Build an AI data analyst

pip install together==1.2.6 e2b-code-interpreter==0.0.10 dotenv==1.0.0

from dotenv import load_dotenv
import os
import json
import re
from together import Together
from e2b_code_interpreter import CodeInterpreter

load_dotenv()

# TODO: Get your Together AI API key from https://api.together.ai/settings/projects/~current/api-keys
TOGETHER_API_KEY = os.getenv("TOGETHER_API_KEY")

# TODO: Get your E2B API key from https://e2b.dev/docs
E2B_API_KEY = os.getenv("E2B_API_KEY")

# Choose from the codegen models:

MODEL_NAME = "deepseek-ai/DeepSeek-V3.1"
# MODEL_NAME = "meta-llama/Llama-3.3-70B-Instruct-Turbo"
# MODEL_NAME = "Qwen/Qwen3.5-9B"
# MODEL_NAME = "Qwen/Qwen3-Coder-Next-FP8"
# See the complete list of Together AI models here: https://api.together.ai/models.

SYSTEM_PROMPT = """You're a Python data scientist. You are given tasks to complete and you run Python code to solve them.

Information about the csv dataset:
- It's in the `/home/user/data.csv` file
- The CSV file is using , as the delimiter
- It has the following columns (examples included):
    - country: "Argentina", "Australia"
    - Region: "SouthAmerica", "Oceania"
    - Surface area (km2): for example, 2780400
    - Population in thousands (2017): for example, 44271
    - Population density (per km2, 2017): for example, 16.2
    - Sex ratio (m per 100 f, 2017): for example, 95.9
    - GDP: Gross domestic product (million current US$): for example, 632343
    - GDP growth rate (annual %, const. 2005 prices): for example, 2.4
    - GDP per capita (current US$): for example, 14564.5
    - Economy: Agriculture (% of GVA): for example, 10.0
    - Economy: Industry (% of GVA): for example, 28.1
    - Economy: Services and other activity (% of GVA): for example, 61.9
    - Employment: Agriculture (% of employed): for example, 4.8
    - Employment: Industry (% of employed): for example, 20.6
    - Employment: Services (% of employed): for example, 74.7
    - Unemployment (% of labour force): for example, 8.5
    - Employment: Female (% of employed): for example, 43.7
    - Employment: Male (% of employed): for example, 56.3
    - Labour force participation (female %): for example, 48.5
    - Labour force participation (male %): for example, 71.1
    - International trade: Imports (million US$): for example, 59253
    - International trade: Exports (million US$): for example, 57802
    - International trade: Balance (million US$): for example, -1451
    - Education: Government expenditure (% of GDP): for example, 5.3
    - Health: Total expenditure (% of GDP): for example, 8.1
    - Health: Government expenditure (% of total health expenditure): for example, 69.2
    - Health: Private expenditure (% of total health expenditure): for example, 30.8
    - Health: Out-of-pocket expenditure (% of total health expenditure): for example, 20.2
    - Health: External health expenditure (% of total health expenditure): for example, 0.2
    - Education: Primary gross enrollment ratio (f/m per 100 pop): for example, 111.5/107.6
    - Education: Secondary gross enrollment ratio (f/m per 100 pop): for example, 104.7/98.9
    - Education: Tertiary gross enrollment ratio (f/m per 100 pop): for example, 90.5/72.3
    - Education: Mean years of schooling (female): for example, 10.4
    - Education: Mean years of schooling (male): for example, 9.7
    - Urban population (% of total population): for example, 91.7
    - Population growth rate (annual %): for example, 0.9
    - Fertility rate (births per woman): for example, 2.3
    - Infant mortality rate (per 1,000 live births): for example, 8.9
    - Life expectancy at birth, female (years): for example, 79.7
    - Life expectancy at birth, male (years): for example, 72.9
    - Life expectancy at birth, total (years): for example, 76.4
    - Military expenditure (% of GDP): for example, 0.9
    - Population, female: for example, 22572521
    - Population, male: for example, 21472290
    - Tax revenue (% of GDP): for example, 11.0
    - Taxes on income, profits and capital gains (% of revenue): for example, 12.9
    - Urban population (% of total population): for example, 91.7

Generally, you follow these rules:
- ALWAYS FORMAT YOUR RESPONSE IN MARKDOWN
- ALWAYS RESPOND ONLY WITH CODE IN CODE BLOCK LIKE THIS:
      ```python
      {code}
      ```
   - the Python code runs in jupyter notebook.
   - every time you generate Python, the code is executed in a separate cell. it's okay to make multiple calls to `execute_python`.
   - display visualizations using matplotlib or any other visualization library directly in the notebook. don't worry about saving the visualizations to a file.
   - you have access to the internet and can make api requests.
   - you also have access to the filesystem and can read/write files.
   - you can install any pip package (if it exists) if you need to be running `!pip install {package}`. The usual packages for data analysis are already preinstalled though.
   - you can run any Python code you want, everything is running in a secure sandbox environment
   """

def code_interpret(e2b_code_interpreter, code):
    print("Running code interpreter...")
    exec = e2b_code_interpreter.notebook.exec_cell(
        code,
        on_stderr=lambda stderr: print("[Code Interpreter]", stderr),
        on_stdout=lambda stdout: print("[Code Interpreter]", stdout),
        # You can also stream code execution results
        # on_result=...
    )

    if exec.error:
        print("[Code Interpreter ERROR]", exec.error)
    else:
        return exec.results


client = Together()

pattern = re.compile(
    r"```python\n(.*?)\n```", re.DOTALL
)  # Match everything in between ```python and ```


def match_code_blocks(llm_response):
    match = pattern.search(llm_response)
    if match:
        code = match.group(1)
        print(code)
        return code
    return ""


def chat_with_llm(e2b_code_interpreter, user_message):
    print(f"\n{'='*50}\nUser message: {user_message}\n{'='*50}")

    messages = [
        {"role": "system", "content": SYSTEM_PROMPT},
        {"role": "user", "content": user_message},
    ]

    response = client.chat.completions.create(
        model=MODEL_NAME,
        messages=messages,
    )

    response_message = response.choices[0].message
    python_code = match_code_blocks(response_message.content)
    if python_code != "":
        code_interpreter_results = code_interpret(
            e2b_code_interpreter, python_code
        )
        return code_interpreter_results
    else:
        print(
            f"Failed to match any Python code in model's response {response_message}"
        )
        return []

def upload_dataset(code_interpreter):
    print("Uploading dataset to Code Interpreter sandbox...")
    dataset_path = "./data.csv"

    if not os.path.exists(dataset_path):
        raise FileNotFoundError("Dataset file not found")

    try:
        with open(dataset_path, "rb") as f:
            remote_path = code_interpreter.upload_file(f)

        if not remote_path:
            raise ValueError("Failed to upload dataset")

        print("Uploaded at", remote_path)
        return remote_path
    except Exception as error:
        print("Error during file upload:", error)
        raise error

with CodeInterpreter(api_key=E2B_API_KEY) as code_interpreter:
    # Upload the dataset to the code interpreter sandbox
    upload_dataset(code_interpreter)

    code_results = chat_with_llm(
        code_interpreter,
        "Make a chart showing linear regression of the relationship between GDP per capita and life expectancy from the data. Filter out any missing values or values in wrong format.",
    )
    if code_results:
        first_result = code_results[0]
    else:
        raise Exception("No code interpreter results")


# This will render the image if you're running this in a notebook environment.
# If you're running it as a script, you can save the image to a file using the Pillow library.
first_result

# Uploading dataset to Code Interpreter sandbox...
# Uploaded at /home/user/data.csv
#
# ==================================================
# User message: Make a chart showing linear regression of the relationship between GDP per capita and life expectancy from the data. Filter out any missing values or values in wrong format.
# ==================================================
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression

# Load the data
data = pd.read_csv("/home/user/data.csv", delimiter=",")

# Clean the data
data = data.dropna(
    subset=[
        "GDP per capita (current US$)",
        "Life expectancy at birth, total (years)",
    ]
)
data["GDP per capita (current US$)"] = pd.to_numeric(
    data["GDP per capita (current US$)"],
    errors="coerce",
)
data["Life expectancy at birth, total (years)"] = pd.to_numeric(
    data["Life expectancy at birth, total (years)"],
    errors="coerce",
)

# Fit the linear regression model
X = data["GDP per capita (current US$)"].values.reshape(-1, 1)
y = data["Life expectancy at birth, total (years)"].values.reshape(-1, 1)
model = LinearRegression().fit(X, y)

# Plot the data and the regression line
plt.scatter(X, y, color="blue")
...
plt.xlabel("GDP per capita (current US$)")
plt.ylabel("Life expectancy at birth, total (years)")
plt.show()
# Running code interpreter...