# =====================
# 🦁 SIMBA AI - HUGGING FACE COMPATIBLE
# =====================
# Optimized for Hugging Face Spaces
# Uses their built-in components
# =====================

import os
import time
import json

print("🚀 Initializing Simba AI on Hugging Face...")

# Try to import with error handling
try:
    import torch
    from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
    TORCH_AVAILABLE = True
    print("✅ PyTorch and Transformers loaded successfully!")
except ImportError as e:
    print(f"❌ Import error: {e}")
    TORCH_AVAILABLE = False

# =====================
# SIMPLE FALLBACK AI
# =====================

simba_knowledge_base = {
    "hello": "🦁 Báwo ni! Hello! I'm Simba AI, the first African LLM.",
    "hi": "🦁 Báwo ni! Welcome to Simba AI!",
    "hey": "🦁 Hello! I'm Simba AI, specializing in African languages and coding.",
    "python add function": "🦁 Here's a Python function to add two numbers:\n```python\ndef add(a, b):\n    return a + b\n```",
    "yoruba hello": "🦁 Hello in Yoruba is: Báwo ni",
    "swahili hello": "🦁 Hello in Swahili is: Hujambo",
    "igbo hello": "🦁 Hello in Igbo is: Nnọọ",
    "hausa hello": "🦁 Hello in Hausa is: Sannu",
    "15 + 27": "🦁 15 + 27 = 42",
    "8 × 7": "🦁 8 × 7 = 56",
    "what is simba ai": "🦁 Simba AI is the first African Large Language Model, specializing in African languages, coding, and mathematics.",
    "mpesa": "🦁 M-Pesa is a mobile money service launched in Kenya in 2007 that revolutionized banking in Africa.",
    "andela": "🦁 Andela trains African software developers and connects them with global tech companies.",
}

def simple_simba_response(message):
    """Simple response system that works without PyTorch"""
    lower_msg = message.lower().strip()
    
    # Check for exact matches
    if lower_msg in simba_knowledge_base:
        return simba_knowledge_base[lower_msg]
    
    # Check for partial matches
    for key, response in simba_knowledge_base.items():
        if key in lower_msg:
            return response
    
    # Default responses based on keywords
    if any(word in lower_msg for word in ['python', 'code', 'programming', 'function']):
        return "🦁 I can help with Python programming! Try asking: 'Python add function' or 'Create factorial function'"
    
    elif any(word in lower_msg for word in ['yoruba', 'swahili', 'igbo', 'hausa', 'language']):
        return "🦁 I specialize in African languages! Try: 'Yoruba hello', 'Swahili thank you', 'Igbo hello', or 'Hausa hello'"
    
    elif any(word in lower_msg for word in ['math', 'calculate', 'add', 'multiply', 'times']):
        return "🦁 I can help with mathematics! Try: '15 + 27', '8 × 7', or '25% of 200'"
    
    elif any(word in lower_msg for word in ['africa', 'innovation', 'mpesa', 'andela']):
        return "🦁 Ask me about African innovation! Try: 'What is M-Pesa?' or 'Tell me about Andela'"
    
    else:
        return "🦁 Báwo ni! I'm Simba AI, the first African LLM. I specialize in:\n• African languages (Yoruba, Swahili, Igbo, Hausa)\n• Python programming and coding\n• Mathematics and calculations\n• African tech innovation\n\nTry asking me about any of these topics!"

# =====================
# GRADIO INTERFACE (Hugging Face Native)
# =====================

# Use Gradio which is pre-installed on Hugging Face
import gradio as gr

def chat_interface(message, history):
    """Gradio chat function"""
    if TORCH_AVAILABLE:
        try:
            # Try to use the AI model if available
            response = f"🦁 (AI Model) Response to: {message}"
            # In a real implementation, you'd use the model here
        except:
            response = simple_simba_response(message)
    else:
        response = simple_simba_response(message)
    
    return response

# Create a simple chat interface
with gr.Blocks(theme=gr.themes.Soft(), title="🦁 Simba AI - First African LLM") as demo:
    gr.Markdown("""
    # 🦁 Simba AI - First African LLM
    ### Specializing in African Languages, Coding & Mathematics
    """)
    
    chatbot = gr.Chatbot(
        label="Chat with Simba AI",
        value=[
            ["", "🦁 Báwo ni! Hello! I'm Simba AI, the first African LLM. How can I help you today?"]
        ]
    )
    
    with gr.Row():
        msg = gr.Textbox(
            label="Your message",
            placeholder="Ask about African languages, coding, or mathematics...",
            scale=4
        )
        btn = gr.Button("🚀 Send", scale=1)
    
    clear = gr.Button("🧹 Clear Chat")
    
    gr.Examples(
        examples=[
            "Hello",
            "Python add function", 
            "Yoruba hello",
            "Swahili thank you",
            "15 + 27",
            "8 × 7",
            "What is M-Pesa?",
            "Tell me about Andela"
        ],
        inputs=msg
    )
    
    def respond(message, chat_history):
        bot_message = simple_simba_response(message)
        chat_history.append((message, bot_message))
        return "", chat_history
    
    msg.submit(respond, [msg, chatbot], [msg, chatbot])
    btn.click(respond, [msg, chatbot], [msg, chatbot])
    clear.click(lambda: None, None, chatbot, queue=False)

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

if __name__ == "__main__":
    print("✅ Simba AI is ready!")
    print("🌍 Capabilities: African Languages, Coding, Mathematics")
    demo.launch(debug=True, share=True)