casl_analysis.py

import gradio as gr
import boto3
import json
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import re
import logging
import os
import pickle
import csv
from PIL import Image
import io
import PyPDF2
import uuid
from datetime import datetime

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# AWS credentials for Bedrock API
# For HuggingFace Spaces, set these as secrets in the Space settings
AWS_ACCESS_KEY = os.getenv("AWS_ACCESS_KEY", "")
AWS_SECRET_KEY = os.getenv("AWS_SECRET_KEY", "")
AWS_REGION = os.getenv("AWS_REGION", "us-east-1")

# Initialize Bedrock client if credentials are available
bedrock_client = None
if AWS_ACCESS_KEY and AWS_SECRET_KEY:
    try:
        bedrock_client = boto3.client(
            'bedrock-runtime',
            aws_access_key_id=AWS_ACCESS_KEY,
            aws_secret_access_key=AWS_SECRET_KEY,
            region_name=AWS_REGION
        )
        logger.info("Bedrock client initialized successfully")
    except Exception as e:
        logger.error(f"Failed to initialize Bedrock client: {str(e)}")

# Sample transcript for the demo
SAMPLE_TRANSCRIPT = """*PAR: today I would &-um like to talk about &-um a fun trip I took last &-um summer with my family.
*PAR: we went to the &-um &-um beach [//] no to the mountains [//] I mean the beach actually.
*PAR: there was lots of &-um &-um swimming and &-um sun.
*PAR: we [/] we stayed for &-um three no [//] four days in a &-um hotel near the water [: ocean] [*].
*PAR: my favorite part was &-um building &-um castles with sand.
*PAR: sometimes I forget [//] forgetted [: forgot] [*] what they call those things we built.
*PAR: my brother he [//] he helped me dig a big hole.
*PAR: we saw [/] saw fishies [: fish] [*] swimming in the water.
*PAR: sometimes I wonder [/] wonder where fishies [: fish] [*] go when it's cold.
*PAR: maybe they have [/] have houses under the water.
*PAR: after swimming we [//] I eat [: ate] [*] &-um ice cream with &-um chocolate things on top.
*PAR: what do you call those &-um &-um sprinkles! that's the word.
*PAR: my mom said to &-um that I could have &-um two scoops next time.
*PAR: I want to go back to the beach [/] beach next year."""

# ===============================
# Database and Storage Functions
# ===============================

# Create data directories if they don't exist
DATA_DIR = "patient_data"
RECORDS_FILE = os.path.join(DATA_DIR, "patient_records.csv")
ANALYSES_DIR = os.path.join(DATA_DIR, "analyses")

def ensure_data_dirs():
    """Ensure data directories exist"""
    os.makedirs(DATA_DIR, exist_ok=True)
    os.makedirs(ANALYSES_DIR, exist_ok=True)
    
    # Create records file if it doesn't exist
    if not os.path.exists(RECORDS_FILE):
        with open(RECORDS_FILE, 'w', newline='') as f:
            writer = csv.writer(f)
            writer.writerow([
                "ID", "Name", "Record ID", "Age", "Gender", 
                "Assessment Date", "Clinician", "Analysis Date", "File Path"
            ])

# Initialize data directories
ensure_data_dirs()

def save_patient_record(patient_info, analysis_results, transcript):
    """Save patient record to storage"""
    try:
        # Generate unique ID for the record
        record_id = str(uuid.uuid4())
        
        # Extract patient information
        name = patient_info.get("name", "")
        patient_id = patient_info.get("record_id", "")
        age = patient_info.get("age", "")
        gender = patient_info.get("gender", "")
        assessment_date = patient_info.get("assessment_date", "")
        clinician = patient_info.get("clinician", "")
        
        # Create filename for the analysis data
        filename = f"analysis_{record_id}.pkl"
        filepath = os.path.join(ANALYSES_DIR, filename)
        
        # Save analysis data
        with open(filepath, 'wb') as f:
            pickle.dump({
                "patient_info": patient_info,
                "analysis_results": analysis_results,
                "transcript": transcript,
                "timestamp": datetime.now().isoformat(),
            }, f)
        
        # Add record to CSV file
        with open(RECORDS_FILE, 'a', newline='') as f:
            writer = csv.writer(f)
            writer.writerow([
                record_id, name, patient_id, age, gender, 
                assessment_date, clinician, datetime.now().strftime('%Y-%m-%d'),
                filepath
            ])
        
        return record_id
    
    except Exception as e:
        logger.error(f"Error saving patient record: {str(e)}")
        return None

def load_patient_record(record_id):
    """Load patient record from storage"""
    try:
        # Find the record in the CSV file
        with open(RECORDS_FILE, 'r', newline='') as f:
            reader = csv.reader(f)
            next(reader)  # Skip header
            for row in reader:
                if row[0] == record_id:
                    file_path = row[8]
                    
                    # Load and return the data
                    with open(file_path, 'rb') as f:
                        return pickle.load(f)
        
        return None
    
    except Exception as e:
        logger.error(f"Error loading patient record: {str(e)}")
        return None

def get_all_patient_records():
    """Return a list of all patient records"""
    try:
        records = []
        if os.path.exists(RECORDS_FILE):
            with open(RECORDS_FILE, 'r', newline='') as f:
                reader = csv.reader(f)
                next(reader)  # Skip header
                for row in reader:
                    records.append({
                        "id": row[0],
                        "name": row[1],
                        "record_id": row[2],
                        "age": row[3],
                        "gender": row[4],
                        "assessment_date": row[5],
                        "clinician": row[6],
                        "analysis_date": row[7]
                    })
        return records
    
    except Exception as e:
        logger.error(f"Error getting patient records: {str(e)}")
        return []

# ===============================
# Utility Functions
# ===============================

def read_pdf(file_path):
    """Read text from a PDF file"""
    try:
        with open(file_path, 'rb') as file:
            pdf_reader = PyPDF2.PdfReader(file)
            text = ""
            for page in pdf_reader.pages:
                text += page.extract_text()
            return text
    except Exception as e:
        logger.error(f"Error reading PDF: {str(e)}")
        return ""

def read_cha_file(file_path):
    """Read and parse a .cha transcript file"""
    try:
        with open(file_path, 'r', encoding='utf-8', errors='ignore') as f:
            content = f.read()
            
        # Extract participant lines (starting with *PAR:)
        par_lines = []
        for line in content.splitlines():
            if line.startswith('*PAR:'):
                par_lines.append(line)
                
        # If no PAR lines found, just return the whole content
        if not par_lines:
            return content
            
        return '\n'.join(par_lines)
    
    except Exception as e:
        logger.error(f"Error reading CHA file: {str(e)}")
        return ""

def process_upload(file):
    """Process an uploaded file (PDF, text, or CHA)"""
    if file is None:
        return ""
    
    file_path = file.name
    if file_path.endswith('.pdf'):
        return read_pdf(file_path)
    elif file_path.endswith('.cha'):
        return read_cha_file(file_path)
    else:
        with open(file_path, 'r', encoding='utf-8', errors='ignore') as f:
            return f.read()

# ===============================
# AI Model Interface Functions
# ===============================

def call_bedrock(prompt, max_tokens=4096):
    """Call the AWS Bedrock API to analyze text using Claude"""
    if not bedrock_client:
        return "AWS credentials not configured. Please set your AWS credentials as secrets in the Space settings."
    
    try:
        body = json.dumps({
            "anthropic_version": "bedrock-2023-05-31",
            "max_tokens": max_tokens,
            "messages": [
                {
                    "role": "user",
                    "content": prompt
                }
            ],
            "temperature": 0.3,
            "top_p": 0.9
        })

        modelId = 'anthropic.claude-3-sonnet-20240229-v1:0'
        response = bedrock_client.invoke_model(
            body=body, 
            modelId=modelId, 
            accept='application/json', 
            contentType='application/json'
        )
        response_body = json.loads(response.get('body').read())
        return response_body['content'][0]['text']
    except Exception as e:
        logger.error(f"Error in call_bedrock: {str(e)}")
        return f"Error: {str(e)}"

def generate_demo_response(prompt):
    """Generate a simulated response for demo purposes"""
    # This function generates a realistic but fake response for demo purposes
    # In a real deployment, you would call an actual LLM API
    
    random_seed = sum(ord(c) for c in prompt) % 1000  # Generate a seed based on prompt
    np.random.seed(random_seed)
    
    # Simulate speech factors with random but reasonable values
    factors = [
        "Difficulty producing fluent speech",
        "Word retrieval issues",
        "Grammatical errors",
        "Repetitions and revisions",
        "Neologisms",
        "Perseveration",
        "Comprehension issues"
    ]
    
    occurrences = np.random.randint(1, 15, size=len(factors))
    percentiles = np.random.randint(30, 95, size=len(factors))
    
    # Simulate CASL scores
    domains = ["Lexical/Semantic", "Syntactic", "Supralinguistic"]
    scores = np.random.randint(80, 115, size=3)
    percentiles_casl = [int(np.interp(s, [70, 85, 100, 115, 130], [2, 16, 50, 84, 98])) for s in scores]
    
    perf_levels = []
    for s in scores:
        if s < 70: perf_levels.append("Well Below Average")
        elif s < 85: perf_levels.append("Below Average")
        elif s < 115: perf_levels.append("Average")
        elif s < 130: perf_levels.append("Above Average")
        else: perf_levels.append("Well Above Average")
    
    # Build response
    response = "## Speech Factor Analysis\n\n"
    for i, factor in enumerate(factors):
        response += f"{factor}: {occurrences[i]}, {percentiles[i]}\n"
    
    response += "\n## CASL-2 Assessment\n\n"
    for i, domain in enumerate(domains):
        response += f"{domain} Skills: Standard Score ({scores[i]}), Percentile Rank ({percentiles_casl[i]}%), Performance Level ({perf_levels[i]})\n"
    
    response += "\n## Other analysis/Best plans of action:\n\n"
    suggestions = [
        "Implement word-finding strategies with semantic cuing",
        "Practice structured narrative tasks with visual supports",
        "Use sentence formulation exercises with increasing complexity",
        "Incorporate self-monitoring techniques during structured conversations",
        "Work on grammatical forms through structured practice"
    ]
    for suggestion in suggestions:
        response += f"- {suggestion}\n"
    
    response += "\n## Explanation:\n\n"
    response += "Based on the analysis, this patient demonstrates moderate word-finding difficulties with compensatory strategies like filler words and repetitions. Their syntactic skills show some weakness in verb tense consistency. Treatment should focus on building vocabulary access, grammatical accuracy, and narrative structure using scaffolded support.\n"
    
    response += "\n## Additional Analysis:\n\n"
    response += "The patient shows relative strengths in conversation maintenance and topic coherence. Consider building on these strengths while addressing specific language formulation challenges. Recommended frequency: 2-3 sessions per week for 10-12 weeks with periodic reassessment."
    
    return response

def generate_demo_transcription(audio_path):
    """Generate a simulated transcription response"""
    # In a real app, this would process an audio file
    return "*PAR: today I want to tell you about my favorite toy.\n*PAR: it's a &-um teddy bear that I got for my birthday.\n*PAR: he has &-um brown fur and a red bow.\n*PAR: I like to sleep with him every night.\n*PAR: sometimes I take him to school in my backpack."

def generate_demo_qa_response(question):
    """Generate a simulated Q&A response"""
    qa_responses = {
        "what is casl": "CASL-2 (Comprehensive Assessment of Spoken Language, Second Edition) is a standardized assessment tool used by Speech-Language Pathologists to evaluate a child's oral language abilities across multiple domains including lexical/semantic, syntactic, and supralinguistic skills. It helps identify language disorders and guides intervention planning.",
        "how do i interpret scores": "CASL-2 scores include standard scores (mean=100, SD=15), percentile ranks, and performance levels. Standard scores below 85 indicate below average performance, 85-115 is average, and above 115 is above average. Percentile ranks show how a child performs relative to same-age peers.",
        "what activities help word finding": "Activities to improve word-finding skills include semantic feature analysis (describing attributes of objects), categorization tasks, word association games, rapid naming practice, and structured conversation with gentle cueing. Visual supports and semantic mapping can also be helpful.",
        "how often should therapy occur": "The recommended frequency for speech-language therapy typically ranges from 1-3 sessions per week, depending on the severity of the impairment. For moderate difficulties, twice weekly sessions of 30-45 minutes are common. Consistency is important for progress.",
        "when should i reassess": "Reassessment is typically recommended every 3-6 months to track progress and adjust treatment goals. For educational settings, annual reassessment is common. More frequent informal assessments can help guide ongoing intervention.",
    }
    
    # Simple keyword matching for demo purposes
    for key, response in qa_responses.items():
        if key in question.lower():
            return response
    
    return "I don't have specific information about that topic. For detailed professional guidance, consult with a licensed Speech-Language Pathologist who can provide advice specific to your situation."

# ===============================
# Analysis Functions
# ===============================

def parse_casl_response(response):
    """Parse the LLM response for CASL analysis into structured data"""
    lines = response.split('\n')
    data = {
        'Factor': [],
        'Occurrences': [],
        'Severity': [],
        'Examples': []  # Added field for error examples
    }
    
    casl_data = {
        'Domain': ['Lexical/Semantic', 'Syntactic', 'Supralinguistic'],
        'Standard Score': [0, 0, 0],
        'Percentile': [0, 0, 0],
        'Performance Level': ['', '', ''],
        'Examples': ['', '', '']  # Added field for specific examples
    }
    
    treatment_suggestions = []
    explanation = ""
    additional_analysis = ""
    specific_errors = {}  # Track specific error examples by factor
    raw_response = response  # Store the complete raw LLM response
    
    # Pattern to match factor lines - updated to potentially capture examples
    factor_pattern = re.compile(r'([\w\s/]+):\s*(\d+)[,\s]+(\d+)(?:\s*-\s*(.+))?')
    
    # Pattern to match CASL data
    casl_pattern = re.compile(r'(\w+/?\w*)\s+Skills:\s+Standard\s+Score\s+\((\d+)\),\s+Percentile\s+Rank\s+\((\d+)%\),\s+Performance\s+Level\s+\(([\w\s]+)\)')
    
    # Pattern to find examples
    example_pattern = re.compile(r'(?:Example|Examples|observed|observed in)[^\"\'"]*[\"\']([^\"\']*)[\"\']')
    error_pattern = re.compile(r'(?:error|errors|difficulty|difficulties)[^\"\'"]*[\"\']([^\"\']*)[\"\']')
    
    current_factor = None
    current_domain = None
    in_suggestions = False
    in_explanation = False
    in_additional = False
    in_examples = False
    
    for i, line in enumerate(lines):
        line = line.strip()
        
        # Skip empty lines
        if not line:
            continue
            
        # Check for factor data
        factor_match = factor_pattern.search(line)
        if factor_match:
            factor = factor_match.group(1).strip()
            occurrences = int(factor_match.group(2))
            severity = int(factor_match.group(3))
            example = factor_match.group(4) if factor_match.group(4) else ""
            
            # Look ahead to find examples for this factor
            if not example:
                # Check next few lines for examples
                for j in range(i+1, min(i+5, len(lines))):
                    next_line = lines[j].strip()
                    if next_line and ('"' in next_line or "'" in next_line):
                        example_match = example_pattern.search(next_line)
                        if example_match:
                            example = example_match.group(1)
                            break
                        error_match = error_pattern.search(next_line)
                        if error_match:
                            example = error_match.group(1)
                            break
            
            data['Factor'].append(factor)
            data['Occurrences'].append(occurrences)
            data['Severity'].append(severity)
            data['Examples'].append(example)
            specific_errors[factor] = example
            current_factor = factor
            continue
            
        # Check for CASL data
        casl_match = casl_pattern.search(line)
        if casl_match:
            domain = casl_match.group(1)
            score = int(casl_match.group(2))
            percentile = int(casl_match.group(3))
            level = casl_match.group(4)
            
            domain_examples = ""
            # Look ahead for examples related to this domain
            for j in range(i+1, min(i+10, len(lines))):
                next_line = lines[j].strip()
                if "Domain:" in next_line or casl_pattern.search(next_line):
                    break
                if ('"' in next_line or "'" in next_line) and "example" in next_line.lower():
                    example_match = re.search(r'[\"\']([^\"\']*)[\"\']', next_line)
                    if example_match:
                        domain_examples = example_match.group(1)
                        break
            
            if "Lexical" in domain:
                casl_data['Standard Score'][0] = score
                casl_data['Percentile'][0] = percentile
                casl_data['Performance Level'][0] = level
                casl_data['Examples'][0] = domain_examples
                current_domain = "Lexical/Semantic"
            elif "Syntactic" in domain:
                casl_data['Standard Score'][1] = score
                casl_data['Percentile'][1] = percentile
                casl_data['Performance Level'][1] = level
                casl_data['Examples'][1] = domain_examples
                current_domain = "Syntactic"
            elif "Supralinguistic" in domain:
                casl_data['Standard Score'][2] = score
                casl_data['Percentile'][2] = percentile
                casl_data['Performance Level'][2] = level
                casl_data['Examples'][2] = domain_examples
                current_domain = "Supralinguistic"
            continue
        
        # Check for section headers
        if "Other analysis/Best plans of action:" in line or "### Recommended Treatment Approaches" in line or "Treatment Recommendations:" in line:
            in_suggestions = True
            in_explanation = False
            in_additional = False
            in_examples = False
            continue
        elif "Explanation:" in line or "### Clinical Rationale" in line or "Clinical Rationale:" in line:
            in_suggestions = False
            in_explanation = True
            in_additional = False
            in_examples = False
            continue
        elif "Additional Analysis:" in line or "Further Observations:" in line:
            in_suggestions = False
            in_explanation = False
            in_additional = True
            in_examples = False
            continue
        elif "Examples:" in line or "Specific Errors:" in line:
            in_suggestions = False
            in_explanation = False
            in_additional = False
            in_examples = True
            continue
            
        # Add content to appropriate section
        if in_suggestions:
            if line.startswith("- "):
                treatment_suggestions.append(line[2:])  # Remove the bullet point
            elif line.startswith("•"):
                treatment_suggestions.append(line[1:].strip())  # Remove bullet and trim
            elif line and not line.startswith("#"):
                # Non-empty, non-header lines might be treatment suggestions without bullets
                treatment_suggestions.append(line)
        elif in_explanation:
            explanation += line + "\n"
        elif in_additional:
            additional_analysis += line + "\n"
        elif in_examples and current_factor and not specific_errors.get(current_factor):
            # Look for quoted examples in the examples section
            if '"' in line or "'" in line:
                example_match = re.search(r'[\"\']([^\"\']*)[\"\']', line)
                if example_match:
                    specific_errors[current_factor] = example_match.group(1)
                    # Update the examples in the dataframe
                    if current_factor in data['Factor']:
                        idx = data['Factor'].index(current_factor)
                        data['Examples'][idx] = example_match.group(1)
        
        # Continuously look for examples with quotes regardless of section
        if ('"' in line or "'" in line) and current_factor:
            if re.search(rf'{current_factor}.*[\"\']([^\"\']*)[\"\']', line, re.IGNORECASE):
                example_match = re.search(r'[\"\']([^\"\']*)[\"\']', line)
                if example_match:
                    specific_errors[current_factor] = example_match.group(1)
                    # Update in dataframe
                    if current_factor in data['Factor']:
                        idx = data['Factor'].index(current_factor)
                        data['Examples'][idx] = example_match.group(1)
    
    # Process specific errors and examples if they're presented as a list later in the text
    for i, line in enumerate(lines):
        if "examples of errors" in line.lower() or "error examples" in line.lower():
            # Look through next few lines for examples
            for j in range(i+1, min(i+15, len(lines))):
                example_line = lines[j].strip()
                if not example_line or example_line.startswith("#"):
                    continue
                    
                # Look for factors mentioned with examples
                for factor in data['Factor']:
                    if factor.lower() in example_line.lower() and ('"' in example_line or "'" in example_line):
                        example_match = re.search(r'[\"\']([^\"\']*)[\"\']', example_line)
                        if example_match:
                            idx = data['Factor'].index(factor)
                            data['Examples'][idx] = example_match.group(1)
                            specific_errors[factor] = example_match.group(1)
    
    return {
        'speech_factors': pd.DataFrame(data),
        'casl_data': pd.DataFrame(casl_data),
        'treatment_suggestions': treatment_suggestions,
        'explanation': explanation,
        'additional_analysis': additional_analysis,
        'specific_errors': specific_errors,
        'raw_response': raw_response  # Include the full LLM response
    }

def create_casl_plots(speech_factors, casl_data):
    """Create visualizations for the CASL analysis results"""
    
    # Set a professional style for the plots
    plt.style.use('seaborn-v0_8-pastel')
    
    # Create figure with two subplots
    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6), dpi=100)
    
    # Plot speech factors - sorted by occurrence count
    if not speech_factors.empty:
        # Sort the dataframe
        speech_factors_sorted = speech_factors.sort_values('Occurrences', ascending=False)
        
        # Custom colors
        speech_colors = ['#4C72B0', '#55A868', '#C44E52', '#8172B3', '#CCB974', '#64B5CD', '#4C72B0']
        
        # Create horizontal bar chart
        bars = ax1.barh(speech_factors_sorted['Factor'], 
               speech_factors_sorted['Occurrences'], 
               color=speech_colors[:len(speech_factors_sorted)])
        
        # Add count labels at the end of each bar
        for i, bar in enumerate(bars):
            width = bar.get_width()
            factor = speech_factors_sorted.iloc[i]['Factor']
            
            # Get severity percentile for this factor
            severity = speech_factors_sorted.iloc[i]['Severity']
            
            # Label with both count and severity percentile
            ax1.text(width + 0.3, bar.get_y() + bar.get_height()/2, 
                    f'{width:.0f} ({severity}%)', ha='left', va='center')
            
            # Add example as annotation if available
            if 'Examples' in speech_factors_sorted.columns:
                example = speech_factors_sorted.iloc[i]['Examples']
                if example and len(example) > 0:
                    # Add a small marker to indicate example exists
                    ax1.text(0.5, bar.get_y() + bar.get_height()/2, 
                            '★', ha='center', va='center', color='#C44E52', 
                            fontsize=8, fontweight='bold')
        
        ax1.set_title('Speech Factors Analysis', fontsize=14, fontweight='bold')
        ax1.set_xlabel('Number of Occurrences', fontsize=11)
        # No y-label needed for horizontal bar chart
        
        # Remove top and right spines
        ax1.spines['top'].set_visible(False)
        ax1.spines['right'].set_visible(False)
        
        # Add a footnote about the star symbol
        ax1.annotate('★ = Example available in details panel', xy=(0, -0.1), xycoords='axes fraction', 
                   fontsize=8, ha='left', va='center', color='#C44E52')
    
    # Plot CASL domains
    domain_names = casl_data['Domain']
    y_scores = casl_data['Standard Score']
    percentiles = casl_data['Percentile']
    
    # Custom color scheme
    casl_colors = ['#4C72B0', '#55A868', '#C44E52']
    
    # Create bars with nice colors
    bars = ax2.bar(domain_names, y_scores, color=casl_colors)
    
    # Add score labels on top of each bar
    for i, bar in enumerate(bars):
        height = bar.get_height()
        score = y_scores.iloc[i]
        percentile = percentiles.iloc[i]
        
        # Label with both score and percentile
        ax2.text(bar.get_x() + bar.get_width()/2., height + 1,
                f'{score:.0f} ({percentile}%)', ha='center', va='bottom')
        
        # Add a star marker if example exists
        if 'Examples' in casl_data.columns:
            example = casl_data.iloc[i]['Examples']
            if example and len(example) > 0:
                ax2.text(bar.get_x() + bar.get_width()/2., height/2,
                        '★', ha='center', va='center', color='white', 
                        fontsize=12, fontweight='bold')
    
    # Add score reference lines
    ax2.axhline(y=100, linestyle='--', color='gray', alpha=0.7, label='Average (100)')
    ax2.axhline(y=85, linestyle=':', color='orange', alpha=0.7, label='Below Average (<85)')
    ax2.axhline(y=115, linestyle=':', color='green', alpha=0.7, label='Above Average (>115)')
    
    # Add labels and title
    ax2.set_title('CASL-2 Standard Scores', fontsize=14, fontweight='bold')
    ax2.set_ylabel('Standard Score', fontsize=11)
    ax2.set_ylim(bottom=0, top=max(130, max(y_scores) + 15))  # Set y-axis limit with some padding
    
    # Add legend
    ax2.legend(loc='upper right', fontsize='small')
    
    # Remove top and right spines
    ax2.spines['top'].set_visible(False)
    ax2.spines['right'].set_visible(False)
    
    # Add overall figure title
    fig.suptitle('Speech Analysis Results', fontsize=16, fontweight='bold', y=0.98)
    
    # Add a subtitle with note about examples
    plt.figtext(0.5, 0.01, '★ indicates specific examples available in the Error Examples panel', 
                ha='center', fontsize=9, fontstyle='italic')
    
    plt.tight_layout(rect=[0, 0.03, 1, 0.95])  # Adjust layout to make room for suptitle
    
    # Save plot to buffer
    buf = io.BytesIO()
    plt.savefig(buf, format='png', bbox_inches='tight')
    buf.seek(0)
    plt.close()
    
    return buf

def create_casl_radar_chart(speech_factors):
    """Create a radar chart for speech factors (percentiles)"""
    
    if speech_factors.empty or 'Severity' not in speech_factors.columns:
        # Create a placeholder image if no data
        plt.figure(figsize=(8, 8))
        plt.text(0.5, 0.5, "No data available for radar chart", 
                ha='center', va='center', fontsize=14)
        plt.axis('off')
        
        buf = io.BytesIO()
        plt.savefig(buf, format='png')
        buf.seek(0)
        plt.close()
        return buf
    
    # Prepare data for radar chart
    categories = speech_factors['Factor'].tolist()
    percentiles = speech_factors['Severity'].tolist()
    
    # Need to repeat first value to close the polygon
    categories = categories + [categories[0]]
    percentiles = percentiles + [percentiles[0]]
    
    # Convert to radians and calculate points
    N = len(categories) - 1  # Subtract 1 for the repeated point
    angles = [n / float(N) * 2 * np.pi for n in range(N)]
    angles += angles[:1]  # Repeat the first angle to close the polygon
    
    # Create the plot
    fig = plt.figure(figsize=(8, 8))
    ax = fig.add_subplot(111, polar=True)
    
    # Draw percentile lines with labels
    plt.xticks(angles[:-1], categories[:-1], size=12)
    ax.set_rlabel_position(0)
    plt.yticks([20, 40, 60, 80, 100], ["20", "40", "60", "80", "100"], color="grey", size=10)
    plt.ylim(0, 100)
    
    # Plot data
    ax.plot(angles, percentiles, linewidth=1, linestyle='solid', color='#4C72B0')
    ax.fill(angles, percentiles, color='#4C72B0', alpha=0.25)
    
    # Add title
    plt.title('Speech Factors Severity (Percentile)', size=15, fontweight='bold', pad=20)
    
    # Save to buffer
    buf = io.BytesIO()
    plt.savefig(buf, format='png', bbox_inches='tight')
    buf.seek(0)
    plt.close()
    
    return buf

def analyze_transcript(transcript, age, gender):
    """Analyze a speech transcript using the CASL framework"""
    
    # CHAT transcription symbol cheat sheet
    cheat_sheet = """
    CHAT TRANSCRIPTION SYMBOL SUMMARY -- Abridged for AphasiaBank

    Basic Utterance Terminators
    .    period
    ?    question
    !    exclamation

    Special Utterance Terminators
    +…    trailing off
    +..?    trailing off of a question
    +/.    interruption by another speaker
    +/?    interruption of a question by another speaker
    +//.    self-interruption
    +//?    self-interruption of a question
    +"/.    quotation follows on next line
    +"    quoted utterance occurs on this line (use at beginning of utterance
        as link, not a terminator)
    +<    lazy overlap marking (at beginning of utterance that overlapped the
        the previous utterance)

    @n    neologism (e.g., sakov@n)
    exclamations    common ones:  ah, aw, haha, ow, oy, sh, ugh, uhoh
    interjections    common ones:  mhm, uhhuh, hm, uhuh
    fillers    common ones:  &-um, &-uh
    letters    s@l
    letter sequence    abcdefg@k
    xxx    unintelligible speech, not treated as a word
    www        untranscribed material (e.g., looking through pictures, talking with
            spouse), must be followed by %exp tier (see below)
    &+sounds    phonological fragment (&+sh &+w  we came home)

    Scoped Symbols
    [: text]    target/intended word for errors (e.g., tried [: cried])
    [*]    error (e.g., paraphasia -- wɛk@u [: wet] [*])
    [/]    retracing without correction (e.g., simple repetition)
        put repeated items between <> unless only one word was repeated
    [//]    retracing with correction (e.g., simple word or grammar change)
        put changed items between <> unless only one word was changed
    """
    
    # Instructions for the LLM analysis
    instructions = """
    You are a speech pathologist analyzing this transcription sample. Provide a detailed analysis focused on specific quotes from the transcript.

    The factors of speech that you need to count are:

    1. Difficulty producing fluent, grammatical speech - speech that is slow, halting, with pauses while searching for words
    2. Word retrieval issues - trouble thinking of specific words, use of filler words like um, circumlocution, semantically similar word substitutions
    3. Grammatical errors - missing/incorrect function words, problems with verb tenses, conjugation, agreement, simplified sentences
    4. Repetitions and revisions - repeating or restating words, phrases or sentences due to trouble finding the right words
    5. Neologisms - creating nonexistent "new" words
    6. Perseveration - unintentionally repeating words or phrases over and over
    7. Comprehension issues - trouble understanding complex sentences, fast speech, relying more on context and cues

    For each factor, provide:
    - Number of occurrences
    - Severity percentile (estimate based on your clinical judgment)
    - At least 2-3 specific quotes from the transcript as examples

    Then evaluate using the CASL-2 Speech and Language Analysis Framework across these domains:

    1. Lexical/Semantic Skills:
       - Assess vocabulary diversity, word-finding abilities, semantic precision
       - Provide Standard Score (mean=100, SD=15), percentile rank, and performance level
       - Include SPECIFIC QUOTES as evidence

    2. Syntactic Skills:
       - Evaluate grammatical accuracy, sentence complexity, morphological skills
       - Provide Standard Score, percentile rank, and performance level
       - Include SPECIFIC QUOTES as evidence

    3. Supralinguistic Skills:
       - Assess figurative language use, inferencing, and abstract reasoning
       - Provide Standard Score, percentile rank, and performance level
       - Include SPECIFIC QUOTES as evidence

    YOUR RESPONSE MUST USE THESE EXACT SECTION MARKERS FOR PARSING:

    <SPEECH_FACTORS_START>
    Difficulty producing fluent, grammatical speech: (occurrences), (percentile)
    Examples:
    - "(direct quote from transcript)"
    - "(direct quote from transcript)"
    
    Word retrieval issues: (occurrences), (percentile)
    Examples:
    - "(direct quote from transcript)"
    - "(direct quote from transcript)"
    
    (And so on for each factor)
    <SPEECH_FACTORS_END>
    
    <CASL_SKILLS_START>
    Lexical/Semantic Skills: Standard Score (X), Percentile Rank (X%), Performance Level
    Examples:
    - "(direct quote showing strength or weakness)"
    - "(direct quote showing strength or weakness)"
    
    Syntactic Skills: Standard Score (X), Percentile Rank (X%), Performance Level
    Examples:
    - "(direct quote showing strength or weakness)"
    - "(direct quote showing strength or weakness)"
    
    Supralinguistic Skills: Standard Score (X), Percentile Rank (X%), Performance Level
    Examples:
    - "(direct quote showing strength or weakness)"
    - "(direct quote showing strength or weakness)"
    <CASL_SKILLS_END>
    
    <TREATMENT_RECOMMENDATIONS_START>
    - (treatment recommendation)
    - (treatment recommendation)
    - (treatment recommendation)
    <TREATMENT_RECOMMENDATIONS_END>
    
    <EXPLANATION_START>
    (brief diagnostic rationale based on findings)
    <EXPLANATION_END>
    
    <ADDITIONAL_ANALYSIS_START>
    (specific insights that would be helpful for treatment planning)
    <ADDITIONAL_ANALYSIS_END>
    
    <DIAGNOSTIC_IMPRESSIONS_START>
    (summarize findings across domains using specific examples and clear explanations)
    <DIAGNOSTIC_IMPRESSIONS_END>
    
    <ERROR_EXAMPLES_START>
    (Copy all the specific quote examples here again, organized by error type or skill domain)
    <ERROR_EXAMPLES_END>
    
    MOST IMPORTANT: 
    1. Use EXACTLY the section markers provided (like <SPEECH_FACTORS_START>) to make parsing reliable
    2. For EVERY factor and domain you analyze, you MUST provide direct quotes from the transcript as evidence
    3. Be very specific and cite the exact text
    4. Do not omit any of the required sections
    """
    
    # Prepare prompt for Claude with the user's role context
    role_context = """
    You are a speech pathologist, a healthcare professional who specializes in evaluating, diagnosing, and treating communication disorders, including speech, language, cognitive-communication, voice, swallowing, and fluency disorders. Your role is to help patients improve their speech and communication skills through various therapeutic techniques and exercises.

    You are working with a student with speech impediments.

    The most important thing is that you stay kind to the child. Be constructive and helpful rather than critical.
    """
    
    prompt = f"""
    {role_context}
    
    You are analyzing a transcript for a patient who is {age} years old and {gender}.
    
    TRANSCRIPT:
    {transcript}
    
    {cheat_sheet}
    
    {instructions}
    
    Remember to be precise but compassionate in your analysis. Use direct quotes from the transcript for every factor and domain you analyze.
    """
    
    # Call the appropriate API or fallback to demo mode
    if bedrock_client:
        response = call_bedrock(prompt)
    else:
        response = generate_demo_response(prompt)
    
    # Parse the response
    results = parse_casl_response(response)
    
    # Create visualizations
    plot_image = create_casl_plots(results['speech_factors'], results['casl_data'])
    radar_image = create_casl_radar_chart(results['speech_factors'])
    
    return results, plot_image, radar_image, response

def generate_report(patient_info, analysis_results, report_type="formal"):
    """Generate a professional report based on analysis results"""
    
    patient_name = patient_info.get("name", "")
    record_id = patient_info.get("record_id", "")
    age = patient_info.get("age", "")
    gender = patient_info.get("gender", "")
    assessment_date = patient_info.get("assessment_date", datetime.now().strftime('%m/%d/%Y'))
    clinician = patient_info.get("clinician", "")
    
    prompt = f"""
    You are a professional Speech-Language Pathologist creating a {report_type} report based on an assessment.
    
    PATIENT INFORMATION:
    Name: {patient_name}
    Record ID: {record_id}
    Age: {age}
    Gender: {gender}
    Assessment Date: {assessment_date}
    Clinician: {clinician}
    
    ASSESSMENT RESULTS:
    {analysis_results}
    
    Please create a professional {report_type} report that includes:
    1. Patient information and assessment details
    2. Summary of findings (strengths and areas of concern)
    3. Detailed analysis of language domains
    4. Specific recommendations for therapy
    5. Recommendation for frequency and duration of services
    
    Use clear, professional language appropriate for {'educational professionals' if report_type == 'formal' else 'parents and caregivers'}.
    Format the report with proper headings and sections.
    """
    
    # Call the API or use demo mode
    if bedrock_client:
        report = call_bedrock(prompt, max_tokens=6000)
    else:
        # For demo, create a simulated report
        if report_type == 'formal':
            report = f"""
            # FORMAL LANGUAGE ASSESSMENT REPORT
            
            **Date of Assessment:** {assessment_date}
            **Clinician:** {clinician}
            
            ## PATIENT INFORMATION
            **Name:** {patient_name}
            **Record ID:** {record_id}
            **Age:** {age}
            **Gender:** {gender}
            
            ## ASSESSMENT SUMMARY
            
            The patient was assessed using the Comprehensive Assessment of Spoken Language, Second Edition (CASL-2) to evaluate language skills across multiple domains. The assessment involved language sample analysis and standardized testing.
            
            ## KEY FINDINGS
            
            **Areas of Strength:**
            - Ability to maintain conversational topics 
            - Good vocabulary for everyday topics
            - Strong nonverbal communication skills
            
            **Areas of Challenge:**
            - Word-finding difficulties during conversation
            - Grammatical errors in complex sentences
            - Difficulty with abstract language concepts
            
            ## DETAILED ANALYSIS
            
            **Lexical/Semantic Skills:** Standard Score 91 (27th percentile) - Low Average Range
            The student demonstrates adequate vocabulary but struggles with retrieving specific words during conversation. Word-finding pauses were noted throughout the language sample.
            
            **Syntactic Skills:** Standard Score 85 (16th percentile) - Low Average Range
            The student shows difficulty with complex grammatical structures, particularly verb tense consistency and complex sentence formation.
            
            **Supralinguistic Skills:** Standard Score 83 (13th percentile) - Below Average Range
            The student struggles with understanding figurative language, making inferences, and comprehending abstract concepts.
            
            ## RECOMMENDATIONS
            
            1. Speech-Language Therapy focused on:
               - Word-finding strategies using semantic feature analysis
               - Structured grammatical exercises to improve sentence complexity
               - Explicit instruction in figurative language comprehension
               - Narrative language development using visual supports
            
            2. Frequency of service: Twice weekly sessions of 30 minutes each for 12 weeks, followed by a reassessment to measure progress.
            
            3. Classroom accommodations including:
               - Extended time for verbal responses
               - Visual supports for complex instructions
               - Pre-teaching of vocabulary for academic units
            
            ## PROGNOSIS
            
            The prognosis for improvement is good with consistent therapeutic intervention and support. Regular reassessment is recommended to monitor progress.
            
            Respectfully submitted,
            
            {clinician}
            Speech-Language Pathologist
            """
        else:
            report = f"""
            # PARENT-FRIENDLY LANGUAGE ASSESSMENT SUMMARY
            
            **Date of Assessment:** {assessment_date}
            **Clinician:** {clinician}
            
            ## PATIENT INFORMATION
            **Name:** {patient_name}
            **Record ID:** {record_id}
            **Age:** {age}
            **Gender:** {gender}
            
            ## ASSESSMENT SUMMARY
            
            We completed a language assessment to better understand your child's communication strengths and challenges. This helps us create a plan to support their development.
            
            ## KEY FINDINGS
            
            **Areas of Strength:**
            - Ability to maintain conversational topics 
            - Good vocabulary for everyday topics
            - Strong nonverbal communication skills
            
            **Areas of Challenge:**
            - Word-finding difficulties during conversation
            - Grammatical errors in complex sentences
            - Difficulty with abstract language concepts
            
            ## DETAILED ANALYSIS
            
            **Lexical/Semantic Skills:** Standard Score 91 (27th percentile) - Low Average Range
            The student demonstrates adequate vocabulary but struggles with retrieving specific words during conversation. Word-finding pauses were noted throughout the language sample.
            
            **Syntactic Skills:** Standard Score 85 (16th percentile) - Low Average Range
            The student shows difficulty with complex grammatical structures, particularly verb tense consistency and complex sentence formation.
            
            **Supralinguistic Skills:** Standard Score 83 (13th percentile) - Below Average Range
            The student struggles with understanding figurative language, making inferences, and comprehending abstract concepts.
            
            ## RECOMMENDATIONS
            
            We recommend:
               - Word-finding strategies using semantic feature analysis
               - Structured grammatical exercises to improve sentence complexity
               - Explicit instruction in figurative language comprehension
               - Narrative language development using visual supports
            
            2. We recommend therapy twice a week for 30 minutes. This consistency will help your child make better progress.
            
            3. In school, your child may benefit from:
               - Extended time for verbal responses
               - Visual supports for complex instructions
               - Pre-teaching of vocabulary for academic units
            
            ## PROGNOSIS
            
            With regular therapy and support at home, we expect your child to make good progress in these areas.
            
            Please reach out with any questions!
            
            {clinician}
            Speech-Language Pathologist
            """
    
    return report

def transcribe_audio(audio_path, patient_age):
    """Transcribe an audio recording using CHAT format"""
    # In a real implementation, this would use a speech-to-text service
    # For demo purposes, we'll return a simulated transcription
    
    if bedrock_client:
        # In a real implementation, you would process the audio file and send it to a transcription service
        # Here we just simulate the result
        transcription = generate_demo_transcription(audio_path)
    else:
        transcription = generate_demo_transcription(audio_path)
    
    return transcription

def answer_slp_question(question):
    """Answer a question about SLP practice or CASL assessment"""
    
    prompt = f"""
    You are an experienced Speech-Language Pathologist answering a question from a colleague.
    
    QUESTION:
    {question}
    
    Please provide a clear, evidence-based answer focused specifically on the question asked.
    Reference best practices and current research where appropriate.
    Keep your answer concise but comprehensive.
    """
    
    if bedrock_client:
        answer = call_bedrock(prompt)
    else:
        answer = generate_demo_qa_response(question)
    
    return answer

# ===============================
# Gradio Interface
# ===============================

def create_interface():
    """Create the main Gradio interface"""
    
    # Use a simple theme with default colors
    custom_theme = gr.themes.Soft(
        font=[gr.themes.GoogleFont("Inter"), "system-ui", "sans-serif"]
    )
    
    with gr.Blocks(theme=custom_theme, css="""
        .header {
            text-align: center;
            margin-bottom: 20px;
        }
        .header img {
            max-height: 100px;
            margin-bottom: 10px;
        }
        .container {
            border-radius: 10px;
            padding: 10px;
            margin-bottom: 20px;
        }
        .patient-info {
            background-color: #e3f2fd;
        }
        .speech-sample {
            background-color: #f0f8ff;
        }
        .results-container {
            background-color: #f9f9f9; 
        }
        .viz-container {
            display: flex;
            justify-content: center;
            margin-bottom: 20px;
        }
        .footer {
            text-align: center;
            margin-top: 30px;
            padding: 10px;
            font-size: 0.8em;
            color: #78909C;
        }
        .info-box {
            background-color: #e8f5e9;
            border-left: 4px solid #4CAF50;
            padding: 10px 15px;
            margin-bottom: 15px;
            border-radius: 4px;
        }
        .warning-box {
            background-color: #fff8e1;
            border-left: 4px solid #FFC107;
            padding: 10px 15px;
            border-radius: 4px;
        }
        .markdown-text h3 {
            color: #2C7FB8;
            border-bottom: 1px solid #eaeaea;
            padding-bottom: 5px;
        }
        .evidence-table {
            border-collapse: collapse;
            width: 100%;
        }
        .evidence-table th, .evidence-table td {
            border: 1px solid #ddd;
            padding: 8px;
            text-align: left;
        }
        .evidence-table th {
            background-color: #f5f7fa;
            color: #333;
        }
        .evidence-table tr:nth-child(even) {
            background-color: #f9f9f9;
        }
        .tab-content {
            padding: 15px;
            background-color: white;
            border-radius: 0 0 8px 8px;
            box-shadow: 0 2px 5px rgba(0,0,0,0.05);
        }
    """) as app:
        # Create header with logo
        gr.HTML(
            """
            <div class="header">
                <h1>SLP Analysis Tool</h1>
                <p>A comprehensive assessment tool for Speech-Language Pathologists</p>
            </div>
            """
        )
        
        # Main tabs
        with gr.Tabs() as main_tabs:
            # ===============================
            # CASL Analysis Tab
            # ===============================
            with gr.TabItem("CASL Analysis", id=0):
                with gr.Row():
                    # Left column - Input section
                    with gr.Column(scale=1):
                        # Patient information panel
                        with gr.Group(elem_classes="container patient-info"):
                            gr.Markdown("### Patient Information")
                            
                            with gr.Row():
                                patient_name = gr.Textbox(label="Patient Name", placeholder="Enter patient name")
                                record_id = gr.Textbox(label="Record ID", placeholder="Enter record ID")
                            
                            with gr.Row():
                                age = gr.Number(label="Age", value=8, minimum=1, maximum=120)
                                gender = gr.Radio(["male", "female", "other"], label="Gender", value="male")
                            
                            with gr.Row():
                                assessment_date = gr.Textbox(
                                    label="Assessment Date", 
                                    placeholder="MM/DD/YYYY", 
                                    value=datetime.now().strftime('%m/%d/%Y')
                                )
                                clinician_name = gr.Textbox(
                                    label="Clinician", 
                                    placeholder="Enter clinician name"
                                )
                        
                        # Speech sample panel
                        with gr.Group(elem_classes="container speech-sample"):
                            gr.Markdown("### Speech Sample")
                            
                            # Sample button
                            sample_btn = gr.Button("Load Sample Transcript", size="sm")
                            
                            # Transcript input
                            transcript = gr.Textbox(
                                label="Transcript", 
                                placeholder="Paste the speech transcript here...",
                                lines=10
                            )
                            
                            # Add info about transcript format
                            gr.Markdown(
                                """
                                <div class="info-box">
                                    <strong>Transcript Format:</strong> Use CHAT format with *PAR: for patient lines. 
                                    Mark word-finding with &-um, paraphasias with [*], and provide intended words with [: word].
                                </div>
                                """,
                                elem_classes="markdown-text"
                            )
                            
                            # File upload
                            file_upload = gr.File(
                                label="Or upload a transcript file", 
                                file_types=["text", "txt", "pdf", "rtf"]
                            )
                            
                            # Analysis button
                            analyze_btn = gr.Button("Analyze Speech Sample", variant="primary", size="lg")
                    
                    # Right column - Results section
                    with gr.Column(scale=1):
                        with gr.Group(elem_classes="container results-container"):
                            with gr.Tabs() as results_tabs:
                                # Summary tab
                                with gr.TabItem("Summary", id=0, elem_classes="tab-content"):
                                    with gr.Group():
                                        gr.Markdown("### Key Findings", elem_classes="markdown-text")
                                        speech_factors_md = gr.Markdown(elem_classes="markdown-text")
                                        
                                    with gr.Accordion("CASL Assessment Results", open=True):
                                        casl_results_md = gr.Markdown(elem_classes="markdown-text")
                                    
                                    with gr.Accordion("Detailed Error Examples", open=True):
                                        specific_errors_md = gr.Markdown(elem_classes="markdown-text")
                                
                                # Treatment tab
                                with gr.TabItem("Treatment Plan", id=1, elem_classes="tab-content"):
                                    gr.Markdown("### Recommended Treatment Approaches", elem_classes="markdown-text")
                                    treatment_md = gr.Markdown(elem_classes="treatment-panel")
                                    
                                    gr.Markdown("### Clinical Rationale", elem_classes="markdown-text")
                                    explanation_md = gr.Markdown(elem_classes="panel")
                                    
                                    with gr.Accordion("Supporting Evidence", open=False):
                                        gr.Markdown("""
                                        <table class="evidence-table">
                                          <tr>
                                            <th>Factor</th>
                                            <th>Evidence-based Approaches</th>
                                            <th>References</th>
                                          </tr>
                                          <tr>
                                            <td>Word Retrieval</td>
                                            <td>Semantic feature analysis, phonological cueing, word generation tasks</td>
                                            <td>Boyle, 2010; Kiran & Thompson, 2003</td>
                                          </tr>
                                          <tr>
                                            <td>Grammatical Errors</td>
                                            <td>Treatment of Underlying Forms (TUF), Morphosyntactic therapy</td>
                                            <td>Thompson et al., 2003; Ebbels, 2014</td>
                                          </tr>
                                          <tr>
                                            <td>Fluency/Prosody</td>
                                            <td>Rate control, rhythmic cueing, contrastive stress exercises</td>
                                            <td>Ballard et al., 2010; Tamplin & Baker, 2017</td>
                                          </tr>
                                        </table>
                                        """, elem_classes="markdown-text")
                                
                                # Full report tab
                                with gr.TabItem("Full Report", id=2, elem_classes="tab-content"):
                                    full_analysis = gr.Markdown()
                                    
                                    # Add PDF export option
                                    export_btn = gr.Button("Export Report as PDF", variant="secondary")
                                    export_status = gr.Markdown("")
                                
                                # Raw LLM Output tab
                                with gr.TabItem("Raw LLM Output", id=3, elem_classes="tab-content"):
                                    gr.Markdown("### Complete Model Output", elem_classes="markdown-text")
                                    gr.Markdown("This tab shows the unprocessed output from the AI model for debugging purposes.")
                                    raw_llm_output = gr.Textbox(
                                        label="Raw AI Output",
                                        lines=20,
                                        interactive=False
                                    )
            
            # ===============================
            # Patient Records Tab
            # ===============================
            with gr.TabItem("Patient Records", id=1):
                with gr.Row():
                    with gr.Column(scale=1):
                        gr.Markdown("### Patient Records")
                        
                        # Records table
                        patient_records_table = gr.Dataframe(
                            headers=["ID", "Name", "Record ID", "Age", "Gender", "Assessment Date", "Clinician"],
                            datatype=["str", "str", "str", "str", "str", "str", "str"],
                            label="Saved Patients",
                            interactive=False
                        )
                        
                        refresh_records_btn = gr.Button("Refresh Records", size="sm")
                        records_status = gr.Markdown("")
                        
                        # Record selection
                        selected_record_id = gr.Textbox(label="Selected Record ID", visible=False)
                        load_record_btn = gr.Button("Load Selected Record", variant="primary")
                    
                    with gr.Column(scale=1):
                        # Record details
                        record_details = gr.Markdown(label="Record Details")
                
                # Event handlers for records
                def refresh_patient_records():
                    """Refresh the patient records table"""
                    records = get_all_patient_records()
                    data = []
                    for r in records:
                        data.append([
                            r["id"], r["name"], r["record_id"], 
                            r["age"], r["gender"], r["assessment_date"], r["clinician"]
                        ])
                    df = pd.DataFrame(data)
                    status_msg = f"Found {len(data)} patient records."
                    return df, status_msg
                
                refresh_records_btn.click(
                    refresh_patient_records,
                    outputs=[patient_records_table, records_status]
                )
                
                # Automatically load records when tab is selected
                main_tabs.select(
                    lambda tab_id: refresh_patient_records() if tab_id == 1 else (pd.DataFrame(), ""),
                    inputs=[main_tabs],
                    outputs=[patient_records_table, records_status]
                )
                
                # Load record when a row is selected
                def handle_record_selection(evt: gr.SelectData, records):
                    if records is None or len(records) == 0:
                        return "", "No record selected."
                    
                    selected_row = evt.index[0]
                    if selected_row < len(records):
                        record_id = records.iloc[selected_row, 0]
                        
                        # Load the record to show details
                        record_data = load_patient_record(record_id)
                        if record_data:
                            patient_info = record_data.get("patient_info", {})
                            
                            # Format record details as markdown
                            details = f"""
                            ## Selected Patient Record
                            
                            **Name:** {patient_info.get('name', 'N/A')}  
                            **Record ID:** {patient_info.get('record_id', 'N/A')}  
                            **Age:** {patient_info.get('age', 'N/A')}  
                            **Gender:** {patient_info.get('gender', 'N/A')}  
                            **Assessment Date:** {patient_info.get('assessment_date', 'N/A')}  
                            **Clinician:** {patient_info.get('clinician', 'N/A')}  
                            **Analyzed:** {record_data.get('timestamp', 'Unknown')}
                            
                            ### Preview
                            
                            This record contains:
                            - Speech transcript analysis
                            - CASL assessment results
                            - Treatment recommendations
                            
                            Click "Load Selected Record" to view the full analysis.
                            """
                            
                            return record_id, details
                        
                        return record_id, f"Selected record: {record_id}"
                    
                    return "", "Invalid selection."
                
                patient_records_table.select(
                    handle_record_selection,
                    inputs=[patient_records_table],
                    outputs=[selected_record_id, record_details]
                )
                
                # Load record into analysis tab
                def load_patient_record_to_analysis(record_id):
                    if not record_id:
                        return gr.update(selected=1), {}, "", "", "", "", "", ""
                    
                    record_data = load_patient_record(record_id)
                    if not record_data:
                        return gr.update(selected=1), "", "", "", "male", "", "", ""
                    
                    # Extract data
                    patient_info = record_data.get("patient_info", {})
                    transcript_text = record_data.get("transcript", "")
                    analysis_results = record_data.get("analysis_results", {})
                    
                    # Create status message for the record loading
                    status_msg = f"✅ Record loaded successfully: {patient_info.get('name', 'Unknown')} ({record_id})"
                    
                    # Now we should also load the analysis results
                    # In a future version, we would need to update all analysis outputs here as well
                    
                    return (
                        gr.update(selected=0),  # Switch to analysis tab
                        patient_info.get("name", ""),
                        patient_info.get("record_id", ""),
                        patient_info.get("age", ""),
                        patient_info.get("gender", "male"),
                        patient_info.get("assessment_date", ""),
                        patient_info.get("clinician", ""),
                        transcript_text,
                        status_msg
                    )
                
                load_record_btn.click(
                    load_patient_record_to_analysis,
                    inputs=[selected_record_id],
                    outputs=[
                        main_tabs,
                        patient_name, record_id, age, gender, 
                        assessment_date, clinician_name, transcript,
                        records_status
                    ]
                )
            
            # ===============================
            # Report Generator Tab
            # ===============================
            with gr.TabItem("Report Generator", id=2):
                with gr.Row():
                    with gr.Column(scale=1):
                        gr.Markdown("### Generate Professional Reports")
                        
                        # Patient info
                        with gr.Group(elem_classes="container patient-info"):
                            gr.Markdown("#### Patient Information")
                            report_patient_name = gr.Textbox(label="Patient Name", placeholder="Enter patient name")
                            report_record_id = gr.Textbox(label="Record ID", placeholder="Enter record ID")
                            report_age = gr.Number(label="Age", value=8, minimum=1, maximum=120)
                            report_gender = gr.Radio(["male", "female", "other"], label="Gender", value="male")
                            report_date = gr.Textbox(
                                label="Assessment Date", 
                                placeholder="MM/DD/YYYY", 
                                value=datetime.now().strftime('%m/%d/%Y')
                            )
                            report_clinician = gr.Textbox(label="Clinician", placeholder="Enter clinician name")
                        
                        with gr.Group():
                            gr.Markdown("#### Assessment Results")
                            report_results = gr.Textbox(
                                label="Paste assessment results or notes here", 
                                placeholder="Include key findings, test scores, and observations...",
                                lines=10
                            )
                            
                            report_type = gr.Radio(
                                ["Formal (for professionals)", "Parent-friendly"], 
                                label="Report Type",
                                value="Formal (for professionals)"
                            )
                            
                            generate_report_btn = gr.Button("Generate Report", variant="primary")
                    
                    with gr.Column(scale=1):
                        report_output = gr.Markdown()
                        report_download_btn = gr.Button("Download Report as PDF", variant="secondary")
                        report_download_status = gr.Markdown("")
            
            # ===============================
            # Transcription Tool Tab
            # ===============================
            with gr.TabItem("Transcription Tool", id=3):
                with gr.Row():
                    with gr.Column(scale=1):
                        gr.Markdown("### Audio Transcription Tool")
                        gr.Markdown("Upload an audio recording to automatically transcribe it in CHAT format.")
                        
                        audio_input = gr.Audio(type="filepath", label="Upload Audio Recording")
                        
                        with gr.Row():
                            transcription_age = gr.Number(label="Patient Age", value=8, minimum=1, maximum=120)
                            transcribe_btn = gr.Button("Transcribe Audio", variant="primary")
                    
                    with gr.Column(scale=1):
                        transcription_output = gr.Textbox(
                            label="Transcription Result", 
                            placeholder="Transcription will appear here...",
                            lines=12
                        )
                        
                        with gr.Row():
                            copy_to_analysis_btn = gr.Button("Use for Analysis", variant="secondary")
                            edit_transcription_btn = gr.Button("Edit Transcription", variant="secondary")
            
            # ===============================
            # SLP Assistant Tab
            # ===============================
            with gr.TabItem("SLP Assistant", id=4):
                with gr.Row():
                    with gr.Column(scale=1):
                        gr.Markdown("### SLP Knowledge Assistant")
                        gr.Markdown("Ask questions about CASL assessment, therapy techniques, or SLP best practices.")
                        
                        question_input = gr.Textbox(
                            label="Your Question", 
                            placeholder="e.g., What activities help improve word-finding skills?",
                            lines=3
                        )
                        
                        ask_question_btn = gr.Button("Ask Question", variant="primary")
                        
                        # Quick question buttons
                        gr.Markdown("#### Common Questions")
                        with gr.Row():
                            q1_btn = gr.Button("What is CASL?")
                            q2_btn = gr.Button("How do I interpret scores?")
                        
                        with gr.Row():
                            q3_btn = gr.Button("Activities for word finding")
                            q4_btn = gr.Button("When to reassess")
                    
                    with gr.Column(scale=1):
                        answer_output = gr.Markdown()
                        
                        with gr.Accordion("References", open=False):
                            gr.Markdown("""
                            - American Speech-Language-Hearing Association (ASHA)
                            - Comprehensive Assessment of Spoken Language (CASL-2) Manual
                            - Evidence-Based Practice in Speech-Language Pathology
                            - Current research in pediatric language intervention
                            """)
        
        # ===============================
        # Event Handlers
        # ===============================
        
        # Load sample transcript button
        def load_sample():
            return SAMPLE_TRANSCRIPT
        
        sample_btn.click(load_sample, outputs=[transcript])
        
        # File upload handler
        file_upload.upload(process_upload, file_upload, transcript)
        
        # Analysis button handler
        def on_analyze_click(transcript_text, age_val, gender_val, patient_name_val, record_id_val, clinician_val, assessment_date_val):
            if not transcript_text or len(transcript_text.strip()) < 50:
                return (
                    "Error: Please provide a longer transcript for analysis.",
                    "The transcript is too short for meaningful analysis.",
                    "Please provide a speech sample with at least 50 characters.",
                    "Error: Insufficient data",
                    "Please provide a speech sample with at least 50 characters.",
                    "",
                    "",
                    ""
                )
            
            try:
                # Get the raw analysis response
                results, _, _, full_text = analyze_transcript(transcript_text, age_val, gender_val)
                
                # Extract speech factors section using section markers
                speech_factors_section = ""
                factors_pattern = re.compile(r"<SPEECH_FACTORS_START>(.*?)<SPEECH_FACTORS_END>", re.DOTALL)
                factors_match = factors_pattern.search(full_text)
                
                if factors_match:
                    speech_factors_section = factors_match.group(1).strip()
                else:
                    # Fallback to old pattern if markers aren't found
                    old_factors_pattern = re.compile(r"(Difficulty producing fluent.*?)(?:Evaluation of CASL Skills|<CASL_SKILLS_START>)", re.DOTALL)
                    old_factors_match = old_factors_pattern.search(full_text)
                    if old_factors_match:
                        speech_factors_section = old_factors_match.group(1).strip()
                    else:
                        speech_factors_section = "Error extracting speech factors from analysis."
                
                # Extract CASL skills section
                casl_section = ""
                casl_pattern = re.compile(r"<CASL_SKILLS_START>(.*?)<CASL_SKILLS_END>", re.DOTALL)
                casl_match = casl_pattern.search(full_text)
                
                if casl_match:
                    casl_section = casl_match.group(1).strip()
                else:
                    # Fallback pattern
                    old_casl_pattern = re.compile(r"(?:Evaluation of CASL Skills:|Lexical/Semantic Skills:)(.*?)(?:Other analysis/Best plans of action:|<TREATMENT_RECOMMENDATIONS_START>)", re.DOTALL)
                    old_casl_match = old_casl_pattern.search(full_text)
                    if old_casl_match:
                        casl_section = old_casl_match.group(1).strip()
                        # Add a header if it's missing
                        if not casl_section.startswith("Lexical"):
                            casl_section = "Evaluation of CASL Skills:\n\n" + casl_section
                    else:
                        casl_section = "Error extracting CASL skills from analysis."
                
                # Extract treatment recommendations
                treatment_text = ""
                treatment_pattern = re.compile(r"<TREATMENT_RECOMMENDATIONS_START>(.*?)<TREATMENT_RECOMMENDATIONS_END>", re.DOTALL)
                treatment_match = treatment_pattern.search(full_text)
                
                if treatment_match:
                    treatment_text = "### Treatment Recommendations\n\n" + treatment_match.group(1).strip()
                else:
                    # Fallback pattern
                    old_treatment_pattern = re.compile(r"(?:Other analysis/Best plans of action:)(.*?)(?:Explanation:|<EXPLANATION_START>)", re.DOTALL)
                    old_treatment_match = old_treatment_pattern.search(full_text)
                    if old_treatment_match:
                        treatment_text = "### Treatment Recommendations\n\n" + old_treatment_match.group(1).strip()
                    elif 'treatment_suggestions' in results:
                        treatment_text = "### Treatment Recommendations\n\n"
                        for suggestion in results['treatment_suggestions']:
                            treatment_text += f"- {suggestion}\n"
                
                # Extract explanation section
                explanation_text = "### Clinical Rationale\n\n"
                explanation_pattern = re.compile(r"<EXPLANATION_START>(.*?)<EXPLANATION_END>", re.DOTALL)
                explanation_match = explanation_pattern.search(full_text)
                
                if explanation_match:
                    explanation_text += explanation_match.group(1).strip()
                else:
                    # Fallback pattern
                    old_explanation_pattern = re.compile(r"(?:Explanation:)(.*?)(?:Additional Analysis:|<ADDITIONAL_ANALYSIS_START>)", re.DOTALL)
                    old_explanation_match = old_explanation_pattern.search(full_text)
                    if old_explanation_match:
                        explanation_text += old_explanation_match.group(1).strip()
                    else:
                        explanation_text += results.get('explanation', "No explanation provided.")
                
                # Extract additional analysis
                additional_analysis = ""
                additional_pattern = re.compile(r"<ADDITIONAL_ANALYSIS_START>(.*?)<ADDITIONAL_ANALYSIS_END>", re.DOTALL)
                additional_match = additional_pattern.search(full_text)
                
                if additional_match:
                    additional_analysis = additional_match.group(1).strip()
                    explanation_text += "\n\n### Additional Analysis\n\n" + additional_analysis
                else:
                    # Fallback pattern
                    old_additional_pattern = re.compile(r"(?:Additional Analysis:)(.*?)(?:Diagnostic Impressions:|<DIAGNOSTIC_IMPRESSIONS_START>)", re.DOTALL)
                    old_additional_match = old_additional_pattern.search(full_text)
                    if old_additional_match:
                        explanation_text += "\n\n### Additional Analysis\n\n" + old_additional_match.group(1).strip()
                    elif 'additional_analysis' in results:
                        explanation_text += "\n\n### Additional Analysis\n\n" + results.get('additional_analysis', "")
                
                # Extract diagnostic impressions
                diagnostic_impressions = ""
                diagnostic_pattern = re.compile(r"<DIAGNOSTIC_IMPRESSIONS_START>(.*?)<DIAGNOSTIC_IMPRESSIONS_END>", re.DOTALL)
                diagnostic_match = diagnostic_pattern.search(full_text)
                
                if diagnostic_match:
                    diagnostic_impressions = diagnostic_match.group(1).strip()
                    # Add to the explanation section
                    explanation_text += "\n\n### Diagnostic Impressions\n\n" + diagnostic_impressions
                
                # Extract specific error examples
                specific_errors_text = "## Detailed Error Examples\n\n"
                
                # First try the dedicated section
                errors_pattern = re.compile(r"<ERROR_EXAMPLES_START>(.*?)<ERROR_EXAMPLES_END>", re.DOTALL)
                errors_match = errors_pattern.search(full_text)
                
                if errors_match:
                    specific_errors_text += errors_match.group(1).strip()
                else:
                    # Fallback to extracting examples from the text
                    example_sections = re.findall(r"Examples:\s*\n((?:- \".*\"\s*\n)+)", full_text)
                    for section in example_sections:
                        specific_errors_text += section + "\n"
                    
                    if not example_sections:
                        specific_errors_text += "No specific error examples were found in the analysis."
                
                # Save the record to storage
                patient_info = {
                    "name": patient_name_val,
                    "record_id": record_id_val,
                    "age": age_val,
                    "gender": gender_val,
                    "assessment_date": assessment_date_val,
                    "clinician": clinician_val
                }
                
                saved_id = save_patient_record(patient_info, results, transcript_text)
                
                save_message = ""
                if saved_id:
                    save_message = f"""
                    ✅ Patient record saved successfully.
                    
                    **System ID:** {saved_id}
                    **Patient:** {patient_name_val or "Unnamed"} 
                    **Record ID:** {record_id_val or "Not provided"}
                    
                    You can access this record later in the Patient Records tab.
                    """
                else:
                    save_message = "⚠️ Failed to save patient record. Please check data directory permissions."
                
                # Format to include patient metadata in the full report
                patient_info_text = ""
                if patient_name_val:
                    patient_info_text += f"**Patient:** {patient_name_val}\n"
                if record_id_val:
                    patient_info_text += f"**Record ID:** {record_id_val}\n"
                if age_val:
                    patient_info_text += f"**Age:** {age_val} years\n"
                if gender_val:
                    patient_info_text += f"**Gender:** {gender_val}\n"
                if assessment_date_val:
                    patient_info_text += f"**Assessment Date:** {assessment_date_val}\n"
                if clinician_val:
                    patient_info_text += f"**Clinician:** {clinician_val}\n"
                if saved_id:
                    patient_info_text += f"**System ID:** {saved_id}\n"
                
                if patient_info_text:
                    full_report = f"## Patient Information\n\n{patient_info_text}\n\n## Analysis Report\n\n{full_text}"
                else:
                    full_report = f"## Complete Analysis Report\n\n{full_text}"
                
                # Get the raw LLM response for debugging
                raw_output = full_text
                
                return (
                    speech_factors_section,
                    casl_section,
                    treatment_text,
                    explanation_text,
                    full_report,
                    save_message,
                    specific_errors_text,
                    raw_output
                )
            except Exception as e:
                logger.exception("Error during analysis")
                error_message = f"Error during analysis: {str(e)}"
                return (
                    f"Error: {str(e)}",
                    "Error: Analysis failed. Please check input data.",
                    "Error: Treatment analysis not available.",
                    "An error occurred while processing the transcript.",
                    f"Error details: {str(e)}",
                    "",
                    "",
                    f"Analysis failed with error: {error_message}\n\nPlease check your transcript format and try again."
                )
        
        analyze_btn.click(
            on_analyze_click,
            inputs=[
                transcript, age, gender, 
                patient_name, record_id, clinician_name, assessment_date
            ],
            outputs=[
                speech_factors_md,
                casl_results_md,
                treatment_md,
                explanation_md,
                full_analysis,
                export_status,
                specific_errors_md,
                raw_llm_output
            ]
        )
        
        # Export report functionality
        def export_pdf(report_text, patient_name="Patient", record_id=""):
            try:
                from reportlab.lib.pagesizes import letter
                from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer
                from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
                import tempfile
                import webbrowser
                import os
                
                # Generate a safe filename
                if patient_name and record_id:
                    safe_name = f"{patient_name.replace(' ', '_')}_{record_id}"
                elif patient_name:
                    safe_name = patient_name.replace(' ', '_')
                else:
                    safe_name = f"speech_analysis_{datetime.now().strftime('%Y%m%d%H%M%S')}"
                
                # Create a temporary file for the PDF
                temp_dir = tempfile.gettempdir()
                pdf_path = os.path.join(temp_dir, f"{safe_name}.pdf")
                
                # Create the PDF document
                doc = SimpleDocTemplate(pdf_path, pagesize=letter)
                styles = getSampleStyleSheet()
                
                # Create custom styles
                styles.add(ParagraphStyle(
                    name='Heading1',
                    parent=styles['Heading1'],
                    fontSize=16,
                    spaceAfter=12
                ))
                
                styles.add(ParagraphStyle(
                    name='Heading2',
                    parent=styles['Heading2'],
                    fontSize=14,
                    spaceAfter=10,
                    spaceBefore=10
                ))
                
                styles.add(ParagraphStyle(
                    name='BodyText',
                    parent=styles['BodyText'],
                    fontSize=12,
                    spaceAfter=8
                ))
                
                # Convert markdown to PDF elements
                # Very basic conversion - in a real app, use a proper markdown to PDF library
                story = []
                
                # Add title
                story.append(Paragraph("Speech Language Assessment Report", styles['Title']))
                story.append(Spacer(1, 12))
                
                # Process the markdown content line by line
                current_style = styles['BodyText']
                
                for line in report_text.split('\n'):
                    # Skip empty lines
                    if not line.strip():
                        story.append(Spacer(1, 6))
                        continue
                        
                    # Check for headings
                    if line.startswith('# '):
                        story.append(Paragraph(line[2:], styles['Heading1']))
                    elif line.startswith('## '):
                        story.append(Paragraph(line[3:], styles['Heading2']))
                    elif line.startswith('- '):
                        # Bullet points
                        story.append(Paragraph('• ' + line[2:], styles['BodyText']))
                    elif line.startswith('**') and line.endswith('**'):
                        # Bold text - assuming it's a short line like a heading
                        text = line.replace('**', '')
                        story.append(Paragraph(f"<b>{text}</b>", styles['BodyText']))
                    else:
                        # Regular text
                        story.append(Paragraph(line, styles['BodyText']))
                
                # Build the PDF
                doc.build(story)
                
                # Open the PDF (in a real web app, you'd provide a download link)
                # This will work in a desktop environment
                return f"Report saved as PDF: {pdf_path}"
            
            except Exception as e:
                logger.exception("Error creating PDF")
                return f"Error creating PDF: {str(e)}\n\nIn a production environment, we would generate a proper PDF for download."
        
        # Simplified simulation for HuggingFace Spaces environment
        def export_pdf_simulation(report_text):
            return "Report export initiated. In a production environment, a PDF would be generated and downloaded."
        
        # Use the actual function in a desktop environment, simulation in web environment
        if os.getenv("SPACE_ID"):  # Check if running on HuggingFace Spaces
            export_btn.click(
                lambda x: export_pdf_simulation(x),
                inputs=[full_analysis],
                outputs=[export_status]
            )
            report_download_btn.click(
                lambda x: export_pdf_simulation(x),
                inputs=[report_output],
                outputs=[report_download_status]
            )
        else:
            # Running locally, use actual PDF generation
            export_btn.click(
                lambda x, y, z: export_pdf(x, y, z),
                inputs=[full_analysis, patient_name, record_id],
                outputs=[export_status]
            )
            report_download_btn.click(
                lambda x, y, z: export_pdf(x, y, z),
                inputs=[report_output, report_patient_name, report_record_id],
                outputs=[report_download_status]
            )
        
        # Report generator button
        def on_generate_report(name, record_id, age, gender, date, clinician, results, report_type):
            patient_info = {
                "name": name,
                "record_id": record_id,
                "age": age,
                "gender": gender,
                "assessment_date": date,
                "clinician": clinician
            }
            
            report_type_val = "formal" if "Formal" in report_type else "parent-friendly"
            
            try:
                report = generate_report(patient_info, results, report_type_val)
                return report
            except Exception as e:
                logger.exception("Error generating report")
                return f"Error generating report: {str(e)}"
        
        generate_report_btn.click(
            on_generate_report,
            inputs=[
                report_patient_name, report_record_id, report_age,
                report_gender, report_date, report_clinician,
                report_results, report_type
            ],
            outputs=[report_output]
        )
        
        # Transcription button
        def on_transcribe_audio(audio_path, age):
            try:
                if not audio_path:
                    return "Please upload an audio file to transcribe."
                
                transcription = transcribe_audio(audio_path, age)
                return transcription
            except Exception as e:
                logger.exception("Error transcribing audio")
                return f"Error transcribing audio: {str(e)}"
        
        transcribe_btn.click(
            on_transcribe_audio,
            inputs=[audio_input, transcription_age],
            outputs=[transcription_output]
        )
        
        # Copy transcription to analysis
        def copy_to_analysis(transcription):
            return transcription, gr.update(selected=0)  # Switches to the Analysis tab
        
        copy_to_analysis_btn.click(
            copy_to_analysis,
            inputs=[transcription_output],
            outputs=[transcript, main_tabs]
        )
        
        # SLP Assistant question handling
        def on_ask_question(question):
            try:
                answer = answer_slp_question(question)
                return answer
            except Exception as e:
                logger.exception("Error getting answer")
                return f"Error: {str(e)}"
        
        ask_question_btn.click(
            on_ask_question,
            inputs=[question_input],
            outputs=[answer_output]
        )
        
        # Quick question buttons
        q1_btn.click(lambda: "What is CASL?", outputs=[question_input])
        q2_btn.click(lambda: "How do I interpret CASL scores?", outputs=[question_input])
        q3_btn.click(lambda: "What activities help with word finding difficulties?", outputs=[question_input])
        q4_btn.click(lambda: "When should I reassess a patient?", outputs=[question_input])
        
    return app

# ===============================
# Main Application
# ===============================

# Create requirements.txt file for HuggingFace Spaces
def create_requirements_file():
    requirements = [
        "gradio>=4.0.0",
        "pandas",
        "matplotlib",
        "numpy",
        "Pillow",
        "PyPDF2",
        "boto3",
        "reportlab",
        "uuid"
    ]
    
    with open("requirements.txt", "w") as f:
        for req in requirements:
            f.write(f"{req}\n")

# Create and launch the interface
if __name__ == "__main__":
    # Create requirements.txt for HuggingFace Spaces
    create_requirements_file()
    
    # Check for AWS credentials
    if not AWS_ACCESS_KEY or not AWS_SECRET_KEY:
        print("NOTE: AWS credentials not found. The app will run in demo mode with simulated responses.")
        print("To enable full functionality, set AWS_ACCESS_KEY and AWS_SECRET_KEY environment variables.")
    
    # Launch the Gradio app
    app = create_interface()
    app.launch()