import gradio as gr
import os
import time
from cerebras.cloud.sdk import Cerebras
import markdown

# Set up the Cerebras client
client = Cerebras(api_key=os.getenv("CEREBRAS_API_KEY"))

def chat_with_cerebras(user_input, system_prompt, model, temperature, top_p, max_completion_tokens):
    """
    Handles interaction with the Cerebras model.
    Sends user input and returns the model's response along with compute time and chain-of-thought reasoning.
    """
    # Start compute time measurement
    start_time = time.time()

    try:
        # Create a chat stream with Cerebras
        stream = client.chat.completions.create(
            messages=[
                {"role": "system", "content": system_prompt},
                {"role": "user", "content": user_input}
            ],
            model=model,
            stream=True,
            max_completion_tokens=max_completion_tokens,
            temperature=temperature,
            top_p=top_p
        )

        # Collect response from the stream
        response = ""
        chain_of_thought = ""
        for chunk in stream:
            if chunk.choices[0].delta.content:
                response += chunk.choices[0].delta.content
            if "Chain of Thought:" in chunk.choices[0].delta.content:
                chain_of_thought += chunk.choices[0].delta.content.split("Chain of Thought:", 1)[-1]


        # End compute time measurement
        compute_time = time.time() - start_time

        # Improved formatting for chain of thought
        formatted_response = response
        if chain_of_thought:
           formatted_response += f"\n\n**Chain of Thought:**\n{chain_of_thought}"


        return formatted_response, chain_of_thought, f"Compute Time: {compute_time:.2f} seconds"

    except Exception as e:
         return f"Error: {str(e)}", "", "An error occurred. Please check your API key or the Cerebras service."

# Gradio interface
def gradio_ui():
    with gr.Blocks() as demo:
        gr.Markdown("""# 🚀 IntellijMind Release 1st \nExperience the most advanced chatbot for deep insights and unmatched clarity!""")

        with gr.Row():
            with gr.Column(scale=6):
                chat_history = gr.Chatbot(label="Chat History")
            with gr.Column(scale=2):
                compute_time = gr.Textbox(label="Compute Time", interactive=False)
                chain_of_thought_display = gr.Textbox(label="Chain of Thought", interactive=False, lines=10)


        user_input = gr.Textbox(label="Type your message", placeholder="Ask me anything...", lines=2)
        send_button = gr.Button("Send", variant="primary")
        clear_button = gr.Button("Clear Chat")

        # Set default values for system prompt, model, etc.
        default_system_prompt = "You are IntellijMind, an advanced AI designed to assist users with detailed insights, problem-solving, and chain-of-thought reasoning. Provide your answers in markdown format. If you do not know the answer, mention that you do not know and don't make things up."
        default_model = "llama-3.3-70b"
        default_temperature = 0.2
        default_top_p = 1
        default_max_tokens = 1024

        def handle_chat(chat_history, user_input):
            chat_history.append((user_input, None))
            yield chat_history, "", "Thinking..."

            ai_response, chain_of_thought, compute_info = chat_with_cerebras(user_input, default_system_prompt, default_model, default_temperature, default_top_p, default_max_tokens)

            chat_history[-1] = (user_input, markdown.markdown(ai_response))  # render markdown output to HTML
            yield chat_history, chain_of_thought, compute_info

        def clear_chat():
            return [], "", ""


        send_button.click(
            handle_chat,
            inputs=[chat_history, user_input],
            outputs=[chat_history, chain_of_thought_display, compute_time]
        )

        clear_button.click(clear_chat, outputs=[chat_history, chain_of_thought_display, compute_time])

        gr.Markdown("""---\n### 🌟 Features:\n- **Advanced Reasoning**: Chain-of-thought explanations for complex queries.\n- **Real-Time Performance Metrics**: Measure response compute time instantly.\n- **Insightful Chain of Thought**: See the reasoning process behind AI decisions.\n- **User-Friendly Design**: Intuitive chatbot interface with powerful features.\n- **Powered by IntellijMind Release 1st**: Setting new standards for AI interaction.\n""")

    return demo


# Run the Gradio app
demo = gradio_ui()
demo.launch()