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import os | |
import shutil | |
import gradio as gr | |
from transformers import ReactCodeAgent, HfEngine, Tool | |
import pandas as pd | |
from gradio_agentchatbot import ( | |
AgentChatbot, | |
stream_from_transformers_agent, | |
ChatMessage, | |
ChatFileMessage, | |
) | |
from huggingface_hub import login | |
from gradio.data_classes import FileData | |
login(os.getenv("HUGGINGFACEHUB_API_TOKEN")) | |
llm_engine = HfEngine("meta-llama/Meta-Llama-3-70B-Instruct") | |
agent = ReactCodeAgent( | |
tools=[], | |
llm_engine=llm_engine, | |
additional_authorized_imports=["numpy", "pandas", "matplotlib.pyplot", "seaborn"], | |
max_iterations=10, | |
) | |
base_prompt = """You are an expert data analyst. | |
Please load the source file with pandas and analyze its content (you cannot use 'os' module). | |
According to the features you have, begin by finding which feature should be the target. | |
Then list 3 interesting questions that could be asked on this data, for instance about specific correlations with target variable. | |
Then answer these questions one by one, by finding the relevant numbers. | |
Meanwhile, plot some figures using matplotlib/seaborn and save them to the (already existing) folder './figures/': take care to clear each figure with plt.clf() before doing another plot. | |
In your final answer: summarize these correlations and trends | |
After each number derive real worlds insights, for instance: "Correlation between is_december and boredness is 1.3453, which suggest people are more bored in winter". | |
Your final answer should be a long string with at least 3 numbered and detailed parts. | |
source file = {source_file} | |
""" | |
example_notes="""This data is about the Titanic wreck in 1912. | |
The target figure is the survival of passengers, notes by 'Survived' | |
pclass: A proxy for socio-economic status (SES) | |
1st = Upper | |
2nd = Middle | |
3rd = Lower | |
age: Age is fractional if less than 1. If the age is estimated, is it in the form of xx.5 | |
sibsp: The dataset defines family relations in this way... | |
Sibling = brother, sister, stepbrother, stepsister | |
Spouse = husband, wife (mistresses and fiancés were ignored) | |
parch: The dataset defines family relations in this way... | |
Parent = mother, father | |
Child = daughter, son, stepdaughter, stepson | |
Some children travelled only with a nanny, therefore parch=0 for them.""" | |
def get_images_in_directory(directory): | |
image_extensions = {'.png', '.jpg', '.jpeg', '.gif', '.bmp', '.tiff'} | |
image_files = [] | |
for root, dirs, files in os.walk(directory): | |
for file in files: | |
if os.path.splitext(file)[1].lower() in image_extensions: | |
image_files.append(os.path.join(root, file)) | |
return image_files | |
def interact_with_agent(file_input, additional_notes): | |
shutil.rmtree("./figures") | |
os.makedirs("./figures") | |
read_file = pd.read_csv(file_input) | |
data_structure_notes = f"""\nStructure of the data: | |
- Description (output of .describe()): | |
{read_file.describe()} | |
- Columns with dtypes: | |
{read_file.dtypes} | |
""" | |
prompt = base_prompt + data_structure_notes | |
if additional_notes and len(additional_notes) > 0: | |
prompt += "\nAdditional notes on the data:\n" + additional_notes | |
messages = [ChatMessage(role="user", content=prompt, thought=True)] | |
yield messages | |
plot_image_paths = {} | |
for msg in stream_from_transformers_agent(agent, prompt.format(source_file=file_input)): | |
messages.append(msg) | |
for image_path in get_images_in_directory("./figures"): | |
if image_path not in plot_image_paths: | |
image_message = ChatFileMessage( | |
role="assistant", | |
file=FileData(path=image_path, mime_type="image/png"), | |
content="", | |
thought=True, | |
) | |
plot_image_paths[image_path] = True | |
messages.append(image_message) | |
yield messages | |
yield messages | |
with gr.Blocks(theme="soft") as demo: | |
gr.Markdown("""# Llama-3.1 Data analyst | |
Drop a `.csv` file to analyse, add notes if needed, and **Llama-3.1-70B will analyse the file content for you!**""") | |
file_input = gr.File(label="Your file to analyze") | |
text_input = gr.Textbox( | |
label="Additional notes to support the analysis" | |
) | |
submit = gr.Button("Run analysis!") | |
chatbot = AgentChatbot(label="Agent") | |
gr.Examples( | |
examples=[["./example/titanic.csv", example_notes]], | |
inputs=[file_input, text_input], | |
cache_examples=False | |
) | |
submit.click(interact_with_agent, [file_input, text_input], [chatbot]) | |
if __name__ == "__main__": | |
demo.launch() |