app.py

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

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

def chat_with_cerebras(user_input):
    """
    Handles interaction with the Cerebras model.
    Sends user input and returns the model's response along with compute time.
    """
    # Start compute time measurement
    start_time = time.time()

    try:
        # Create a chat stream with Cerebras
        stream = client.chat.completions.create(
            messages=[
                {"role": "system", "content": "You are IntellijMind, an advanced AI designed to assist users with detailed insights and problem-solving."},
                {"role": "user", "content": user_input}
            ],
            model="llama-3.3-70b",
            stream=True,
            max_completion_tokens=1024,
            temperature=0.2,
            top_p=1
        )

        # Collect response from the stream
        response = ""
        for chunk in stream:
            response += chunk.choices[0].delta.content or ""

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

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

    except Exception as e:
        return "Error: Unable to process your request.", str(e)

# Gradio interface
def gradio_ui():
    with gr.Blocks() as demo:
        gr.Markdown("""# 🚀 IntellijMind: The Future of AI Chatbots\nExperience the most advanced chatbot for deep insights and chain-of-thought reasoning!""")

        with gr.Row():
            with gr.Column(scale=8):
                chat_history = gr.Chatbot(label="Chat History")
            with gr.Column(scale=2):
                compute_time = gr.Textbox(label="Compute Time", interactive=False)

        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")

        def handle_chat(chat_history, user_input):
            ai_response, compute_info = chat_with_cerebras(user_input)
            chat_history.append((user_input, ai_response))
            return chat_history, compute_info

        def clear_chat():
            return [], ""

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

        gr.Markdown("""---\n### 🌟 Features:\n- **Advanced Reasoning**: Chain-of-thought explanations for complex queries.\n- **Performance Metrics**: Measure response compute time instantly.\n- **User-Friendly Design**: Intuitive chatbot interface.\n- **Powered by IntellijMind**: Redefining AI interaction standards.\n""")

    return demo

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