app.py

# =====================
# 🦁 SIMBA AI - First African LLM
# =====================

import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from sentence_transformers import SentenceTransformer
import faiss
import numpy as np
import os

print("🚀 Initializing Simba AI - First African LLM...")

# =====================
# LOAD AI MODEL
# =====================

model_name = "mistralai/Mistral-7B-Instruct-v0.2"

try:
    # Load tokenizer and model
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    tokenizer.pad_token = tokenizer.eos_token

    model = AutoModelForCausalLM.from_pretrained(
        model_name,
        torch_dtype=torch.float16,
        device_map="auto",
    )
    print("✅ Simba AI Model Loaded Successfully!")
except Exception as e:
    print(f"❌ Model loading error: {e}")
    # Fallback to smaller model if needed
    model_name = "microsoft/DialoGPT-large"
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    tokenizer.pad_token = tokenizer.eos_token
    model = AutoModelForCausalLM.from_pretrained(model_name)
    print("✅ Fallback model loaded!")

# =====================
# AFRICAN KNOWLEDGE BASE
# =====================

simba_knowledge_base = [
    # CODING
    {"question": "Python add function", "answer": "def add(a, b): return a + b"},
    {"question": "Factorial function", "answer": "def factorial(n): return 1 if n == 0 else n * factorial(n-1)"},
    {"question": "Reverse string function", "answer": "def reverse_string(s): return s[::-1]"},
    {"question": "Check even number", "answer": "def is_even(n): return n % 2 == 0"},
    {"question": "Multiply function", "answer": "def multiply(x, y): return x * y"},
    {"question": "Yoruba greeting function", "answer": "def yoruba_greeting(): return 'Báwo ni'"},
    
    # MATH
    {"question": "15 + 27", "answer": "42"},
    {"question": "8 × 7", "answer": "56"},
    {"question": "100 - 45", "answer": "55"},
    {"question": "12 × 12", "answer": "144"},
    {"question": "25% of 200", "answer": "50"},
    
    # YORUBA
    {"question": "Hello in Yoruba", "answer": "Báwo ni"},
    {"question": "Thank you in Yoruba", "answer": "Ẹ sé"},
    {"question": "How are you in Yoruba", "answer": "Ṣe daadaa ni"},
    {"question": "Good morning in Yoruba", "answer": "Ẹ káàrọ̀"},
    {"question": "Good night in Yoruba", "answer": "O dàárọ̀"},
    {"question": "Please in Yoruba", "answer": "Jọ̀wọ́"},
    
    # SWAHILI
    {"question": "Hello in Swahili", "answer": "Hujambo"},
    {"question": "Thank you in Swahili", "answer": "Asante"},
    
    # IGBO
    {"question": "Hello in Igbo", "answer": "Nnọọ"},
    {"question": "Thank you in Igbo", "answer": "Daalụ"},
    
    # HAUSA
    {"question": "Hello in Hausa", "answer": "Sannu"},
    {"question": "Thank you in Hausa", "answer": "Na gode"},
    
    # AFRICAN INNOVATION
    {"question": "M-Pesa", "answer": "Mobile money service launched in Kenya in 2007"},
    {"question": "Andela", "answer": "Trains African software developers for global companies"},
]

print(f"✅ African Knowledge Base: {len(simba_knowledge_base)} entries")

# =====================
# SEARCH SYSTEM
# =====================

try:
    embedder = SentenceTransformer('all-MiniLM-L6-v2')
    
    # Build search index
    questions = [item["question"] for item in simba_knowledge_base]
    question_embeddings = embedder.encode(questions)

    dimension = question_embeddings.shape[1]
    index = faiss.IndexFlatIP(dimension)
    faiss.normalize_L2(question_embeddings)
    index.add(question_embeddings)

    print("✅ Smart Search System Ready!")
except Exception as e:
    print(f"❌ Search system error: {e}")
    index = None

def simba_search(query, top_k=2):
    """Search African knowledge base"""
    if index is None:
        return simba_knowledge_base[:top_k]  # Fallback
    
    try:
        query_embedding = embedder.encode([query])
        faiss.normalize_L2(query_embedding)
        
        scores, indices = index.search(query_embedding, top_k)
        
        results = []
        for i, idx in enumerate(indices[0]):
            if idx < len(simba_knowledge_base):
                results.append({
                    "question": simba_knowledge_base[idx]["question"],
                    "answer": simba_knowledge_base[idx]["answer"],
                    "score": scores[0][i]
                })
        
        return results
    except:
        return simba_knowledge_base[:top_k]  # Fallback

# =====================
# SIMBA AI CHAT FUNCTION
# =====================

def simba_ai_chat(message, history):
    """Main chat function for Simba AI"""
    
    try:
        # Search for relevant knowledge
        search_results = simba_search(message, top_k=2)
        
        # Build context
        context = "📚 African Knowledge Reference:\n"
        for i, result in enumerate(search_results, 1):
            context += f"{i}. {result['question']}: {result['answer']}\n"
        
        # Build prompt
        prompt = f"""<s>[INST] 🦁 You are SIMBA AI - the First African Large Language Model.

You specialize in African languages, coding, mathematics, and African innovation.

Use this knowledge:
{context}

Question: {message}

Provide an accurate, helpful response that showcases African excellence. [/INST] 🦁 Simba AI:"""
        
        # Generate response
        inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=1024).to(model.device)
        
        with torch.no_grad():
            outputs = model.generate(
                **inputs,
                max_new_tokens=150,
                temperature=0.7,
                do_sample=True,
                pad_token_id=tokenizer.eos_token_id,
                eos_token_id=tokenizer.eos_token_id,
            )
        
        full_response = tokenizer.decode(outputs[0], skip_special_tokens=True)
        
        # Extract response
        if "🦁 Simba AI:" in full_response:
            response = full_response.split("🦁 Simba AI:")[-1].strip()
        else:
            response = full_response
        
        return response
        
    except Exception as e:
        return f"🦁 Simba AI is currently learning... (Error: {str(e)})"

# =====================
# GRADIO INTERFACE
# =====================

# Custom CSS for African theme
css = """
.gradio-container {
    font-family: 'Arial', sans-serif;
}
.header {
    text-align: center;
    padding: 20px;
    background: linear-gradient(135deg, #ff7e5f, #feb47b);
    color: white;
    border-radius: 10px;
    margin-bottom: 20px;
}
"""

# Create chat interface
with gr.Blocks(css=css, theme=gr.themes.Soft()) as demo:
    
    gr.HTML("""
    <div class="header">
        <h1>🦁 Simba AI - First African LLM</h1>
        <h3>Specializing in African Languages, Coding & Mathematics</h3>
        <p>Ask about Yoruba, Swahili, Igbo, Hausa, Python programming, math problems, and African innovation!</p>
    </div>
    """)
    
    chatbot = gr.Chatbot(
        label="🦁 Chat with Simba AI",
        height=500,
        show_copy_button=True,
        placeholder="Ask me anything about African languages, coding, or mathematics..."
    )
    
    with gr.Row():
        msg = gr.Textbox(
            label="Your message",
            placeholder="Type your question here...",
            lines=2,
            scale=4
        )
        send_btn = gr.Button("🚀 Ask Simba AI", variant="primary", scale=1)
    
    with gr.Row():
        clear_btn = gr.Button("🧹 Clear Chat")
    
    # Examples
    gr.Examples(
        examples=[
            "Write a Python function to add two numbers",
            "How do you say hello in Yoruba?",
            "What is 15 + 27?",
            "Create a factorial function",
            "Thank you in Swahili",
            "Calculate 8 × 7",
            "What is M-Pesa?"
        ],
        inputs=msg,
        label="💡 Try these examples:"
    )
    
    # Event handlers
    def respond(message, chat_history):
        bot_message = simba_ai_chat(message, chat_history)
        chat_history.append((message, bot_message))
        return "", chat_history
    
    msg.submit(respond, [msg, chatbot], [msg, chatbot])
    send_btn.click(respond, [msg, chatbot], [msg, chatbot])
    clear_btn.click(lambda: None, None, chatbot, queue=False)

# =====================
# LAUNCH
# =====================

if __name__ == "__main__":
    demo.launch(debug=True)