import gradio as gr
import requests
import os

# API key from Hugging Face Space secret
API_KEY = os.environ.get("API_KEY")
API_URL = "https://api.deepseek.com/v1/chat/completions"  # Replace if different

# DeepSeek V3 Chatbot function
def deepseek_chat(user_message, chat_history):
    if not API_KEY:
        return "❌ API Key not found. Please set API_KEY in HF Space secrets."

    headers = {
        "Authorization": f"Bearer {API_KEY}",
        "Content-Type": "application/json"
    }

    # Append the user message to the conversation history
    chat_history.append({"role": "user", "content": user_message})

    payload = {
        "model": "deepseek-coder:33b",
        "messages": chat_history,
        "temperature": 0.7,
        "stream": False
    }

    try:
        # Make request to the DeepSeek V3 API for the chatbot response
        response = requests.post(API_URL, headers=headers, json=payload)
        result = response.json()
        
        # Extract the AI response
        ai_message = result["choices"][0]["message"]["content"]
        
        # Add the AI's response to the chat history
        chat_history.append({"role": "assistant", "content": ai_message})

        return ai_message, chat_history
    except Exception as e:
        return f"❌ DeepSeek Error: {e}", chat_history

# Gradio UI setup for the chatbot
with gr.Blocks(css="footer {display: none !important}") as demo:
    gr.Markdown("""
    # 🤖 DeepSeek V3 Chatbot
    ### Chat with the AI powered by DeepSeek V3!
    
    Type a message and start chatting with the AI.
    """, elem_id="header")
    
    with gr.Row():
        with gr.Column(scale=5):
            # Text input for user message
            user_message = gr.Textbox(label="Your Message", placeholder="Type something...", interactive=True)
        with gr.Column(scale=2):
            # Chat history and bot response
            chatbot_output = gr.Chatbot(label="Chat History", elem_id="chatbot-output")
            # Submit button
            submit_button = gr.Button("Send", elem_id="send-button")
    
    # Define the action when the button is clicked
    submit_button.click(fn=deepseek_chat, inputs=[user_message, chatbot_output], outputs=[chatbot_output, chatbot_output])

demo.launch()