src/app.py

"""Streamlit front‑end entry‑point."""
import yaml
import json
import streamlit as st
import logging
from dotenv import load_dotenv
from orchestrator.planner import Planner
from orchestrator.executor import Executor
from config.settings import settings
from config.config_manager import config_manager
import fitz  # PyMuPDF local import to avoid heavy load on startup
import pandas as pd
from datetime import datetime
from services.cost_tracker import CostTracker

# Create a custom stream handler to capture logs
class LogCaptureHandler(logging.StreamHandler):
    def __init__(self):
        super().__init__()
        self.logs = []
        
    def emit(self, record):
        try:
            msg = self.format(record)
            self.logs.append(msg)
        except Exception:
            self.handleError(record)
            
    def get_logs(self):
        return "\n".join(self.logs)
        
    def clear(self):
        self.logs = []

# Initialize session state for storing execution history
if 'execution_history' not in st.session_state:
    st.session_state.execution_history = []

# Initialize session state for field descriptions tables
if 'field_descriptions_table' not in st.session_state:
    st.session_state.field_descriptions_table = []

# Initialize session state for unique indices descriptions table
if 'unique_indices_descriptions_table' not in st.session_state:
    st.session_state.unique_indices_descriptions_table = []

# Initialize session state for fields string
if 'fields_str' not in st.session_state:
    st.session_state.fields_str = "Chain, Percentage, Seq Loc"

# Set up logging capture
log_capture = LogCaptureHandler()
log_capture.setFormatter(logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s'))

# Configure root logger
root_logger = logging.getLogger()
root_logger.setLevel(logging.INFO)
root_logger.addHandler(log_capture)

# Configure specific loggers
for logger_name in ['orchestrator', 'agents', 'services']:
    logger = logging.getLogger(logger_name)
    logger.setLevel(logging.INFO)
    logger.addHandler(log_capture)

load_dotenv()

st.set_page_config(page_title="PDF Field Extractor", layout="wide")

# Sidebar navigation
st.sidebar.title("Navigation")
page = st.sidebar.radio("Go to", ["Documentation", "Traces", "Execution"])

# Documentation Page
if page == "Documentation":
    st.title("Deep‑Research PDF Field Extractor")
    
    st.markdown("""
    ## Overview
    This system uses a multi-agent architecture to extract fields from PDFs with high accuracy and reliability.
    
    ### Core Components
    
    1. **Planner**
       - Generates execution plans using Azure OpenAI
       - Determines optimal extraction strategy
       - Manages task dependencies
    
    2. **Executor**
       - Executes the generated plan
       - Manages agent execution flow
       - Handles context and result management
    
    3. **Agents**
       - `TableAgent`: Extracts text and tables using Azure Document Intelligence
       - `FieldMapper`: Maps fields to values using extracted content
       - `ForEachField`: Controls field iteration flow
    
    ### Processing Pipeline
    
    1. **Document Processing**
       - Text and table extraction using Azure Document Intelligence
       - Layout and structure preservation
       - Support for complex document formats
    
    2. **Field Extraction**
       - Document type inference
       - User profile determination
       - Page-by-page scanning
       - Value extraction and validation
    
    3. **Context Building**
       - Document metadata
       - Field descriptions
       - User context
       - Execution history
    
    ### Key Features
    
    #### Smart Field Extraction
    - Two-step extraction strategy:
      1. Page-by-page scanning for precise extraction
      2. Semantic search fallback if no value found
    - Basic context awareness for improved extraction
    - Support for tabular data extraction
    
    #### Document Intelligence
    - Azure Document Intelligence integration
    - Layout and structure preservation
    - Table extraction and formatting
    - Complex document handling
    
    #### Execution Monitoring
    - Detailed execution traces
    - Success/failure status
    - Comprehensive logging
    - Result storage and retrieval
    
    ### Technical Requirements
    
    - Azure OpenAI API key
    - Azure Document Intelligence endpoint
    - Python 3.9 or higher
    - Required Python packages (see requirements.txt)
    
    ### Getting Started
    
    1. **Upload Your PDF**
       - Click the "Upload PDF" button
       - Select your PDF file
    
    2. **Specify Fields**
       - Enter comma-separated field names
       - Example: `Date, Name, Value, Location`
    
    3. **Optional: Add Field Descriptions**
       - Provide YAML-formatted field descriptions
       - Helps improve extraction accuracy
    
    4. **Run Extraction**
       - Click "Run extraction"
       - Monitor progress in execution trace
       - View results in table format
    
    5. **Download Results**
       - Export as CSV
       - View detailed execution logs
    
    ### Support
    
    For detailed technical documentation, please refer to:
    - [Architecture Overview](ARCHITECTURE.md)
    - [Developer Documentation](DEVELOPER.md)
    """)

# Traces Page
elif page == "Traces":
    st.title("Execution Traces")
    
    if not st.session_state.execution_history:
        st.info("No execution traces available yet. Run an extraction to see traces here.")
    else:
        # Create a DataFrame from the execution history
        history_data = []
        for record in st.session_state.execution_history:
            history_data.append({
                "filename": record["filename"],
                "datetime": record["datetime"],
                "fields": ", ".join(record.get("fields", [])),
                "logs": record.get("logs", []),
                "results": record.get("results", None)
            })
        
        history_df = pd.DataFrame(history_data)
        
        # Display column headers
        col1, col2, col3, col4, col5 = st.columns([2, 2, 3, 1, 1])
        with col1:
            st.markdown("**Filename**")
        with col2:
            st.markdown("**Timestamp**")
        with col3:
            st.markdown("**Fields**")
        with col4:
            st.markdown("**Logs**")
        with col5:
            st.markdown("**Results**")
        
        st.markdown("---")  # Add a separator line
        
        # Display the table with download buttons
        for idx, row in history_df.iterrows():
            col1, col2, col3, col4, col5 = st.columns([2, 2, 3, 1, 1])
            with col1:
                st.write(row["filename"])
            with col2:
                st.write(row["datetime"])
            with col3:
                st.write(row["fields"])
            with col4:
                if row["logs"]:  # Check if we have any logs
                    st.download_button(
                        "Download Logs",
                        row["logs"],  # Use the stored logs
                        file_name=f"logs_{row['filename']}_{row['datetime']}.txt",
                        key=f"logs_dl_{idx}"
                    )
                else:
                    st.write("No Logs")
            with col5:
                if row["results"] is not None:
                    results_df = pd.DataFrame(row["results"])
                    st.download_button(
                        "Download Results",
                        results_df.to_csv(index=False),
                        file_name=f"results_{row['filename']}_{row['datetime']}.csv",
                        key=f"results_dl_{idx}"
                    )
                else:
                    st.write("No Results")
            st.markdown("---")  # Add a separator line between rows

# Execution Page
else:  # page == "Execution"
    st.title("Deep‑Research PDF Field Extractor (POC)")

    def flatten_json_response(json_data, fields):
        """Flatten the nested JSON response into a tabular structure with dynamic columns."""
        logger = logging.getLogger(__name__)
        logger.info("Starting flatten_json_response")
        logger.info(f"Input fields: {fields}")
        
        # Handle the case where the response is a string
        if isinstance(json_data, str):
            logger.info("Input is a string, attempting to parse as JSON")
            try:
                json_data = json.loads(json_data)
                logger.info("Successfully parsed JSON string")
            except json.JSONDecodeError as e:
                logger.error(f"Failed to parse JSON string: {e}")
                return pd.DataFrame(columns=fields)
        
        # If the data is wrapped in an array, get the first item
        if isinstance(json_data, list) and len(json_data) > 0:
            logger.info("Data is wrapped in an array, extracting first item")
            json_data = json_data[0]
        
        # If the data is a dictionary with numeric keys, get the first value
        if isinstance(json_data, dict):
            keys = list(json_data.keys())
            logger.info(f"Checking dictionary keys: {keys}")
            # Check if all keys are integers or string representations of integers
            if all(isinstance(k, int) or (isinstance(k, str) and k.isdigit()) for k in keys):
                logger.info("Data has numeric keys, extracting first value")
                first_key = sorted(keys, key=lambda x: int(x) if isinstance(x, str) else x)[0]
                json_data = json_data[first_key]
                logger.info(f"Extracted data from key '{first_key}'")
        
        logger.info(f"JSON data keys: {list(json_data.keys()) if isinstance(json_data, dict) else 'Not a dict'}")
        
        # Create a list to store rows
        rows = []
        
        # Get the length of the first array to determine number of rows
        if isinstance(json_data, dict) and len(json_data) > 0:
            first_field = list(json_data.keys())[0]
            num_rows = len(json_data[first_field]) if isinstance(json_data[first_field], list) else 1
            logger.info(f"Number of rows to process: {num_rows}")
            
            # Create a row for each index
            for i in range(num_rows):
                logger.debug(f"Processing row {i}")
                row = {}
                for field in fields:
                    if field in json_data and isinstance(json_data[field], list) and i < len(json_data[field]):
                        row[field] = json_data[field][i]
                        logger.debug(f"Field '{field}' value at index {i}: {json_data[field][i]}")
                    else:
                        row[field] = None
                        logger.debug(f"Field '{field}' not found or index {i} out of bounds")
                rows.append(row)
        else:
            logger.error(f"Unexpected data structure: {type(json_data)}")
            return pd.DataFrame(columns=fields)
        
        # Create DataFrame with all requested fields as columns
        df = pd.DataFrame(rows)
        logger.info(f"Created DataFrame with shape: {df.shape}")
        logger.info(f"DataFrame columns: {df.columns.tolist()}")
        
        # Ensure columns are in the same order as the fields list
        df = df[fields]
        logger.info(f"Final DataFrame columns after reordering: {df.columns.tolist()}")
        
        return df

    # ============================================================================
    # SECTION 1: FILE UPLOAD
    # ============================================================================
    st.header("📄 Step 1: Upload Document")
    pdf_file = st.file_uploader("Upload PDF", type=["pdf"], help="Select a PDF file to process")
    
    if pdf_file:
        st.success(f"✅ File uploaded: {pdf_file.name}")
    
    # ============================================================================
    # SECTION 2: STRATEGY SELECTION
    # ============================================================================
    st.header("🎯 Step 2: Select Extraction Strategy")
    
    strategy = st.radio(
        "Choose your extraction approach:",
        ["Original Strategy", "Unique Indices Strategy"],
        help="**Original Strategy**: Process document page by page, extracting each field individually. **Unique Indices Strategy**: Process entire document at once using unique combinations of indices.",
        horizontal=True
    )
    
    if strategy == "Original Strategy":
        st.info("📋 **Original Strategy**: Will extract fields one by one from the document pages.")
    else:
        st.info("🔍 **Unique Indices Strategy**: Will find unique combinations and extract additional fields for each.")
    
    # ============================================================================
    # SECTION 3: CONFIGURATION (Only for Unique Indices Strategy)
    # ============================================================================
    if strategy == "Unique Indices Strategy":
        st.header("⚙️ Step 3: Configuration")
        
        # File Type Selection
        col1, col2 = st.columns([3, 1])
        with col1:
            # Get available configurations
            config_names = config_manager.get_config_names()
            
            selected_config_name = st.selectbox(
                "Select File Type Configuration:",
                config_names,
                format_func=lambda x: config_manager.get_config(x)['name'] if config_manager.get_config(x) else x,
                help="Choose a predefined configuration or create a new one"
            )
        with col2:
            if st.button("🔄 Load Config", help="Load the selected configuration"):
                config = config_manager.get_config(selected_config_name)
                if config:
                    # Update fields
                    st.session_state.fields_str = config.get('fields', '')
                    
                    # Update field descriptions table
                    field_descs = config.get('field_descriptions', {})
                    st.session_state.field_descriptions_table = []
                    for field_name, field_info in field_descs.items():
                        st.session_state.field_descriptions_table.append({
                            'field_name': field_name,
                            'field_description': field_info.get('description', ''),
                            'format': field_info.get('format', ''),
                            'examples': field_info.get('examples', ''),
                            'possible_values': field_info.get('possible_values', '')
                        })
                    
                    # Update unique indices descriptions table
                    unique_descs = config.get('unique_indices_descriptions', {})
                    st.session_state.unique_indices_descriptions_table = []
                    for field_name, field_info in unique_descs.items():
                        st.session_state.unique_indices_descriptions_table.append({
                            'field_name': field_name,
                            'field_description': field_info.get('description', ''),
                            'format': field_info.get('format', ''),
                            'examples': field_info.get('examples', ''),
                            'possible_values': field_info.get('possible_values', '')
                        })
                    
                    st.session_state.last_selected_config = selected_config_name
                    st.success(f"✅ Configuration '{config['name']}' loaded successfully!")
                    st.rerun()
                else:
                    st.error("❌ Failed to load configuration")
        
        # Clear Configuration Button
        if st.button("🗑️ Clear All Configuration", help="Clear all configuration and start fresh"):
            st.session_state.field_descriptions_table = []
            st.session_state.unique_indices_descriptions_table = []
            st.session_state.fields_str = ""
            st.session_state.last_selected_config = ""
            st.success("✅ Configuration cleared!")
            st.rerun()
        
        # ============================================================================
        # SECTION 4: FIELD DESCRIPTIONS
        # ============================================================================
        st.subheader("📝 Field Descriptions")
        st.markdown("""
        <div style="background-color: #e8f4fd; padding: 1rem; border-radius: 0.5rem; border-left: 4px solid #1f77b4; color: #333;">
        <strong>Field Descriptions</strong><br>
        Add descriptions for the fields you want to extract. These help the system understand what to look for.
        </div>
        """, unsafe_allow_html=True)
        
        # Create the table interface
        col1, col2, col3, col4, col5, col6 = st.columns([2, 3, 2, 2, 2, 1])
        
        with col1:
            st.markdown("**Field Name**")
        with col2:
            st.markdown("**Field Description**")
        with col3:
            st.markdown("**Format**")
        with col4:
            st.markdown("**Examples**")
        with col5:
            st.markdown("**Possible Values**")
        with col6:
            st.markdown("**Actions**")
        
        # Display existing rows
        for i, row in enumerate(st.session_state.field_descriptions_table):
            col1, col2, col3, col4, col5, col6 = st.columns([2, 3, 2, 2, 2, 1])
            
            with col1:
                field_name = st.text_input("", value=row.get('field_name', ''), key=f"field_name_{i}")
            with col2:
                field_desc = st.text_input("", value=row.get('field_description', ''), key=f"field_desc_{i}")
            with col3:
                field_format = st.text_input("", value=row.get('format', ''), key=f"field_format_{i}")
            with col4:
                field_examples = st.text_input("", value=row.get('examples', ''), key=f"field_examples_{i}")
            with col5:
                field_possible_values = st.text_input("", value=row.get('possible_values', ''), key=f"field_possible_values_{i}")
            with col6:
                if st.button("🗑️", key=f"delete_{i}", help="Delete this row"):
                    st.session_state.field_descriptions_table.pop(i)
                    st.rerun()
            
            # Update the row in session state
            st.session_state.field_descriptions_table[i] = {
                'field_name': field_name,
                'field_description': field_desc,
                'format': field_format,
                'examples': field_examples,
                'possible_values': field_possible_values
            }
        
        # Add new row button
        if st.button("➕ Add Field Description Row"):
            st.session_state.field_descriptions_table.append({
                'field_name': '',
                'field_description': '',
                'format': '',
                'examples': '',
                'possible_values': ''
            })
            st.rerun()
        
        # ============================================================================
        # SECTION 5: UNIQUE FIELD DESCRIPTIONS
        # ============================================================================
        st.subheader("🔑 Unique Field Descriptions")
        st.markdown("""
        <div style="background-color: #fff8e1; padding: 1rem; border-radius: 0.5rem; border-left: 4px solid #ffc107; color: #333;">
        <strong>Unique Field Descriptions</strong><br>
        Add descriptions for the unique fields that will be used to identify different combinations in the document.
        </div>
        """, unsafe_allow_html=True)
        
        # Create the table interface for unique indices
        col1, col2, col3, col4, col5, col6 = st.columns([2, 3, 2, 2, 2, 1])
        
        with col1:
            st.markdown("**Field Name**")
        with col2:
            st.markdown("**Field Description**")
        with col3:
            st.markdown("**Format**")
        with col4:
            st.markdown("**Examples**")
        with col5:
            st.markdown("**Possible Values**")
        with col6:
            st.markdown("**Actions**")
        
        # Display existing rows for unique indices
        for i, row in enumerate(st.session_state.unique_indices_descriptions_table):
            col1, col2, col3, col4, col5, col6 = st.columns([2, 3, 2, 2, 2, 1])
            
            with col1:
                idx_field_name = st.text_input("", value=row.get('field_name', ''), key=f"unique_field_name_{i}")
            with col2:
                idx_field_desc = st.text_input("", value=row.get('field_description', ''), key=f"unique_field_desc_{i}")
            with col3:
                idx_field_format = st.text_input("", value=row.get('format', ''), key=f"unique_field_format_{i}")
            with col4:
                idx_field_examples = st.text_input("", value=row.get('examples', ''), key=f"unique_field_examples_{i}")
            with col5:
                idx_field_possible_values = st.text_input("", value=row.get('possible_values', ''), key=f"unique_field_possible_values_{i}")
            with col6:
                if st.button("🗑️", key=f"unique_delete_{i}", help="Delete this row"):
                    st.session_state.unique_indices_descriptions_table.pop(i)
                    st.rerun()
            
            # Update the row in session state
            st.session_state.unique_indices_descriptions_table[i] = {
                'field_name': idx_field_name,
                'field_description': idx_field_desc,
                'format': idx_field_format,
                'examples': idx_field_examples,
                'possible_values': idx_field_possible_values
            }
        
        # Add new row button for unique indices
        if st.button("➕ Add Unique Field Description Row"):
            st.session_state.unique_indices_descriptions_table.append({
                'field_name': '',
                'field_description': '',
                'format': '',
                'examples': '',
                'possible_values': ''
            })
            st.rerun()
        
        # ============================================================================
        # SECTION 6: SAVE CONFIGURATION
        # ============================================================================
        st.subheader("💾 Save Configuration")
        st.markdown("""
        <div style="background-color: #e1f5fe; padding: 1rem; border-radius: 0.5rem; border-left: 4px solid #17a2b8; color: #333;">
        <strong>Save Current Configuration</strong><br>
        Save your current configuration as a new file type for future use.
        </div>
        """, unsafe_allow_html=True)
        
        col1, col2 = st.columns([3, 1])
        with col1:
            save_config_name = st.text_input(
                "Configuration Name:",
                placeholder="Enter a name for this configuration (e.g., 'Biotech Report', 'Clinical Data')",
                help="Choose a descriptive name that will appear in the dropdown"
            )
        with col2:
            if st.button("💾 Save Config", help="Save the current configuration"):
                if save_config_name:
                    # Prepare configuration data
                    field_descs = {}
                    for row in st.session_state.field_descriptions_table:
                        if row['field_name']:  # Only include rows with field names
                            field_descs[row['field_name']] = {
                                'description': row['field_description'],
                                'format': row['format'],
                                'examples': row['examples'],
                                'possible_values': row['possible_values']
                            }
                    
                    # Get unique indices descriptions
                    unique_indices_descs = {}
                    for row in st.session_state.unique_indices_descriptions_table:
                        if row['field_name']:  # Only include rows with field names
                            unique_indices_descs[row['field_name']] = {
                                'description': row['field_description'],
                                'format': row['format'],
                                'examples': row['examples'],
                                'possible_values': row['possible_values']
                            }
                    
                    # Get fields from unique indices
                    fields_str = ", ".join([row['field_name'] for row in st.session_state.unique_indices_descriptions_table if row['field_name']])
                    
                    config_data = {
                        'name': save_config_name,
                        'description': f"Configuration for {save_config_name}",
                        'fields': fields_str,
                        'field_descriptions': field_descs,
                        'unique_indices_descriptions': unique_indices_descs
                    }
                    
                    if config_manager.save_config(save_config_name, config_data):
                        st.success(f"✅ Configuration '{save_config_name}' saved successfully!")
                        config_manager.reload_configs()
                        st.rerun()
                    else:
                        st.error("❌ Failed to save configuration")
                else:
                    st.error("❌ Please enter a configuration name")
    
    # ============================================================================
    # SECTION 7: ORIGINAL STRATEGY CONFIGURATION
    # ============================================================================
    else:  # Original Strategy
        st.header("⚙️ Step 3: Field Configuration")
        
        fields_str = st.text_input(
            "Fields to Extract (comma-separated):",
            value=st.session_state.fields_str,
            key="fields_input",
            help="Enter the field names you want to extract, separated by commas"
        )
        st.session_state.fields_str = fields_str

    # ============================================================================
    # SECTION 8: EXECUTION
    # ============================================================================
    st.header("🚀 Step 4: Run Extraction")
    
    # Convert table to JSON for processing
    field_descs = {}
    if st.session_state.field_descriptions_table:
        for row in st.session_state.field_descriptions_table:
            if row['field_name']:  # Only include rows with field names
                field_descs[row['field_name']] = {
                    'description': row['field_description'],
                    'format': row['format'],
                    'examples': row['examples'],
                    'possible_values': row['possible_values']
                }

    # Prepare unique indices for Unique Indices Strategy
    unique_indices = None
    unique_indices_descriptions = None
    if strategy == "Unique Indices Strategy":
        # Convert unique indices table to JSON for processing and extract field names
        unique_indices_descriptions = {}
        unique_indices = []
        if st.session_state.unique_indices_descriptions_table:
            for row in st.session_state.unique_indices_descriptions_table:
                if row['field_name']:  # Only include rows with field names
                    unique_indices.append(row['field_name'])
                    unique_indices_descriptions[row['field_name']] = {
                        'description': row['field_description'],
                        'format': row['format'],
                        'examples': row['examples'],
                        'possible_values': row['possible_values']
                    }
    
    # Status indicator
    if pdf_file:
        if strategy == "Original Strategy":
            field_count = len([f.strip() for f in st.session_state.fields_str.split(",") if f.strip()])
            st.info(f"📊 Ready to extract {field_count} fields using Original Strategy")
        else:
            unique_count = len(unique_indices) if unique_indices else 0
            field_count = len(field_descs)
            st.info(f"📊 Ready to extract {field_count} additional fields for {unique_count} unique combinations using Unique Indices Strategy")
    
    # Run button
    if st.button("🚀 Run Extraction", type="primary", disabled=not pdf_file):
        if not pdf_file:
            st.error("❌ Please upload a PDF file first")
        else:
            # Prepare field list based on strategy
            if strategy == "Original Strategy":
                field_list = [f.strip() for f in st.session_state.fields_str.split(",") if f.strip()]
            else:  # Unique Indices Strategy
                # For Unique Indices Strategy, get fields from the unique indices descriptions table
                field_list = []
                if st.session_state.unique_indices_descriptions_table:
                    for row in st.session_state.unique_indices_descriptions_table:
                        if row['field_name']:  # Only include rows with field names
                            field_list.append(row['field_name'])

            try:
                with st.spinner("Planning …"):
                    # quick first-page text preview to give LLM document context
                    doc = fitz.open(stream=pdf_file.getvalue(), filetype="pdf")  # type: ignore[arg-type]
                    preview = "\n".join(page.get_text() for page in doc[:10])[:20000]  # first 2 pages, 2k chars

                    # Create a cost tracker for this run
                    cost_tracker = CostTracker()

                    planner = Planner(cost_tracker=cost_tracker)
                    plan = planner.build_plan(
                        pdf_meta={"filename": pdf_file.name},
                        doc_preview=preview,
                        fields=field_list,
                        field_descs=field_descs,
                        strategy=strategy,
                        unique_indices=unique_indices,
                        unique_indices_descriptions=unique_indices_descriptions
                    )
                    
                    # Add a visual separator
                    st.markdown("---")

                with st.spinner("Executing …"):
                    executor = Executor(settings=settings, cost_tracker=cost_tracker)
                    results, logs = executor.run(plan, pdf_file)

                    # Get detailed costs
                    costs = executor.cost_tracker.calculate_current_file_costs()
                    model_cost = costs["openai"]["total_cost"]
                    di_cost = costs["document_intelligence"]["total_cost"]

                    # Add debug logging for cost tracking
                    logger.info(f"Cost tracker debug info:")
                    logger.info(f"  LLM input tokens: {executor.cost_tracker.llm_input_tokens}")
                    logger.info(f"  LLM output tokens: {executor.cost_tracker.llm_output_tokens}")
                    logger.info(f"  DI pages: {executor.cost_tracker.di_pages}")
                    logger.info(f"  LLM calls count: {len(executor.cost_tracker.llm_calls)}")
                    logger.info(f"  Current file costs: {executor.cost_tracker.current_file_costs}")
                    logger.info(f"  Calculated costs: {costs}")

                    # Display detailed costs table
                    st.subheader("Detailed Costs")
                    costs_df = executor.cost_tracker.get_detailed_costs_table()
                    st.dataframe(costs_df, use_container_width=True)

                    st.info(
                        f"LLM input tokens: {executor.cost_tracker.llm_input_tokens}, "
                        f"LLM output tokens: {executor.cost_tracker.llm_output_tokens}, "
                        f"DI pages: {executor.cost_tracker.di_pages}, "
                        f"Model cost: ${model_cost:.4f}, "
                        f"DI cost: ${di_cost:.4f}, "
                        f"Total cost: ${model_cost + di_cost:.4f}"
                    )

                    # Add detailed logging about what executor returned
                    logger.info(f"Executor returned results of type: {type(results)}")
                    logger.info(f"Results content: {results}")
                    
                    # Check if results is already a DataFrame
                    if isinstance(results, pd.DataFrame):
                        logger.info(f"Results is already a DataFrame with shape: {results.shape}")
                        logger.info(f"DataFrame columns: {results.columns.tolist()}")
                        logger.info(f"DataFrame head: {results.head()}")
                        df = results
                    else:
                        logger.info("Results is not a DataFrame, calling flatten_json_response")
                        # Process results using flatten_json_response
                        df = flatten_json_response(results, field_list)
                    
                    # Log final DataFrame info
                    logger.info(f"Final DataFrame shape: {df.shape}")
                    logger.info(f"Final DataFrame columns: {df.columns.tolist()}")
                    if not df.empty:
                        logger.info(f"Final DataFrame sample: {df.head()}")

                    # Store execution in history
                    execution_record = {
                        "filename": pdf_file.name,
                        "datetime": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
                        "fields": field_list,
                        "logs": log_capture.get_logs(),  # Store the actual logs
                        "results": df.to_dict() if not df.empty else None
                    }
                    st.session_state.execution_history.append(execution_record)
                    log_capture.clear()  # Clear logs after storing them

                # ----------------- UI: show execution tree -----------------
                st.subheader("Execution trace")
                for log in logs:
                    indent = "&nbsp;" * 4 * log["depth"]
                    # Add error indicator if there was an error
                    error_indicator = "❌ " if log.get("error") else "✓ "
                    # Use a fixed preview text instead of the result
                    with st.expander(f"{indent}{error_indicator}{log['tool']} – Click to view result"):
                        st.markdown(f"**Args**: `{log['args']}`", unsafe_allow_html=True)
                        if log.get("error"):
                            st.error(f"Error: {log['error']}")
                        
                        # Special handling for IndexAgent output
                        if log['tool'] == "IndexAgent" and isinstance(log["result"], dict):
                            # Display chunk statistics if available
                            if "chunk_stats" in log["result"]:
                                st.markdown("### Chunk Statistics")
                                # Create a DataFrame for better visualization
                                stats_df = pd.DataFrame(log["result"]["chunk_stats"])
                                st.dataframe(stats_df)
                                
                                # Add summary statistics
                                st.markdown("### Summary")
                                st.markdown(f"""
                                - Total chunks: {len(stats_df)}
                                - Average chunk length: {stats_df['length'].mean():.0f} characters
                                - Shortest chunk: {stats_df['length'].min()} characters
                                - Longest chunk: {stats_df['length'].max()} characters
                                """)
                                
                                # Add a bar chart of chunk lengths
                                st.markdown("### Chunk Length Distribution")
                                st.bar_chart(stats_df.set_index('chunk_number')['length'])
                        else:
                            st.code(log["result"])

                if not df.empty:
                    st.success("Done ✓")
                    st.dataframe(df)
                    st.download_button("Download CSV", df.to_csv(index=False), "results.csv")
                else:
                    st.warning("No results were extracted. Check the execution trace for errors.")
            except Exception as e:
                logging.exception("App error:")
                st.error(f"An error occurred: {e}")