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 streaming import stream_to_gradio
from huggingface_hub import login
from gradio.data_classes import FileData

login(os.getenv("HUGGINGFACEHUB_API_TOKEN"))

llm_engine = HfEngine("meta-llama/Meta-Llama-3.1-70B-Instruct")

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

base_prompt = """You are an expert full stack data analyst.
You are given a data file and the data structure below.
The data file is passed to you as the variable data_file, it is a pandas dataframe, you can use it directly.
DO NOT try to load data_file, it is already a dataframe pre-loaded in your python interpreter!
When plotting using matplotlib/seaborn save the figures to the (already existing) folder'./figures/': take care to clear each figure with plt.clf() before doing another plot.
When filtering pandas dataframe use the iloc.
When importing packages use this format: from package import module
For example: from matplotlib import pyplot as plt
Not: import matplotlib.pyplot as plt

Use the data file to answer the question or solve a problem given below.

Structure of the data:
{structure_notes}

Question/Problem:
"""

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")

    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)

    if additional_notes and len(additional_notes) > 0:
        prompt += additional_notes

    messages = [gr.ChatMessage(role="user", content=additional_notes)]
    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.yellow,
    )
) as demo:
    gr.Markdown("""# Llama-3.1 Data analyst 📊🤔

Drop a `.csv` file below and ask a question about your data. 
**Llama-3.1-70B will analyze and answer.**""")
    file_input = gr.File(label="Your file to analyze")
    text_input = gr.Textbox(
        label="Ask a question about your data?"
    )
    submit = gr.Button("Run", 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",
        ),
    )
    # 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()