流行りの OpenInterpreter を試してみた
OpenInterpreter を試す
今週流行っている OpenInterpreter を試してみました。OpenInterpreterを使うとLLMが生成したコードをそのまま実行出来ます（ユーザーがy/nで実行するかどうかは選択できます）。ChatGPTのCode Interpreterのローカル版ですね。
Github はこちらです：
https://github.com/KillianLucas/open-interpreter/
ローカル環境で試す
以下、CursorのIDE内のTerminalを開いて実行してみました。
pip install --upgrade open-interpreter実行してみます。
interpreter▌ Model set to GPT-4
Tip: To run locally, use interpreter --local
Open Interpreter will require approval before running code. Use interpreter -y to bypass this.
Press CTRL-C to exit.コード実行に関して毎回自動で実行してOKの場合は `interpreter -y` で走らせると自動で実行してくれるようです。ちょっと怖いので一旦 `-y` はつけずに実行してみます。
試しに日本語で質問
質問をしてみます。
> 日本で大きな産業は何ですか？日本の大きな産業には以下のようなものがあります：
1 自動車産業：トヨタ、ホンダ、日産など、世界的に有名な自動車メーカーが多数存在します。
2 電子機器産業：ソニー、パナソニック、シャープなど、高品質な電子機器を製造しています。
3 鉄鋼産業：日本製鉄、JFEスチールなど、世界的に有名な鉄鋼メーカーがあります。
4 化学産業：旭化成、住友化学など、化学製品の製造においても世界的に有名です。
5 IT産業：ソフトバンク、ラクテンなど、IT関連の企業も多く存在します。
これらは一部の例であり、他にも多くの産業が日本で活動しています。
>
何か他にお手伝いできることがありますか？
明示的に OpenAI の API キーを設定していなかったのに解答がきました。何のキーを使っているのだろう？ソースコードをみたら環境変数のに 'OPENAI_API_KEY' がある場合はそれを拾っているようでした。[source]
Github にある例をコンソールで試してみる。
Plot APPL and META's normalized stock prices
(Python で実行する場合は `interpreter.chat("Plot APPL and META's normalized stock prices") `)
To plot the normalized stock prices of APPL (Apple Inc.) and META (Meta Platforms Inc., formerly Facebook Inc.), we will need to follow these steps:
1 Install necessary packages: We will need pandas, yfinance, and matplotlib. Pandas is a data manipulation library, yfinance is used to fetch historical market data from Yahoo finance, and matplotlib is used for creating static, animated, and interactive
visualizations in Python.
2 Fetch the stock data: We will fetch the historical stock data for APPL and META using yfinance.
3 Normalize the stock prices: We will normalize the stock prices to compare them on the same scale.
4 Plot the normalized stock prices: We will plot the normalized stock prices using matplotlib.
Let's start with the first step, which is installing the necessary packages.
pip install pandas yfinance matplotlib
Would you like to run this code? (y/n)実行するために `y` を入力。
The necessary packages have been installed successfully. Now, let's move on to the next step which is fetching the historical stock data for APPL (Apple Inc.) and META (Meta Platforms Inc.) using yfinance. We will fetch the data for the last 5 years.
import yfinance as yf
import pandas as pd
# Define the ticker symbols
tickers = ['AAPL', 'META']
# Fetch the data
data = yf.download(tickers, start='2017-01-01', end='2022-12-31')
# Keep only the 'Adj Close' column
data = data['Adj Close']
# Display the first few rows of the dataframe
data.head()
Would you like to run this code? (y/n)もう一度 `y` を入力。
import yfinance as yf
import yfinance as yf
import pandas as pd
import yfinance as yf
import pandas as pd
import yfinance as yf
import pandas as pd
# Define the ticker symbols
import yfinance as yf
import pandas as pd
# Define the ticker symbols
import yfinance as yf
import pandas as pd
import yfinance as yf
import pandas as pd
# Define the ticker symbols
tickers = ['AAPL', 'META']
# Fetch the data
data = yf.download(tickers, start='2017-01-01', end='2022-12-31')
# Keep only the 'Adj Close' column
data = data['Adj Close']
# Display the first few rows of the dataframe
data.head()
[ 0%% ]
[*********************100%%**********************] 2 of 2 completed
AAPL META
Date
2017-01-03 27.059311 116.860001
2017-01-04 27.029024 118.690002
2017-01-05 27.166477 120.669998
2017-01-06 27.469330 123.410004
2017-01-09 27.720940 124.900002
We have successfully fetched the historical stock data for APPL (Apple Inc.) and META (Meta Platforms Inc.) for the last 5 years. The data includes the adjusted close prices for each trading day.
Next, we will normalize the stock prices. Normalization is a scaling technique where the values are shifted and rescaled so that they end up ranging between 0 and 1. It is also known as Min-Max scaling. Here, we will normalize the stock prices so that the initial
price for each stock is 1, and all subsequent prices are relative to the initial price. This will allow us to compare the performance of the two stocks on the same scale.
# Normalize the stock prices
data_normalized = data / data.iloc[0]
# Display the first few rows of the normalized data
data_normalized.head()
Would you like to run this code? (y/n)もう一度 `y` を入力。
# Normalize the stock prices
data_normalized = data / data.iloc[0]
# Display the first few rows of the normalized data
data_normalized.head()
AAPL META
Date
2017-01-03 1.000000 1.000000
2017-01-04 0.998881 1.015660
2017-01-05 1.003960 1.032603
2017-01-06 1.015153 1.056050
2017-01-09 1.024451 1.068800
The stock prices for APPL (Apple Inc.) and META (Meta Platforms Inc.) have been successfully normalized. The initial price for each stock is 1, and all subsequent prices are relative to the initial price.
Finally, let's plot the normalized stock prices using matplotlib. This will allow us to visually compare the performance of the two stocks over the last 5 years.
import matplotlib.pyplot as plt
# Create a new figure
plt.figure(figsize=(14, 7))
# Plot the normalized stock prices
for ticker in tickers:
plt.plot(data_normalized[ticker], label=ticker)
# Add a title and labels
plt.title('Normalized Stock Prices (2017-2022)')
plt.xlabel('Date')
plt.ylabel('Normalized Price')
plt.legend()
# Display the plot
plt.show()
Would you like to run this code? (y/n)
もう一度 `y` を入力。
# Normalize the stock prices
data_normalized = data / data.iloc[0]
# Display the first few rows of the normalized data
data_normalized.head()
AAPL META
Date
2017-01-03 1.000000 1.000000
2017-01-04 0.998881 1.015660
2017-01-05 1.003960 1.032603
2017-01-06 1.015153 1.056050
2017-01-09 1.024451 1.068800
The stock prices for APPL (Apple Inc.) and META (Meta Platforms Inc.) have been successfully normalized. The initial price for each stock is 1, and all subsequent prices are relative to the initial price.
Finally, let's plot the normalized stock prices using matplotlib. This will allow us to visually compare the performance of the two stocks over the last 5 years.
import matplotlib.pyplot as plt
# Create a new figure
plt.figure(figsize=(14, 7))
# Plot the normalized stock prices
for ticker in tickers:
plt.plot(data_normalized[ticker], label=ticker)
# Add a title and labels
plt.title('Normalized Stock Prices (2017-2022)')
plt.xlabel('Date')
plt.ylabel('Normalized Price')
plt.legend()
# Display the plot
plt.show()
Would you like to run this code? (y/n)もう一度 `y` を入力。
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
# Create a new figure
import matplotlib.pyplot as plt
# Create a new figure
import matplotlib.pyplot as plt
# Create a new figure
plt.figure(figsize=(14, 7))
import matplotlib.pyplot as plt
# Create a new figure
plt.figure(figsize=(14, 7))
import matplotlib.pyplot as plt
# Create a new figure
plt.figure(figsize=(14, 7))
import matplotlib.pyplot as plt
# Create a new figure
plt.figure(figsize=(14, 7))
# Plot the normalized stock prices
for ticker in tickers:
plt.plot(data_normalized[ticker], label=ticker)
# Add a title and labels
plt.title('Normalized Stock Prices (2017-2022)')
plt.xlabel('Date')
plt.ylabel('Normalized Price')
plt.legend()
# Display the plot
plt.show()
█
<Figure size 1400x700 with 0 Axes>
[<matplotlib.lines.Line2D object at 0x1675058d0>]
[<matplotlib.lines.Line2D object at 0x16be2d710>]
Text(0.5, 1.0, 'Normalized Stock Prices (2017-2022)')
Text(0.5, 0, 'Date')
Text(0, 0.5, 'Normalized Price')
<matplotlib.legend.Legend object at 0x16764b310>
無事グラフが生成されました！
閉じたあとに、下記が出ました。
Here is the plot of the normalized stock prices for APPL (Apple Inc.) and META (Meta Platforms Inc.) from 2017 to 2022. As you can see, both stocks have shown significant growth over this period, but the growth has not been linear and there have been periods of
both increase and decrease.
Please note that this is a simplified analysis and does not take into account many factors that could influence the performance of these stocks. For a more comprehensive analysis, you may want to consider other factors such as earnings reports, economic indicators,
and market trends.
Is there anything else you would like to know or do?
終わりに
OpenInterpreterですが、ChatGPTのCode Interpreterと似た感覚で使え非常に便利そうでした。
これだけ流行っているのもうなずけます。
Terminal上で動くのでインプットファイルを使った編集などもかなり楽そうでした。
まだ試してませんが、Pythonで走らせることもできるみたいです。
import interpreter
interpreter.chat("Plot APPL and META's normalized stock prices") # Executes a single command
interpreter.chat() # Starts an interactive chat
Githubを見ると、他にも色々と設定できるみたいでした。
以上、お読みいただきありがとうございます。少しでも参考になればと思います。
もし今回の記事が楽しんでいただけたのであれば、note と Twitter でフォローしていただけると嬉しいです。
LLMを使ってコードが実行できる、今流行りの OpenInterpreter を試してみました https://t.co/rY9tI9npKi#note #LLM #機械学習
— alex @ very GPU-poor🥹 (@alexweberk) September 8, 2023
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