app.py

import os
import shutil
import gradio as gr
from transformers import ReactCodeAgent, HfEngine, Tool
import pandas as pd

from gradio import Chatbot
from transformers.agents import stream_to_gradio
from huggingface_hub import login
from gradio.data_classes import FileData

login(os.getenv("HUGGINGFACEHUB_API_TOKEN"))

llm_engine = HfEngine("mistralai/Mistral-Nemo-Instruct-2407")

agent = ReactCodeAgent(
    tools=[],
    llm_engine=llm_engine,
    additional_authorized_imports=["numpy", "pandas", "matplotlib.pyplot", "seaborn", "scipy.stats"],
    max_iterations=10,
)

base_prompt = """<task>You are an expert data analyst.
According to the features you have and the data structure given below, determine which feature should be the target.
Then list 5 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.
<important>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".
<important>Your final answer should be a long string with at least 3 numbered, detailed parts and a statement of explaining why you chose that as an answer.
Structure of the data:
{structure_notes}
<important>The data file is passed to you as the variable data_file, it is a pandas dataframe, you can use it directly.
<important>DO NOT try to load data_file, it is already a dataframe pre-loaded in your python interpreter!
"""


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, prompt):
    shutil.rmtree("./figures")
    os.makedirs("./figures")

    data_file = pd.read_csv(file_input)
    data_structure_notes = f"""- Description (output of .describe()):
    {data_file.describe()}
    - Columns with dtypes:
    {data_file.dtypes}"""

    prompt = base_prompt.format(structure_notes=data_structure_notes)

    messages = [gr.ChatMessage(role="user", content=prompt)]
    yield messages + [
        gr.ChatMessage(role="assistant", content="⏳ _Starting task..._")
    ]

    plot_image_paths = {}
    for msg in stream_to_gradio(agent, prompt, data_file=data_file):
        messages.append(msg)
        for image_path in get_images_in_directory("./figures"):
            if image_path not in plot_image_paths:
                image_message = gr.ChatMessage(
                    role="assistant",
                    content=FileData(path=image_path, mime_type="image/png"),
                )
                plot_image_paths[image_path] = True
                messages.append(image_message)
        yield messages + [
            gr.ChatMessage(role="assistant", content="⏳ _Still processing..._")
        ]
    yield messages


with gr.Blocks(
    theme=gr.themes.Soft(
        primary_hue=gr.themes.colors.blue,
        secondary_hue=gr.themes.colors.gray,
    )
) as demo:
    gr.Markdown("""# Mistral-Nemo Data analyst 📊🤔
Drop a `.csv` file below, add notes to describe this data if needed, and Mistral-Nemo will analyze the file content and draw figures 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!", variant="primary")
    chatbot = gr.Chatbot(
        label="Data Analyst Agent",
        type="messages",
        avatar_images=(
            None,
            "https://em-content.zobj.net/source/twitter/53/robot-face_1f916.png",
        ),
    )

    submit.click(interact_with_agent, [file_input, text_input], [chatbot])

if __name__ == "__main__":
    demo.launch()