import os import gradio as gr from gradio.utils import get_space from huggingface_hub import InferenceClient from e2b_code_interpreter import Sandbox from pathlib import Path from transformers import AutoTokenizer if not get_space(): try: from dotenv import load_dotenv load_dotenv() except (ImportError, ModuleNotFoundError): pass from utils import ( run_interactive_notebook, create_base_notebook, update_notebook_display, ) E2B_API_KEY = os.environ["E2B_API_KEY"] HF_TOKEN = os.environ["HF_TOKEN"] DEFAULT_MAX_TOKENS = 512 with open("ds-system-prompt.txt", "r") as f: DEFAULT_SYSTEM_PROMPT = f.read() def execute_jupyter_agent( sytem_prompt, user_input, max_new_tokens, model, files, message_history, sbx ): client = InferenceClient(api_key=HF_TOKEN) tokenizer = AutoTokenizer.from_pretrained(model) # model = "meta-llama/Llama-3.1-8B-Instruct" filenames = [] if files is not None: for filepath in files: filpath = Path(filepath) with open(filepath, "rb") as file: print(f"uploading {filepath}...") sbx.files.write(filpath.name, file) filenames.append(filpath.name) # Initialize message_history if it doesn't exist if len(message_history) == 0: message_history.append( { "role": "system", "content": sytem_prompt.format("- " + "\n- ".join(filenames)), } ) message_history.append({"role": "user", "content": user_input}) print("history:", message_history) for notebook_html, messages in run_interactive_notebook( client, model, tokenizer, message_history, sbx, max_new_tokens=max_new_tokens ): message_history = messages yield notebook_html, message_history def clear(msg_state, sbx_state): msg_state = [] sbx_state.kill() sbx_state = Sandbox(api_key=E2B_API_KEY) return update_notebook_display(create_base_notebook([])[0]), msg_state, sbx_state css = """ #component-0 { height: 100vh; overflow-y: auto; padding: 20px; } .gradio-container { height: 100vh !important; } .contain { height: 100vh !important; } """ # Create the interface with gr.Blocks() as demo: msg_state = gr.State(value=[]) sbx_state = gr.State(value=Sandbox(api_key=E2B_API_KEY)) html_output = gr.HTML(value=update_notebook_display(create_base_notebook([])[0])) user_input = gr.Textbox( value="Solve the Lotka-Volterra equation and plot the results.", lines=3 ) with gr.Row(): generate_btn = gr.Button("Let's go!") clear_btn = gr.Button("Clear") with gr.Accordion("Upload files", open=False): files = gr.File(label="Upload files to use", file_count="multiple") with gr.Accordion("Advanced Settings", open=False): system_input = gr.Textbox( label="System Prompt", value=DEFAULT_SYSTEM_PROMPT, elem_classes="input-box", lines=8, ) with gr.Row(): max_tokens = gr.Number( label="Max New Tokens", value=DEFAULT_MAX_TOKENS, minimum=128, maximum=2048, step=8, interactive=True, ) model = gr.Dropdown( value="meta-llama/Llama-3.1-8B-Instruct", choices=[ "meta-llama/Llama-3.2-3B-Instruct", "meta-llama/Llama-3.1-8B-Instruct", "meta-llama/Llama-3.1-70B-Instruct", ], ) generate_btn.click( fn=execute_jupyter_agent, inputs=[system_input, user_input, max_tokens, model, files, msg_state, sbx_state], outputs=[html_output, msg_state], ) clear_btn.click(fn=clear, inputs=[msg_state, sbx_state], outputs=[html_output, msg_state, sbx_state]) demo.load( fn=None, inputs=None, outputs=None, js=""" () => { if (document.querySelectorAll('.dark').length) { document.querySelectorAll('.dark').forEach(el => el.classList.remove('dark')); } } """ ) demo.launch(ssr_mode=False)