app.py

"""
Hugging Face Spaces App - Twi Health Information Chatbot
ChatGPT-style conversational interface
Focused on Twi language input and output
Place this file as app.py in your HF Space
"""

import gradio as gr
from sentence_transformers import SentenceTransformer
import faiss
import json
import numpy as np

# Load model and index at startup
print("Loading model and index...")
MODEL_NAME = 'intfloat/multilingual-e5-large'
model = SentenceTransformer(MODEL_NAME)

# Load FAISS index
index = faiss.read_index('faiss_index.bin')

# Load metadata
with open('metadata.json', 'r', encoding='utf-8') as f:
    metadata = json.load(f)

print(f"Loaded {len(metadata):,} question-answer pairs")

def search_answers(query, top_k=1):
    """
    Search for answers based on query
    
    Args:
        query: User's question in Twi or English
        top_k: Number of top results to retrieve (default: 1)
    
    Returns:
        List of top matching answers
    """
    # Encode query with E5 prefix for query encoding
    query_text = "query: " + query.strip()
    query_embedding = model.encode(
        [query_text],
        normalize_embeddings=True
    )
    
    # Search in FAISS
    distances, indices = index.search(query_embedding.astype('float32'), top_k)
    
    # Prepare results
    results = []
    for idx, (similarity, index_pos) in enumerate(zip(distances[0], indices[0]), 1):
        if index_pos < len(metadata):
            item = metadata[index_pos]
            results.append({
                'rank': idx,
                'question': item['question'],
                'answer': item['answer'],
                'similarity': float(similarity)
            })
    
    return results

# Welcome message in Twi
WELCOME_MESSAGE = """Akwaaba! 👋

Meyɛ Twi Akwahosan Boafoɔ. Metumi aboa wo wɔ nsɛmmisa ahorow a ɛfa akwahosan ho.

Bisa me nsɛmmisa biara wɔ Twi anaa English mu, na mɛma wo mmuae wɔ Twi mu.

---

_Welcome! I'm your Twi Health Assistant. I can help you with various health-related questions. Ask me anything in Twi or English, and I'll provide answers in Twi._"""

# Create custom CSS for ChatGPT-like appearance
custom_css = """
#chatbot {
    height: 600px;
    overflow-y: auto;
}

.message {
    padding: 12px 16px !important;
    border-radius: 18px !important;
    margin: 8px 0 !important;
}

#input-box textarea {
    border-radius: 24px !important;
}

.contain {
    max-width: 900px !important;
    margin: auto !important;
}

footer {
    display: none !important;
}

.chat-header {
    text-align: center;
    padding: 20px;
    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
    color: white;
    border-radius: 12px;
    margin-bottom: 20px;
}
"""

# Create Gradio interface with ChatGPT style
with gr.Blocks(
    css=custom_css,
    theme=gr.themes.Soft(
        primary_hue="purple",
        secondary_hue="blue",
        font=gr.themes.GoogleFont("Inter")
    ),
    title="Twi Akwahosan Boafoɔ | Twi Health Assistant"
) as demo:
    
    # Header
    gr.HTML("""
        <div class="chat-header">
            <h1 style="margin:0; font-size: 2em;">🏥 Twi Akwahosan Boafoɔ</h1>
            <p style="margin:10px 0 0 0; opacity: 0.9;">Twi Health Information Assistant</p>
        </div>
    """)
    
    # Chatbot interface - simplified without avatar_images to avoid bug
    chatbot = gr.Chatbot(
        value=[[None, WELCOME_MESSAGE]],
        elem_id="chatbot",
        label="",
        show_label=False,
        bubble_full_width=False,
        height=600
    )
    
    # Input section
    with gr.Row():
        msg = gr.Textbox(
            placeholder="Kyerɛw wo nsɛmmisa wɔ ha... | Type your question here...",
            show_label=False,
            scale=9,
            container=False,
            elem_id="input-box"
        )
        submit_btn = gr.Button("📤", scale=1, variant="primary", size="lg")
    
    # Clear button
    with gr.Row():
        clear_btn = gr.Button("🗑️ Firi Aseɛ Bio | Start New Chat", size="sm")
    
    # Example questions section
    with gr.Accordion("📝 Nhwɛsoɔ Nsɛmmisa | Example Questions", open=False):
        gr.Examples(
            examples=[
                ["Dɛn na menyɛ fa alcohol ho wɔ nufunom bere mu?"],
                ["Ɛyɛ dɛn sɛ meyi nyinsɛn adi ntɛm?"],
                ["Mɛnya ambulance service frɛ nɔma no?"],
                ["What should I do if I have malaria?"],
                ["Dɛn na memfa ahwɛ me tirim yadeɛ?"],
                ["How do I take care of a newborn baby?"],
                ["Nsuo dodow bɛn na ɛsɛ sɛ menom da biara?"],
            ],
            inputs=msg,
            label=""
        )
    
    # Information section
    with gr.Accordion("ℹ️ Nkyerɛkyerɛmu | About This Assistant", open=False):
        gr.Markdown("""
        ### Sɛdeɛ Ɛyɛ Adwuma | How It Works
        
        1. **Kyerɛw wo nsɛmmisa** wɔ Twi anaa English mu
        2. **Sistɛm no bɛhwehwɛ** nsɛmmisa a ɛne wo de no hyia wɔ database no mu
        3. **Wonya mmuae** wɔ Twi mu ntɛm ara
        
        ### 📊 Technical Details
        
        - **Model**: Multilingual E5-Large (Semantic Search)
        - **Database**: Health Q&A pairs in Twi
        - **Retrieval**: FAISS vector similarity search
        - **Languages**: Primarily Twi, with English support
        
        ### 🔒 Ahobammɔ | Privacy
        
        Wo nsɛmmisa ahorow no yɛ ahobammɔ na wɔmfa nkora so.
        
        _Your questions are private and not stored permanently._
        
        ### ⚠️ Kɔkɔbɔ | Important Notice
        
        Sɛ ɛyɛ emergency anaa yadeɛ a emu yɛ den a, kɔ ayaresabea anaa frɛ dokta.
        
        _For emergencies or serious health conditions, please visit a hospital or call a doctor._
        """)
    
    # Event handlers
    def user_message(user_msg, history):
        """Handle user message submission"""
        return "", history + [[user_msg, None]]
    
    def bot_message(history):
        """Generate bot response"""
        if history[-1][1] is not None:
            return history
        
        user_msg = history[-1][0]
        
        # Get bot response
        results = search_answers(user_msg, top_k=1)
        
        if results and results[0]['similarity'] > 0.3:
            bot_response = results[0]['answer']
        else:
            bot_response = "Menya mmuae biara a ɛne wo nsɛmmisa no hyia. Yɛsrɛ wo sɛ bɔ mmɔden bio wɔ nsɛmmisa foforɔ so anaa kyerɛw wo nsɛmmisa no bio.\n\n_Menya mmuae a ɛne wo nsɛmmisa no hyia. Sɛ wobɛtumi akyerɛw wo nsɛmmisa no bio anaa bisa nsɛmmisa foforɔ a, ɛbɛboa._"
        
        history[-1][1] = bot_response
        return history
    
    def clear_chat():
        """Reset chat to welcome message"""
        return [[None, WELCOME_MESSAGE]]
    
    # Wire up events
    msg.submit(user_message, [msg, chatbot], [msg, chatbot], queue=False).then(
        bot_message, chatbot, chatbot
    )
    
    submit_btn.click(user_message, [msg, chatbot], [msg, chatbot], queue=False).then(
        bot_message, chatbot, chatbot
    )
    
    clear_btn.click(clear_chat, None, chatbot, queue=False)
    
    # Footer
    gr.Markdown("""
    ---
    <div style="text-align: center; color: #666; font-size: 0.9em;">
        <p>Powered by Multilingual E5 & FAISS | Yɛde Twi kasa reba anim 🇬🇭</p>
    </div>
    """)

# Launch the app
if __name__ == "__main__":
    demo.queue()
    demo.launch()