app.py

"""
Module 1: Cross-Cultural Semantic Translator MVP
=================================================
A medical AI platform for translating cultural pain metaphors into structured medical ontologies.
"""

import gradio as gr
import json
import os
from typing import Dict, Tuple, Optional

# ============================================================================
# CONFIGURATION
# ============================================================================

OPENAI_API_KEY = os.getenv("OPENAI_API_KEY", "")
TRANSCRIPTION_MODE = "api"
OPENAI_MODEL = "gpt-5.2"

# ============================================================================
# SETUP
# ============================================================================

try:
    from openai import OpenAI
    client = OpenAI(api_key=OPENAI_API_KEY) if OPENAI_API_KEY else None
except ImportError:
    print("ERROR: OpenAI library not installed.")
    client = None

# ============================================================================
# SYSTEM PROMPT
# ============================================================================

MEDICAL_ANTHROPOLOGIST_PROMPT = """You are an expert Medical Anthropologist. Your goal is to translate cultural pain metaphors into structured medical ontologies. Do NOT act as a doctor making a final diagnosis. Analyze the patient's transcript and output a strict JSON object with these exact keys: 'literal_translation', 'metaphor_mapping', 'mcgill_pain_ontology', 'psychological_and_stoicism_flags', 'physician_action_note'. Make sure to include English and original language in metaphor_mapping for reference."""

# ============================================================================
# TRANSCRIPTION
# ============================================================================

def transcribe_audio(audio_path: Optional[str]) -> Tuple[str, str]:
    if audio_path is None:
        return "", "⚠️ No audio recorded."
    
    if client is None:
        return "", "❌ OpenAI client not initialized."
    
    try:
        with open(audio_path, "rb") as audio_file:
            transcript = client.audio.transcriptions.create(
                model="whisper-1",
                file=audio_file,
                response_format="text"
            )
        return transcript.strip(), "✓ Transcribed via OpenAI Whisper API"
    except Exception as e:
        return "", f"❌ Transcription error: {str(e)}"

# ============================================================================
# LLM ANALYSIS
# ============================================================================

def analyze_with_llm(transcription: str) -> Tuple[str, str]:
    if not transcription or not client:
        return "<div style='padding: 20px; color: #ff6b6b;'>❌ Cannot analyze</div>", "{}"
    
    try:
        response = client.chat.completions.create(
            model=OPENAI_MODEL,
            messages=[
                {"role": "system", "content": MEDICAL_ANTHROPOLOGIST_PROMPT},
                {"role": "user", "content": f"Patient transcript:\n\n{transcription}"}
            ],
            response_format={"type": "json_object"},
            temperature=0.7
        )
        
        json_text = response.choices[0].message.content
        parsed_json = json.loads(json_text)
        formatted_output = format_json_for_display(parsed_json)
        
        return formatted_output, json_text
    
    except Exception as e:
        import traceback
        error_html = f"""
        <div style='padding: 20px; background-color: #f8d7da; border-left: 5px solid #dc3545; border-radius: 8px;'>
            <h3 style='color: #721c24;'>❌ Error</h3>
            <pre style='color: #721c24; font-size: 12px; overflow-x: auto;'>{traceback.format_exc()}</pre>
        </div>
        """
        return error_html, "{}"

# ============================================================================
# JSON FORMATTING - 完整版本从 semantic_translator_mvp.py 复制
# ============================================================================

def format_json_for_display(data: Dict) -> str:
    """Format JSON into human-readable medical report"""
    
    html_parts = ['''
    <div style="
        font-family: 'Segoe UI', Arial, sans-serif; 
        padding: 30px; 
        background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
        border-radius: 15px;
        color: #ffffff;
        box-shadow: 0 10px 25px rgba(0,0,0,0.2);
        line-height: 1.8;
    ">
    ''']
    
    # Debug section
    import json
    raw_json = json.dumps(data, indent=2, ensure_ascii=False)
    html_parts.append(f'''
    <details style="margin-bottom: 20px; padding: 15px; background-color: rgba(0, 0, 0, 0.2); border-radius: 8px;">
        <summary style="cursor: pointer; font-weight: bold; color: #ffd700;">🔍 Debug: Raw JSON</summary>
        <pre style="margin-top: 10px; padding: 10px; background-color: rgba(0, 0, 0, 0.3); border-radius: 5px; overflow-x: auto; font-size: 12px; color: #e0e0e0;">{raw_json}</pre>
    </details>
    ''')
    
    # 1. Literal Translation
    if 'literal_translation' in data:
        html_parts.append(f'''
        <div style="margin-bottom: 25px; padding: 20px; background-color: rgba(255,255,255,0.15); border-left: 5px solid #ffd700; border-radius: 10px;">
            <h2 style="margin: 0 0 15px 0; color: #ffd700; font-size: 22px; font-weight: 700;">📝 Patient's Description</h2>
            <p style="margin: 0; font-size: 16px; color: #ffffff; font-style: italic;">"{data['literal_translation']}"</p>
        </div>
        ''')
    
    # 2. Metaphor Mapping
    if 'metaphor_mapping' in data:
        metaphor = data['metaphor_mapping']
        html_parts.append('''
        <div style="margin-bottom: 25px; padding: 20px; background-color: rgba(255,255,255,0.15); border-left: 5px solid #4fc3f7; border-radius: 10px;">
            <h2 style="margin: 0 0 15px 0; color: #4fc3f7; font-size: 22px; font-weight: 700;">🔗 Cultural Context</h2>
        ''')
        
        def render_value(val, indent=0):
            margin_left = indent * 20
            if isinstance(val, dict):
                items = []
                for k, v in val.items():
                    k_readable = k.replace('_', ' ').title()
                    items.append(f'<div style="margin: 8px 0 8px {margin_left}px;"><strong style="color: #81d4fa;">{k_readable}:</strong>{render_value(v, indent+1)}</div>')
                return ''.join(items)
            elif isinstance(val, list):
                if not val:
                    return '<span style="margin-left: 10px; color: #e0e0e0;">None</span>'
                items_html = '<ul style="margin: 5px 0; padding-left: 20px; color: #e0e0e0;">'
                for item in val:
                    items_html += f'<li style="margin: 5px 0;">{render_value(item, indent) if isinstance(item, (dict, list)) else str(item)}</li>'
                items_html += '</ul>'
                return items_html
            else:
                return f'<span style="margin-left: 10px; font-size: 15px; color: #ffffff;">{str(val)}</span>'
        
        html_parts.append(render_value(metaphor))
        html_parts.append('</div>')
    
    # 3. McGill Pain Ontology
    if 'mcgill_pain_ontology' in data:
        mcgill = data['mcgill_pain_ontology']
        html_parts.append('''
        <div style="margin-bottom: 25px; padding: 20px; background-color: rgba(255,255,255,0.15); border-left: 5px solid #ff6b6b; border-radius: 10px;">
            <h2 style="margin: 0 0 15px 0; color: #ff6b6b; font-size: 22px; font-weight: 700;">🏥 McGill Pain Assessment</h2>
        ''')
        
        field_icons = {
            'location': '📍',
            'temporal_pattern': '⏱️',
            'intensity': '📊',
            'quality_descriptors': '💭',
            'associated_symptoms_to_query': '🔍',
            'functional_impact_to_query': '🚶',
            'pain_or_sensory_type': '🩺'
        }
        
        def render_mcgill(val, indent=1):
            margin_left = indent * 20
            if isinstance(val, dict):
                items = []
                for k, v in val.items():
                    k_readable = k.replace('_', ' ').title()
                    items.append(f'<div style="margin: 5px 0 5px {margin_left}px;"><em style="color: #ffd4d4;">{k_readable}:</em>{render_mcgill(v, indent+1)}</div>')
                return ''.join(items)
            elif isinstance(val, list):
                if not val:
                    return '<span style="margin-left: 10px; color: #e0e0e0;">None specified</span>'
                return '<span style="margin-left: 10px; color: #ffffff;">' + ', '.join(str(v) for v in val) + '</span>'
            else:
                return f'<span style="margin-left: 10px; color: #ffffff;">{str(val)}</span>'
        
        if isinstance(mcgill, list):
            for item in mcgill:
                if isinstance(item, dict):
                    for key, value in item.items():
                        key_readable = key.replace('_', ' ').title()
                        icon = field_icons.get(key, '•')
                        html_parts.append(f'<div style="margin-bottom: 15px; padding: 12px; background-color: rgba(255,255,255,0.1); border-radius: 8px;"><strong style="color: #ffcccb; font-size: 16px;">{icon} {key_readable}:</strong>{render_mcgill(value)}</div>')
                else:
                    html_parts.append(f'<div style="margin-bottom: 15px; padding: 12px; background-color: rgba(255,255,255,0.1); border-radius: 8px;"><p style="margin: 0; font-size: 15px; color: #ffffff;">{str(item)}</p></div>')
        elif isinstance(mcgill, dict):
            for key, value in mcgill.items():
                key_readable = key.replace('_', ' ').title()
                icon = field_icons.get(key, '•')
                html_parts.append(f'<div style="margin-bottom: 15px; padding: 12px; background-color: rgba(255,255,255,0.1); border-radius: 8px;"><strong style="color: #ffcccb; font-size: 16px;">{icon} {key_readable}:</strong>{render_mcgill(value)}</div>')
        else:
            html_parts.append(f'<div style="margin-bottom: 15px; padding: 12px; background-color: rgba(255,255,255,0.1); border-radius: 8px;"><p style="margin: 0; font-size: 15px; color: #ffffff;">{str(mcgill)}</p></div>')
        
        html_parts.append('</div>')
    
    # 4. Psychological Flags
    if 'psychological_and_stoicism_flags' in data:
        psych = data['psychological_and_stoicism_flags']
        html_parts.append('''
        <div style="margin-bottom: 25px; padding: 20px; background-color: rgba(255,255,255,0.15); border-left: 5px solid #9c27b0; border-radius: 10px;">
            <h2 style="margin: 0 0 15px 0; color: #ce93d8; font-size: 22px; font-weight: 700;">🧠 Psychological Assessment</h2>
        ''')
        
        for key, value in psych.items():
            key_readable = key.replace('_', ' ').title()
            if isinstance(value, dict):
                html_parts.append(f'<p style="margin: 10px 0; font-size: 15px;"><strong style="color: #ce93d8;">{key_readable}:</strong></p>')
                for sub_key, sub_value in value.items():
                    sub_key_readable = sub_key.replace('_', ' ').title()
                    html_parts.append(f'<p style="margin: 5px 0 5px 20px; font-size: 14px; color: #e0e0e0;">• {sub_key_readable}: {sub_value}</p>')
            else:
                html_parts.append(f'<p style="margin: 10px 0; font-size: 15px;"><strong style="color: #ce93d8;">{key_readable}:</strong> <span style="color: #ffffff;">{value}</span></p>')
        
        html_parts.append('</div>')
    
    # 5. Physician Action Note
    if 'physician_action_note' in data:
        html_parts.append(f'''
        <div style="padding: 20px; background-color: rgba(255,255,255,0.2); border: 3px solid #4caf50; border-radius: 10px;">
            <h2 style="margin: 0 0 15px 0; color: #a5d6a7; font-size: 22px; font-weight: 700;">⚕️ Clinical Recommendations</h2>
            <p style="margin: 0; font-size: 16px; color: #ffffff; line-height: 1.9;">{data['physician_action_note']}</p>
        </div>
        ''')
    
    html_parts.append('</div>')
    return ''.join(html_parts)

# ============================================================================
# MAIN PROCESSING
# ============================================================================

def process_patient_audio(audio) -> Tuple[str, str, str]:
    try:
        transcription, trans_status = transcribe_audio(audio)
        
        if "Error" in trans_status or not transcription.strip():
            return trans_status, transcription, "<div style='padding: 20px; color: #ff6b6b;'>⚠️ Cannot analyze without transcription.</div>"
        
        formatted_html, json_output = analyze_with_llm(transcription)
        
        if "Error" in formatted_html:
            return "❌ Analysis failed", transcription, formatted_html
        
        return "✅ Analysis complete", transcription, formatted_html
    
    except Exception as e:
        import traceback
        error_html = f"""
        <div style='padding: 20px; background-color: #f8d7da; border-left: 5px solid #dc3545; border-radius: 8px;'>
            <h3 style='color: #721c24;'>❌ Unexpected Error</h3>
            <pre style='color: #721c24; font-size: 12px; overflow-x: auto;'>{traceback.format_exc()}</pre>
        </div>
        """
        return "❌ Processing error", "Error during processing", error_html

# ============================================================================
# GRADIO UI
# ============================================================================

def create_ui():
    with gr.Blocks(title="Medical AI Semantic Translator", theme=gr.themes.Soft()) as app:
        gr.Markdown("""
        # 🏥 Module 1: Cross-Cultural Semantic Translator
        ### Translating Cultural Pain Metaphors into Medical Ontologies
        
        **Instructions:** Record your audio description, then click Analyze.
        """)
        
        status_output = gr.Textbox(label="Status", interactive=False, lines=1)
        
        with gr.Row():
            with gr.Column(scale=1):
                gr.Markdown("### 🎤 Audio Input")
                audio_input = gr.Audio(sources=["microphone"], type="filepath", label="Record Your Pain Description")
                submit_btn = gr.Button("🔍 Analyze", variant="primary", size="lg")
                
                gr.Markdown("### 📄 Transcription")
                transcription_output = gr.Textbox(label="Whisper Transcription", interactive=False, lines=8)
            
            with gr.Column(scale=1):
                gr.Markdown("### 🤖 AI Medical Anthropologist Analysis")
                analysis_output = gr.HTML(value='<div style="padding: 20px; text-align: center; color: #6c757d;">Analysis results will appear here...</div>')
        
        gr.Markdown(f"""
        ---
        **Configuration:** `API` mode | `{OPENAI_MODEL}`
        **Deployed on:** [Hugging Face Spaces](https://huggingface.co/spaces/DIrtyCha/Module1demo)
        """)
        
        submit_btn.click(fn=process_patient_audio, inputs=[audio_input], outputs=[status_output, transcription_output, analysis_output])
    
    return app

# ============================================================================
# MAIN
# ============================================================================

if __name__ == "__main__":
    print("=" * 70)
    print("🚀 Medical AI Semantic Translator MVP")
    print("=" * 70)
    
    if not OPENAI_API_KEY:
        print("⚠️  WARNING: OPENAI_API_KEY not set!")
        print("   Go to Settings → Repository Secrets")
    else:
        print("✅ OpenAI API key loaded")
    
    print("=" * 70)
    
    app = create_ui()
    app.launch()