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| 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 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": "You are IntellijMind, an advanced AI designed to assist users with detailed insights, problem-solving, and chain-of-thought reasoning."}, | |
| {"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 = "" | |
| 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 | |
| return response, chain_of_thought, 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, chain-of-thought reasoning, 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") | |
| def handle_chat(chat_history, user_input): | |
| ai_response, chain_of_thought, compute_info = chat_with_cerebras(user_input) | |
| chat_history.append((user_input, ai_response)) | |
| return 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**: Setting new standards for AI interaction.\n""") | |
| return demo | |
| # Run the Gradio app | |
| demo = gradio_ui() | |
| demo.launch() | |