprobs, tokens)
        verification_result, verification_data = analyze_verification_signals(logprobs, tokens)
        inversion_result, inversion_data = detect_semantic_inversions(logprobs, tokens)

        # Format results for display
        analysis_html = f"""
        <h3>Trace Analysis Results</h3>
        <ul>
            <li><strong>Confidence Signature:</strong> {confidence_result}</li>
            {f"<ul><li>Positions: {', '.join(str(pos) for pos, tok in confidence_data)}</li></ul>" if confidence_data else ""}
            <li><strong>Interpretation Pivots:</strong> {pivot_result}</li>
            {f"<ul><li>Positions: {', '.join(str(pos) for pos, _, _ in pivot_data)}</li></ul>" if pivot_data else ""}
            <li><strong>Decision Entropy Spikes:</strong> {entropy_result}</li>
            {f"<ul><li>Positions: {', '.join(str(pos) for pos in entropy_data)}</li></ul>" if entropy_data else ""}
            <li><strong>Conclusion Competition:</strong> {conclusion_result}</li>
            <li><strong>Verification Signals:</strong> {verification_result}</li>
            <li><strong>Semantic Inversions:</strong> {inversion_result}</li>
            {f"<ul><li>Positions: {', '.join(str(pos) for pos, _, _, _ in inversion_data)}</li></ul>" if inversion_data else ""}
        </ul>
        """
        return analysis_html, None, None, None, None, None

# Gradio interface with two tabs: Trace Analysis and Visualization
with gr.Blocks(title="Log Probability Visualizer") as app:
    gr.Markdown("# Log Probability Visualizer")
    gr.Markdown(
        "Paste your JSON log prob data below to analyze reasoning traces and visualize tokens in chunks of 100. Fixed filter ≥ -100000, dynamic number of top_logprobs, handles missing or null fields. Next chunk is precomputed proactively."
    )

    with gr.Tabs():
        with gr.Tab("Trace Analysis"):
            with gr.Row():
                json_input_analysis = gr.Textbox(
                    label="JSON Input for Trace Analysis",
                    lines=10,
                    placeholder="Paste your JSON (e.g., {\"content\": [{\"bytes\": [44], \"logprob\": 0.0, \"token\": \",\", \"top_logprobs\": {\" so\": -13.8046875, \".\": -13.8046875, \"，\": -13.640625}}]}).",
                )
            with gr.Row():
                analysis_output = gr.HTML(label="Trace Analysis Results")

            btn_analyze = gr.Button("Analyze Trace")
            btn_analyze.click(
                fn=analyze_full_trace,
                inputs=[json_input_analysis],
                outputs=[analysis_output, gr.State(), gr.State(), gr.State(), gr.State(), gr.State()],
            )

        with gr.Tab("Visualization"):
            with gr.Row():
                json_input_viz = gr.Textbox(
                    label="JSON Input for Visualization",
                    lines=10,
                    placeholder="Paste your JSON (e.g., {\"content\": [{\"bytes\": [44], \"logprob\": 0.0, \"token\": \",\", \"top_logprobs\": {\" so\": -13.8046875, \".\": -13.8046875, \"，\": -13.640625}}]}).",
                )
                chunk = gr.Number(value=0, label="Current Chunk", precision=0, minimum=0)

            with gr.Row():
                plot_output = gr.Plot(label="Log Probability Plot (Click for Tokens)")
                drops_output = gr.Plot(label="Probability Drops (Click for Details)")

            with gr.Row():
                table_output = gr.Dataframe(label="Token Log Probabilities and Top Alternatives")
                alt_viz_output = gr.Plot(label="Top Token Log Probabilities (Click for Details)")

            with gr.Row():
                text_output = gr.HTML(label="Colored Text (Confidence Visualization)")

            with gr.Row():
                prev_btn = gr.Button("Previous Chunk")
                next_btn = gr.Button("Next Chunk")
                total_chunks_output = gr.Number(label="Total Chunks", interactive=False)

            # Precomputed next chunk state (hidden)
            precomputed_next = gr.State(value=None)

            btn_viz = gr.Button("Visualize")
            btn_viz.click(
                fn=visualize_logprobs,
                inputs=[json_input_viz, chunk],
                outputs=[plot_output, table_output, text_output, alt_viz_output, drops_output, total_chunks_output, chunk],
            )

            # Precompute next chunk proactively when on current chunk
            async def precompute_next_chunk(json_input, current_chunk, precomputed_next):
                if precomputed_next is not None:
                    return precomputed_next  # Use cached precomputed chunk if available
                next_tokens, next_logprobs, next_alternatives = await precompute_chunk(json_input, 100, current_chunk)
                if next_tokens is None or next_logprobs is None or next_alternatives is None:
                    return None
                return (next_tokens, next_logprobs, next_alternatives)

            # Update chunk on button clicks
            def update_chunk(json_input, current_chunk, action, precomputed_next=None):
                total_chunks = visualize_logprobs(json_input, 0)[5]  # Get total chunks
                if action == "prev" and current_chunk > 0:
                    current_chunk -= 1
                elif action == "next" and current_chunk < total_chunks - 1:
                    current_chunk += 1
                    # If precomputed next chunk exists, use it; otherwise, compute it
                    if precomputed_next:
                        next_tokens, next_logprobs, next_alternatives = precomputed_next
                        if next_tokens and next_logprobs and next_alternatives:
                            logger.debug("Using precomputed next chunk for chunk %d", current_chunk)
                            return visualize_logprobs(json_input, current_chunk)
                return visualize_logprobs(json_input, current_chunk)

            prev_btn.click(
                fn=update_chunk,
                inputs=[json_input_viz, chunk, gr.State(value="prev"), precomputed_next],
                outputs=[plot_output, table_output, text_output, alt_viz_output, drops_output, total_chunks_output, chunk],
            )

            next_btn.click(
                fn=update_chunk,
                inputs=[json_input_viz, chunk, gr.State(value="next"), precomputed_next],
                outputs=[plot_output, table_output, text_output, alt_viz_output, drops_output, total_chunks_output, chunk],
            )

            # Trigger precomputation when chunk changes (via button clicks or initial load)
            def trigger_precomputation(json_input, current_chunk):
                asyncio.create_task(precompute_next_chunk(json_input, current_chunk, None))
                return gr.update(value=current_chunk)

            # Use a dummy event to trigger precomputation on chunk change (simplified for Gradio)
            chunk.change(
                fn=trigger_precomputation,
                inputs=[json_input_viz, chunk],
                outputs=[chunk],
            )

app.launch()