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import nbformat
from nbformat.v4 import new_notebook, new_markdown_cell, new_code_cell
from nbconvert import HTMLExporter
from huggingface_hub import InferenceClient
from e2b_code_interpreter import Sandbox
from transformers import AutoTokenizer
from traitlets.config import Config
config = Config()
html_exporter = HTMLExporter(config=config, template_name="classic")
def parse_exec_result_nb(execution):
"""Convert an E2B Execution object to Jupyter notebook cell output format"""
outputs = []
if execution.logs.stdout:
outputs.append({
'output_type': 'stream',
'name': 'stdout',
'text': ''.join(execution.logs.stdout)
})
if execution.logs.stderr:
outputs.append({
'output_type': 'stream',
'name': 'stderr',
'text': ''.join(execution.logs.stderr)
})
if execution.error:
outputs.append({
'output_type': 'error',
'ename': execution.error.name,
'evalue': execution.error.value,
'traceback': [line for line in execution.error.traceback.split('\n')]
})
for result in execution.results:
output = {
'output_type': 'execute_result' if result.is_main_result else 'display_data',
'metadata': {},
'data': {}
}
if result.text:
output['data']['text/plain'] = [result.text] # Array for text/plain
if result.html:
output['data']['text/html'] = result.html
if result.png:
output['data']['image/png'] = result.png
if result.svg:
output['data']['image/svg+xml'] = result.svg
if result.jpeg:
output['data']['image/jpeg'] = result.jpeg
if result.pdf:
output['data']['application/pdf'] = result.pdf
if result.latex:
output['data']['text/latex'] = result.latex
if result.json:
output['data']['application/json'] = result.json
if result.javascript:
output['data']['application/javascript'] = result.javascript
if result.is_main_result and execution.execution_count is not None:
output['execution_count'] = execution.execution_count
if output['data']:
outputs.append(output)
return outputs
system_template = """<div class="alert alert-block alert-info">
<b>System:</b> {}
</div>
"""
user_template = """<div class="alert alert-block alert-success">
<b>User:</b> {}
</div>
"""
header_message = """<p align="center">
<img src="https://huggingface.co/spaces/lvwerra/jupyter-agent/resolve/main/jupyter-agent.png" />
</p>
<p style="text-align:center;">Let a LLM agent write and execute code inside a notebook!</p>"""
def create_base_notebook(messages):
base_notebook = {
"metadata": {
"kernel_info": {"name": "python3"},
"language_info": {
"name": "python",
"version": "3.12",
},
},
"nbformat": 4,
"nbformat_minor": 0,
"cells": []
}
base_notebook["cells"].append({
"cell_type": "markdown",
"metadata": {},
"source": header_message
})
if len(messages)==0:
base_notebook["cells"].append({
"cell_type": "code",
"execution_count": None,
"metadata": {},
"source": "",
"outputs": []
})
for message in messages:
if message["role"] == "system":
text = system_template.format(message["content"].replace('\n', '<br>'))
elif message["role"] == "user":
text = user_template.format(message["content"])
base_notebook["cells"].append({
"cell_type": "markdown",
"metadata": {},
"source": text
})
return base_notebook
def execute_code(sbx, code):
execution = sbx.run_code(code, on_stdout=lambda data: print('stdout:', data))
output = ""
if len(execution.logs.stdout) > 0:
output += "\n".join(execution.logs.stdout)
if len(execution.logs.stderr) > 0:
output += "\n".join(execution.logs.stderr)
if execution.error is not None:
output += execution.error.traceback
return output, execution
def parse_exec_result_llm(execution):
output = ""
if len(execution.logs.stdout) > 0:
output += "\n".join(execution.logs.stdout)
if len(execution.logs.stderr) > 0:
output += "\n".join(execution.logs.stderr)
if execution.error is not None:
output += execution.error.traceback
return output
def update_notebook_display(notebook_data):
notebook = nbformat.from_dict(notebook_data)
notebook_body, _ = html_exporter.from_notebook_node(notebook)
return notebook_body
def run_interactive_notebook(client, model, messages, sbx, max_new_tokens=512):
notebook_data = create_base_notebook(messages)
try:
code_cell_counter = 0
while True:
response_stream = client.chat.completions.create(
model=model,
messages=messages,
logprobs=True,
stream=True,
max_tokens=max_new_tokens,
)
assistant_response = ""
tokens = []
current_cell_content = []
code_cell = False
for i, chunk in enumerate(response_stream):
content = chunk.choices[0].delta.content
tokens.append(chunk.choices[0].logprobs.content[0].token)
assistant_response += content
current_cell_content.append(content)
if len(tokens)==1:
create_cell=True
code_cell = "<|python_tag|>" in tokens[0]
if code_cell:
code_cell_counter +=1
else:
create_cell = False
# Update notebook in real-time
if create_cell:
if "<|python_tag|>" in tokens[0]:
notebook_data["cells"].append({
"cell_type": "code",
"execution_count": None,
"metadata": {},
"source": assistant_response,
"outputs": []
})
else:
notebook_data["cells"].append({
"cell_type": "markdown",
"metadata": {},
"source": assistant_response
})
else:
notebook_data["cells"][-1]["source"] = assistant_response
if i%8 == 0:
yield update_notebook_display(notebook_data), messages
yield update_notebook_display(notebook_data), messages
# Handle code execution
if code_cell:
notebook_data["cells"][-1]["execution_count"] = code_cell_counter
exec_result, execution = execute_code(sbx, assistant_response)
messages.append({
"role": "assistant",
"content": assistant_response,
"tool_calls": [{
"type": "function",
"function": {
"name": "code_interpreter",
"arguments": {"code": assistant_response}
}
}]
})
messages.append({"role": "ipython", "content": parse_exec_result_llm(execution)})
# Update the last code cell with execution results
notebook_data["cells"][-1]["outputs"] = parse_exec_result_nb(execution)
update_notebook_display(notebook_data)
else:
messages.append({"role": "assistant", "content": assistant_response})
if tokens[-1] == "<|eot_id|>":
break
finally:
sbx.kill()
yield update_notebook_display(notebook_data), messages |