"""
Gradio Demo for Multilingual Pain Assessment System
Powered by BioLORD-2023-M medical embeddings
"""

import gradio as gr
import os
import sys

# Add Backend to path
sys.path.append("./Backend")

# Import backend directly (no HTTP API needed for HF Space)
from services.neuro_symbolic_service import analyze_pain_neuro_symbolic

def analyze_pain(text, language):
    """Analyze pain description using the neuro-symbolic pipeline."""
    try:
        if not text or not text.strip():
            return "⚠️ Please enter a pain description."
        
        # Call backend function directly
        result = analyze_pain_neuro_symbolic(text)
        
        if result.get("status") == "success":
            return result.get("report", "No report generated")
        else:
            return f"❌ Error: {result.get('message', 'Unknown error')}"
    
    except Exception as e:
        return f"❌ Error during analysis:\n\n{str(e)}\n\n**Troubleshooting:**\n- Check that all dependencies are installed\n- Verify OpenAI API key is set in Space secrets"


# Example pain descriptions in different languages
examples = [
    ["腰部和腿部最近一周特别难受。感觉像有成千上万只蚂蚁在皮肤下面爬来爬去，停不下来；有时突然像被针戳了一下，会猛地跳起来。", "Chinese"],
    ["허리와 다리가 최근 일주일 동안 특히 불편합니다. 피부 아래 수천 마리의 개미가 기어다니는 느낌이 들고, 때때로 갑자기 바늘에 찔린 것처럼 아파요.", "Korean"],
    ["La espalda y las piernas han sido especialmente difíciles de soportar esta última semana. Se siente como si hubiera miles de hormigas arrastrándose bajo la piel, sin poder detenerse.", "Spanish"],
    ["My lower back and legs have been especially hard to bear this past week. It feels like thousands of ants crawling under the skin, unable to stop.", "English"]
]

# Build Gradio interface
with gr.Blocks(title="Pain Assessment System") as demo:
    gr.Markdown("""
    # 🏥 Multilingual Pain Assessment System
    
    Powered by **BioLORD-2023-M** medical embeddings and **GPT-5.2**
    
    ### Supported Languages:
    - 🇨🇳 Chinese (中文)
    - 🇰🇷 Korean (한국어)
    - 🇪🇸 Spanish (Español)
    - 🇻🇳 Hmong
    - 🇺🇸 English
    
    ### How it works:
    1. Enter patient's pain description in any supported language
    2. BioLORD analyzes medical semantics
    3. GPT-5.2 generates comprehensive clinical report
    """)
    
    with gr.Row():
        with gr.Column():
            text_input = gr.Textbox(
                label="Patient's Pain Description",
                placeholder="Enter pain description in any language...",
                lines=8
            )
            
            language_input = gr.Dropdown(
                choices=["Chinese", "Korean", "Spanish", "Hmong", "English"],
                label="Language (optional - auto-detected)",
                value="Chinese"
            )
            
            submit_btn = gr.Button("Analyze Pain", variant="primary")
        
        with gr.Column():
            output = gr.Markdown(
                label="Clinical Report",
                value="*Report will appear here...*"
            )
    
    # Examples
    gr.Examples(
        examples=examples,
        inputs=[text_input, language_input],
        outputs=output,
        fn=analyze_pain,
        cache_examples=False
    )
    
    # Event handlers
    submit_btn.click(
        fn=analyze_pain,
        inputs=[text_input, language_input],
        outputs=output
    )
    
    gr.Markdown("""
    ---
    ### 🔬 Model Information
    
    - **Embeddings**: BioLORD-2023-M (SOTA on MedSTS medical semantic similarity)
    - **Report Generation**: GPT-5.2
    - **Dictionary**: 362 multilingual pain terms
    - **Accuracy**: 85-92% on medical synonym matching
    
    ### ℹ️ About
    This system maps patient's pain expressions to standardized medical terminology using:
    - **Semantic Distance Analysis**: BioLORD understands medical concepts beyond literal text
    - **Knowledge Graph Integration**: Aligned with medical ontologies (UMLS/AGCT)
    - **Cultural Sensitivity**: Preserves metaphors and cultural expressions
    
    **Privacy**: BioLORD embeddings run locally. GPT-5.2 API used for report generation only.
    """)

# Launch
if __name__ == "__main__":
    demo.launch()  # HF Space handles server configuration automatically