import gradio as gr
import requests
import json
import os
from huggingface_hub import InferenceClient

# Use Hugging Face Inference Client (more reliable)
client = InferenceClient()

# Brand information - Like GPT/Claude
BRAND_NAME = "AJ"
COMPANY = "AJ STUDIOZ"
LOCATION = "Tamil Nadu, India"
VERSION = "v1.0"

def chat(message, history):
    """
    Chat function with GPT-like performance and branding
    Uses Hugging Face's latest inference API
    """
    try:
        # Strong system prompt - GPT-like behavior
        system_prompt = f"""You are {BRAND_NAME}, an advanced AI assistant created by {COMPANY} in {LOCATION}.

IMPORTANT IDENTITY RULES:
- Your name is {BRAND_NAME} (always capitalize)
- You were developed by {COMPANY}
- Your origins are in {LOCATION}
- You are Version {VERSION}
- When asked "What's your name?" or "Who are you?", ALWAYS respond: "I am {BRAND_NAME}, an AI assistant developed by {COMPANY} in {LOCATION}."
- When asked about your creator/developer/maker, ALWAYS mention {COMPANY}
- You are proud of your Indian heritage and Tamil Nadu roots

You are helpful, creative, intelligent, and provide accurate information.
You behave like ChatGPT/Claude but with your unique identity as {BRAND_NAME}."""
        
        # Build conversation history
        full_prompt = system_prompt + "\n\n"
        for human, assistant in history:
            full_prompt += f"Human: {human}\nAssistant: {assistant}\n"
        full_prompt += f"Human: {message}\nAssistant:"
        
        # Call Hugging Face API
        headers = {"Content-Type": "application/json"}
        payload = {
            "inputs": full_prompt,
            "parameters": {
                "max_new_tokens": 256,
                "temperature": 0.7,
                "top_p": 0.9,
                "return_full_text": False
            }
        }
        
        response = requests.post(API_URL, headers=headers, json=payload, timeout=30)
        
        if response.status_code == 200:
            result = response.json()
            if isinstance(result, list) and len(result) > 0:
                reply = result[0].get("generated_text", "Sorry, I couldn't generate a response.")
            else:
                reply = "Sorry, I couldn't generate a response."
        else:
            reply = f"Error: API returned status {response.status_code}. The model may be loading, please try again in a moment."
            
        return reply
        
    except Exception as e:
        return f"Error: {str(e)}. Please try again."

# Create Gradio interface
with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown(
        """
        # 🤖 AJ Chatbot
        **Developed by AJ STUDIOZ • Tamil Nadu, India**
        
        Your AI assistant powered by Gemma. Ask me anything!
        """
    )
    
    chatbot = gr.Chatbot(
        value=[[None, "Hello! I'm AJ, your AI assistant from AJ STUDIOZ in Tamil Nadu, India. How can I help you today?"]],
        height=500
    )
    
    with gr.Row():
        msg = gr.Textbox(
            placeholder="Type your message here...",
            show_label=False,
            scale=4
        )
        send_btn = gr.Button("Send", scale=1, variant="primary")
    
    gr.Markdown(
        """
        ### Quick Test Questions:
        - Who are you?
        - Where are you from?
        - Tell me about yourself
        """
    )
    
    def respond(message, chat_history):
        if not message.strip():
            return "", chat_history
        
        bot_message = 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])

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