simple_app.py

import gradio as gr
import boto3
import json
import re
import logging
import os
import tempfile
import shutil
from datetime import datetime

# Try to import ReportLab (needed for PDF generation)
try:
    from reportlab.lib.pagesizes import letter
    from reportlab.lib import colors
    from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle
    from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
    REPORTLAB_AVAILABLE = True
except ImportError:
    logger.warning("ReportLab library not available - PDF export will be disabled")
    REPORTLAB_AVAILABLE = False

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

# AWS credentials for Bedrock API
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)}")

# Create data directories if they don't exist
DATA_DIR = os.environ.get("DATA_DIR", "patient_data")
DOWNLOADS_DIR = os.path.join(DATA_DIR, "downloads")

def ensure_data_dirs():
    """Ensure data directories exist"""
    try:
        os.makedirs(DATA_DIR, exist_ok=True)
        os.makedirs(DOWNLOADS_DIR, exist_ok=True)
        logger.info(f"Data directories created: {DATA_DIR}, {DOWNLOADS_DIR}")
    except Exception as e:
        logger.warning(f"Could not create data directories: {str(e)}")
        # Fallback to tmp directory on HF Spaces
        global DOWNLOADS_DIR
        DOWNLOADS_DIR = os.path.join(tempfile.gettempdir(), "casl_downloads")
        os.makedirs(DOWNLOADS_DIR, exist_ok=True)
        logger.info(f"Using fallback directory: {DOWNLOADS_DIR}")

# Initialize data directories
ensure_data_dirs()

# 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."""

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'):
        # For PDF, we would need PyPDF2 or similar
        return "PDF upload not supported in this simple version"
    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()

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. Using demo response instead."
    
    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
    
    return """<SPEECH_FACTORS_START>
Difficulty producing fluent speech: 8, 65
Examples:
- "today I would &-um like to talk about &-um a fun trip I took last &-um summer with my family"
- "we went to the &-um &-um beach [//] no to the mountains [//] I mean the beach actually"

Word retrieval issues: 6, 72
Examples:
- "what do you call those &-um &-um sprinkles! that's the word"
- "sometimes I forget [//] forgetted [: forgot] [*] what they call those things we built"

Grammatical errors: 4, 58
Examples:
- "after swimming we [//] I eat [: ate] [*] &-um ice cream"
- "sometimes I forget [//] forgetted [: forgot] [*] what they call those things we built"

Repetitions and revisions: 5, 62
Examples:
- "we [/] we stayed for &-um three no [//] four days"
- "we went to the &-um &-um beach [//] no to the mountains [//] I mean the beach actually"
<SPEECH_FACTORS_END>

<CASL_SKILLS_START>
Lexical/Semantic Skills: Standard Score (92), Percentile Rank (30%), Average Performance
Examples:
- "what do you call those &-um &-um sprinkles! that's the word"
- "we went to the &-um &-um beach [//] no to the mountains [//] I mean the beach actually"

Syntactic Skills: Standard Score (87), Percentile Rank (19%), Low Average Performance
Examples:
- "my brother he [//] he helped me dig a big hole"
- "after swimming we [//] I eat [: ate] [*] &-um ice cream with &-um chocolate things on top"

Supralinguistic Skills: Standard Score (90), Percentile Rank (25%), Average Performance
Examples:
- "sometimes I wonder [/] wonder where fishies [: fish] [*] go when it's cold"
- "maybe they have [/] have houses under the water"
<CASL_SKILLS_END>

<TREATMENT_RECOMMENDATIONS_START>
- Implement word-finding strategies with semantic cuing focused on everyday objects and activities, using the patient's beach experience as a context (e.g., "sprinkles," "castles")
- Practice structured narrative tasks with visual supports to reduce revisions and improve sequencing
- Use sentence formulation exercises focusing on verb tense consistency (addressing errors like "forgetted" and "eat" for "ate")
- Incorporate self-monitoring techniques to help identify and correct grammatical errors
- Work on increasing vocabulary specificity (e.g., "things on top" to "sprinkles")
<TREATMENT_RECOMMENDATIONS_END>

<EXPLANATION_START>
This child demonstrates moderate word-finding difficulties with compensatory strategies including fillers ("&-um") and repetitions. The frequent use of self-corrections shows good metalinguistic awareness, but the pauses and repairs impact conversational fluency. Syntactic errors primarily involve verb tense inconsistency. Overall, the pattern suggests a mild-to-moderate language disorder with stronger receptive than expressive skills.
<EXPLANATION_END>

<ADDITIONAL_ANALYSIS_START>
The child shows relative strengths in maintaining topic coherence and conveying a complete narrative structure despite the language challenges. The pattern of errors suggests that word-finding difficulties and processing speed are primary concerns rather than conceptual or cognitive issues. Semantic network activities that strengthen word associations would likely be beneficial, particularly when paired with visual supports.
<ADDITIONAL_ANALYSIS_END>

<DIAGNOSTIC_IMPRESSIONS_START>
Based on the language sample, this child presents with a profile consistent with a mild-to-moderate expressive language disorder. The most prominent features include:

1. Word-finding difficulties characterized by fillers, pauses, and self-corrections when attempting to retrieve specific vocabulary
2. Grammatical challenges primarily affecting verb tense consistency and morphological markers
3. Relatively intact narrative structure and topic maintenance

These findings suggest intervention should focus on word retrieval strategies, grammatical form practice, and continued support for narrative development, with an emphasis on fluency and self-monitoring.
<DIAGNOSTIC_IMPRESSIONS_END>

<ERROR_EXAMPLES_START>
Word-finding difficulties:
- "what do you call those &-um &-um sprinkles! that's the word"
- "we went to the &-um &-um beach [//] no to the mountains [//] I mean the beach actually"
- "there was lots of &-um &-um swimming and &-um sun"

Grammatical errors:
- "after swimming we [//] I eat [: ate] [*] &-um ice cream"
- "sometimes I forget [//] forgetted [: forgot] [*] what they call those things we built"
- "we saw [/] saw fishies [: fish] [*] swimming in the water"

Repetitions and revisions:
- "we [/] we stayed for &-um three no [//] four days"
- "I want to go back to the beach [/] beach next year"
- "sometimes I wonder [/] wonder where fishies [: fish] [*] go when it's cold"
<ERROR_EXAMPLES_END>"""

def parse_casl_response(response):
    """Parse the LLM response for CASL analysis into structured data"""
    # 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(response)
    
    if factors_match:
        speech_factors_section = 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(response)
    
    if casl_match:
        casl_section = casl_match.group(1).strip()
    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(response)
    
    if treatment_match:
        treatment_text = treatment_match.group(1).strip()
    else:
        treatment_text = "Error extracting treatment recommendations from analysis."
    
    # Extract explanation section
    explanation_text = ""
    explanation_pattern = re.compile(r"<EXPLANATION_START>(.*?)<EXPLANATION_END>", re.DOTALL)
    explanation_match = explanation_pattern.search(response)
    
    if explanation_match:
        explanation_text = explanation_match.group(1).strip()
    else:
        explanation_text = "Error extracting clinical explanation from analysis."
    
    # Extract additional analysis
    additional_analysis = ""
    additional_pattern = re.compile(r"<ADDITIONAL_ANALYSIS_START>(.*?)<ADDITIONAL_ANALYSIS_END>", re.DOTALL)
    additional_match = additional_pattern.search(response)
    
    if additional_match:
        additional_analysis = additional_match.group(1).strip()
    
    # Extract diagnostic impressions
    diagnostic_impressions = ""
    diagnostic_pattern = re.compile(r"<DIAGNOSTIC_IMPRESSIONS_START>(.*?)<DIAGNOSTIC_IMPRESSIONS_END>", re.DOTALL)
    diagnostic_match = diagnostic_pattern.search(response)
    
    if diagnostic_match:
        diagnostic_impressions = diagnostic_match.group(1).strip()
    
    # Extract specific error examples
    specific_errors_text = ""
    errors_pattern = re.compile(r"<ERROR_EXAMPLES_START>(.*?)<ERROR_EXAMPLES_END>", re.DOTALL)
    errors_match = errors_pattern.search(response)
    
    if errors_match:
        specific_errors_text = errors_match.group(1).strip()
    
    # Create full report text
    full_report = f"""
## Speech Factors Analysis

{speech_factors_section}

## CASL Skills Assessment

{casl_section}

## Treatment Recommendations

{treatment_text}

## Clinical Explanation

{explanation_text}
"""
    
    if additional_analysis:
        full_report += f"\n## Additional Analysis\n\n{additional_analysis}"
    
    if diagnostic_impressions:
        full_report += f"\n## Diagnostic Impressions\n\n{diagnostic_impressions}"
    
    if specific_errors_text:
        full_report += f"\n## Detailed Error Examples\n\n{specific_errors_text}"
    
    return {
        'speech_factors': speech_factors_section,
        'casl_data': casl_section,
        'treatment_suggestions': treatment_text,
        'explanation': explanation_text,
        'additional_analysis': additional_analysis,
        'diagnostic_impressions': diagnostic_impressions,
        'specific_errors': specific_errors_text,
        'full_report': full_report,
        'raw_response': response
    }

def analyze_transcript(transcript, age, gender):
    """Analyze a speech transcript using Claude"""
    # CASL-2 assessment cheat sheet
    cheat_sheet = """
    # Speech-Language Pathologist Analysis Cheat Sheet
    
    ## Types of Speech Patterns to Identify:
    
    1. Difficulty producing fluent, grammatical speech
       - Fillers (um, uh) and pauses
       - False starts and revisions
       - Incomplete sentences
    
    2. Word retrieval issues
       - Pauses before content words
       - Circumlocutions (talking around a word)
       - Word substitutions
    
    3. Grammatical errors
       - Verb tense inconsistencies
       - Subject-verb agreement errors
       - Morphological errors (plurals, possessives)
    
    4. Repetitions and revisions
       - Word or phrase repetitions [/]
       - Self-corrections [//]
       - Retracing
    
    5. Neologisms
       - Made-up words
       - Word blends
    
    6. Perseveration
       - Inappropriate repetition of ideas
       - Recurring themes
    
    7. Comprehension issues
       - Topic maintenance difficulties
       - Non-sequiturs
       - Inappropriate responses
    """
    
    # Instructions for the analysis
    instructions = """
    Analyze this speech transcript to identify specific patterns and provide a detailed CASL-2 (Comprehensive Assessment of Spoken Language) assessment.
    
    For each speech pattern you identify:
    1. Count the occurrences in the transcript
    2. Estimate a percentile (how typical/atypical this is for the age)
    3. Provide DIRECT QUOTES from the transcript as evidence
    
    Then assess the following CASL-2 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)
    
    return results

def export_pdf(results, patient_name="", record_id="", age="", gender="", assessment_date="", clinician=""):
    """Export analysis results to a PDF report"""
    # Check if ReportLab is available
    if not REPORTLAB_AVAILABLE:
        return "ERROR: PDF export is not available - ReportLab library is not installed. Please run 'pip install reportlab'."
    
    try:
        # Generate a safe filename
        if patient_name:
            safe_name = f"{patient_name.replace(' ', '_')}"
        else:
            safe_name = f"speech_analysis_{datetime.now().strftime('%Y%m%d%H%M%S')}"
        
        # Make sure the downloads directory exists
        try:
            os.makedirs(DOWNLOADS_DIR, exist_ok=True)
        except Exception as e:
            logger.warning(f"Could not access downloads directory: {str(e)}")
            # Fallback to temp directory
            global DOWNLOADS_DIR
            DOWNLOADS_DIR = os.path.join(tempfile.gettempdir(), "casl_downloads")
            os.makedirs(DOWNLOADS_DIR, exist_ok=True)
        
        # Create the PDF path in our downloads directory
        pdf_path = os.path.join(DOWNLOADS_DIR, f"{safe_name}.pdf")
        
        # Create the PDF document
        doc = SimpleDocTemplate(pdf_path, pagesize=letter)
        styles = getSampleStyleSheet()
        
        # Create enhanced custom styles
        styles.add(ParagraphStyle(
            name='Heading1',
            parent=styles['Heading1'],
            fontSize=16,
            spaceAfter=12,
            textColor=colors.navy
        ))
        
        styles.add(ParagraphStyle(
            name='Heading2',
            parent=styles['Heading2'],
            fontSize=14,
            spaceAfter=10,
            spaceBefore=10,
            textColor=colors.darkblue
        ))
        
        styles.add(ParagraphStyle(
            name='Heading3',
            parent=styles['Heading2'],
            fontSize=12,
            spaceAfter=8,
            spaceBefore=8,
            textColor=colors.darkblue
        ))
        
        styles.add(ParagraphStyle(
            name='BodyText',
            parent=styles['BodyText'],
            fontSize=11,
            spaceAfter=8,
            leading=14
        ))
        
        styles.add(ParagraphStyle(
            name='BulletPoint',
            parent=styles['BodyText'],
            fontSize=11,
            leftIndent=20,
            firstLineIndent=-15,
            spaceAfter=4,
            leading=14
        ))
        
        # Convert markdown to PDF elements
        story = []
        
        # Add title and date
        story.append(Paragraph("Speech Language Assessment Report", styles['Title']))
        story.append(Spacer(1, 12))
        
        # Add patient information table
        if patient_name or record_id or age or gender:
            # Prepare patient info data
            data = []
            if patient_name:
                data.append(["Patient Name:", patient_name])
            if record_id:
                data.append(["Record ID:", record_id])
            if age:
                data.append(["Age:", f"{age} years"])
            if gender:
                data.append(["Gender:", gender])
            if assessment_date:
                data.append(["Assessment Date:", assessment_date])
            if clinician:
                data.append(["Clinician:", clinician])
            
            if data:
                # Create a table with the data
                patient_table = Table(data, colWidths=[120, 350])
                patient_table.setStyle(TableStyle([
                    ('BACKGROUND', (0, 0), (0, -1), colors.lightgrey),
                    ('TEXTCOLOR', (0, 0), (0, -1), colors.darkblue),
                    ('ALIGN', (0, 0), (0, -1), 'RIGHT'),
                    ('ALIGN', (1, 0), (1, -1), 'LEFT'),
                    ('FONTNAME', (0, 0), (0, -1), 'Helvetica-Bold'),
                    ('BOTTOMPADDING', (0, 0), (-1, -1), 6),
                    ('TOPPADDING', (0, 0), (-1, -1), 6),
                    ('GRID', (0, 0), (-1, -1), 0.5, colors.lightgrey),
                ]))
                story.append(patient_table)
                story.append(Spacer(1, 12))
        
        # Add clinical analysis sections
        story.append(Paragraph("Speech Factors Analysis", styles['Heading1']))
        speech_factors_paragraphs = []
        for line in results['speech_factors'].split('\n'):
            line = line.strip()
            if not line:
                continue
            if line.startswith('- '):
                story.append(Paragraph(f"• {line[2:]}", styles['BulletPoint']))
            else:
                story.append(Paragraph(line, styles['BodyText']))
        story.append(Spacer(1, 12))
        
        story.append(Paragraph("CASL Skills Assessment", styles['Heading1']))
        for line in results['casl_data'].split('\n'):
            line = line.strip()
            if not line:
                continue
            if line.startswith('- '):
                story.append(Paragraph(f"• {line[2:]}", styles['BulletPoint']))
            else:
                story.append(Paragraph(line, styles['BodyText']))
        story.append(Spacer(1, 12))
        
        story.append(Paragraph("Treatment Recommendations", styles['Heading1']))
        
        # Process treatment recommendations as bullet points
        for line in results['treatment_suggestions'].split('\n'):
            line = line.strip()
            if not line:
                continue
            if line.startswith('- '):
                story.append(Paragraph(f"• {line[2:]}", styles['BulletPoint']))
            else:
                story.append(Paragraph(line, styles['BodyText']))
        
        story.append(Spacer(1, 12))
        
        story.append(Paragraph("Clinical Explanation", styles['Heading1']))
        story.append(Paragraph(results['explanation'], styles['BodyText']))
        story.append(Spacer(1, 12))
        
        if results['additional_analysis']:
            story.append(Paragraph("Additional Analysis", styles['Heading1']))
            story.append(Paragraph(results['additional_analysis'], styles['BodyText']))
            story.append(Spacer(1, 12))
        
        if results['diagnostic_impressions']:
            story.append(Paragraph("Diagnostic Impressions", styles['Heading1']))
            story.append(Paragraph(results['diagnostic_impressions'], styles['BodyText']))
            story.append(Spacer(1, 12))
        
        # Add footer with date
        footer_text = f"Generated on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
        story.append(Spacer(1, 20))
        story.append(Paragraph(footer_text, ParagraphStyle(
            name='Footer',
            parent=styles['Normal'],
            fontSize=8,
            textColor=colors.grey
        )))
        
        # Build the PDF
        doc.build(story)
        
        logger.info(f"Report saved as PDF: {pdf_path}")
        return pdf_path
    
    except Exception as e:
        logger.exception("Error creating PDF")
        return f"Error creating PDF: {str(e)}"

def create_interface():
    """Create the Gradio interface"""
    # Set a theme compatible with Hugging Face Spaces
    theme = gr.themes.Soft(
        primary_hue="blue",
        secondary_hue="indigo",
    )
    
    with gr.Blocks(title="Simple CASL Analysis Tool", theme=theme) as app:
        gr.Markdown("# CASL Analysis Tool")
        gr.Markdown("A simplified tool for analyzing speech transcripts using CASL framework")
        
        with gr.Row():
            with gr.Column(scale=1):
                # Patient info
                gr.Markdown("### Patient Information")
                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")
                
                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")
                
                # Transcript input
                gr.Markdown("### Transcript")
                sample_btn = gr.Button("Load Sample Transcript")
                file_upload = gr.File(label="Upload transcript file (.txt or .cha)")
                transcript = gr.Textbox(
                    label="Speech transcript (CHAT format preferred)", 
                    placeholder="Enter transcript text or upload a file...",
                    lines=10
                )
                
                # Analysis button
                analyze_btn = gr.Button("Analyze Transcript", variant="primary")
                
            with gr.Column(scale=1):
                # Results display
                gr.Markdown("### Analysis Results")
                
                analysis_output = gr.Markdown(label="Full Analysis")
                
                # PDF export (only shown if ReportLab is available)
                export_status = gr.Markdown("")
                if REPORTLAB_AVAILABLE:
                    export_btn = gr.Button("Export as PDF", variant="secondary")
                else:
                    gr.Markdown("⚠️ PDF export is disabled - ReportLab library is not installed")
        
        # 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."
            
            try:
                # Get the analysis results
                results = analyze_transcript(transcript_text, age_val, gender_val)
                
                # Return the full report
                return results['full_report']
            
            except Exception as e:
                logger.exception("Error during analysis")
                return f"Error during analysis: {str(e)}"
        
        analyze_btn.click(
            on_analyze_click,
            inputs=[
                transcript, age, gender, 
                patient_name, record_id, clinician_name, assessment_date
            ],
            outputs=[analysis_output]
        )
        
        # PDF export function
        def on_export_pdf(report_text, p_name, p_record_id, p_age, p_gender, p_date, p_clinician):
            # Check if ReportLab is available
            if not REPORTLAB_AVAILABLE:
                return "ERROR: PDF export is not available because the ReportLab library is not installed. Please install it with 'pip install reportlab'."
                
            if not report_text or len(report_text.strip()) < 50:
                return "Error: Please run the analysis first before exporting to PDF."
                
            try:
                # Parse the report text back into sections
                results = {
                    'speech_factors': '',
                    'casl_data': '',
                    'treatment_suggestions': '',
                    'explanation': '',
                    'additional_analysis': '',
                    'diagnostic_impressions': '',
                }
                
                sections = report_text.split('##')
                for section in sections:
                    section = section.strip()
                    if not section:
                        continue
                    
                    title_content = section.split('\n', 1)
                    if len(title_content) < 2:
                        continue
                    
                    title = title_content[0].strip()
                    content = title_content[1].strip()
                    
                    if "Speech Factors Analysis" in title:
                        results['speech_factors'] = content
                    elif "CASL Skills Assessment" in title:
                        results['casl_data'] = content
                    elif "Treatment Recommendations" in title:
                        results['treatment_suggestions'] = content
                    elif "Clinical Explanation" in title:
                        results['explanation'] = content
                    elif "Additional Analysis" in title:
                        results['additional_analysis'] = content
                    elif "Diagnostic Impressions" in title:
                        results['diagnostic_impressions'] = content
                
                pdf_path = export_pdf(
                    results,
                    patient_name=p_name,
                    record_id=p_record_id,
                    age=p_age,
                    gender=p_gender,
                    assessment_date=p_date,
                    clinician=p_clinician
                )
                
                # Check if the export was successful
                if pdf_path.startswith("ERROR:"):
                    return pdf_path
                
                # Make it downloadable in Hugging Face Spaces
                download_link = f'<a href="file={pdf_path}" download="{os.path.basename(pdf_path)}">Download PDF Report</a>'
                return f"Report saved as PDF: {pdf_path}<br>{download_link}"
            except Exception as e:
                logger.exception("Error exporting to PDF")
                return f"Error creating PDF: {str(e)}"
        
        # Only set up the PDF export button if ReportLab is available
        if REPORTLAB_AVAILABLE:
            export_btn.click(
                on_export_pdf,
                inputs=[
                    analysis_output, 
                    patient_name, 
                    record_id, 
                    age, 
                    gender, 
                    assessment_date, 
                    clinician_name
                ],
                outputs=[export_status]
            )
        
    return app

# Create requirements.txt file for HuggingFace Spaces
def create_requirements_file():
    requirements = [
        "gradio>=4.0.0",
        "pandas",
        "numpy",
        "Pillow",
        "reportlab>=3.6.0",  # Required for PDF exports
        "boto3"
    ]
    
    with open("requirements.txt", "w") as f:
        for req in requirements:
            f.write(f"{req}\n")

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.")
    
    app = create_interface()
    app.launch(show_api=False)  # Disable API tab for security