rename interpreter

examples/di/README.md

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+# Data Interpreter (DI)
+
+## What is Data Interpreter
+Data Interpreter is an agent who solves problems through codes. It understands user requirements, makes plans, writes codes for execution, and uses tools if necessary. These capabilities enable it to tackle a wide range of scenarios, please check out the examples below.
+
+## Example List
+- Data visualization
+- Machine learning modeling
+- Image background removal
+- Solve math problems
+- Receipt OCR
+- Tool usage: web page imitation
+- Tool usage: web crawling
+- Tool usage: text2image
+- Tool usage: email summarization and response
+- More on the way!
+
+Please see [here](https://docs.deepwisdom.ai/main/en/guide/use_cases/agent/interpreter/intro.html) for detailed explanation.

examples/di/crawl_webpage.py

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+# -*- encoding: utf-8 -*-
+"""
+@Date    :   2024/01/24 15:11:27
+@Author  :   orange-crow
+@File    :   crawl_webpage.py
+"""
+
+from metagpt.roles.di.data_interpreter import DataInterpreter
+
+
+async def main():
+    prompt = """Get data from `paperlist` table in https://papercopilot.com/statistics/iclr-statistics/iclr-2024-statistics/,
+    and save it to a csv file. paper title must include `multiagent` or `large language model`. *notice: print key variables*"""
+    di = DataInterpreter(use_tools=True)
+
+    await di.run(prompt)
+
+
+if __name__ == "__main__":
+    import asyncio
+
+    asyncio.run(main())

examples/di/data_visualization.py

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+import asyncio
+
+from metagpt.roles.di.data_interpreter import DataInterpreter
+
+
+async def main(requirement: str = ""):
+    di = DataInterpreter(use_tools=False)
+    await di.run(requirement)
+
+
+if __name__ == "__main__":
+    requirement = "Run data analysis on sklearn Iris dataset, include a plot"
+
+    asyncio.run(main(requirement))

examples/di/email_summary.py

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+# -*- encoding: utf-8 -*-
+"""
+@Date    :   2024/02/07 
+@Author  :   Tuo Zhou
+@File    :   email_summary.py
+"""
+import os
+
+from metagpt.roles.di.data_interpreter import DataInterpreter
+
+
+async def main():
+    email_account = "your_email_account"
+    # your password will stay only on your device and not go to LLM api
+    os.environ["email_password"] = "your_email_password"
+
+    ### Prompt for automatic email reply, uncomment to try this too ###
+    # prompt = f"""I will give you your Outlook email account ({email_account}) and password (email_password item in the environment variable). You need to find the latest email in my inbox with the sender's suffix @gmail.com and reply "Thank you! I have received your email~"""""
+
+    ### Prompt for automatic email summary ###
+    prompt = f"""I will give you your Outlook email account ({email_account}) and password (email_password item in the environment variable).
+            Firstly, Please help me fetch the latest 5 senders and full letter contents.
+            Then, summarize each of the 5 emails into one sentence (you can do this by yourself, no need to import other models to do this) and output them in a markdown format."""
+
+    di = DataInterpreter(use_tools=True)
+
+    await di.run(prompt)
+
+
+if __name__ == "__main__":
+    import asyncio
+
+    asyncio.run(main())

examples/di/imitate_webpage.py

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+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+"""
+@Time    : 2024/01/15
+@Author  : mannaandpoem
+@File    : imitate_webpage.py
+"""
+from metagpt.roles.di.data_interpreter import DataInterpreter
+
+
+async def main():
+    web_url = "https://pytorch.org/"
+    prompt = f"""This is a URL of webpage: '{web_url}' .
+Firstly, utilize Selenium and WebDriver for rendering. 
+Secondly, convert image to a webpage including HTML, CSS and JS in one go. 
+Finally, save webpage in a text file. 
+Note: All required dependencies and environments have been fully installed and configured."""
+    di = DataInterpreter(use_tools=True)
+
+    await di.run(prompt)
+
+
+if __name__ == "__main__":
+    import asyncio
+
+    asyncio.run(main())

examples/di/machine_learning.py

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+import fire
+
+from metagpt.roles.di.data_interpreter import DataInterpreter
+
+
+async def main(auto_run: bool = True):
+    requirement = "Run data analysis on sklearn Wine recognition dataset, include a plot, and train a model to predict wine class (20% as validation), and show validation accuracy."
+    di = DataInterpreter(auto_run=auto_run)
+    await di.run(requirement)
+
+
+if __name__ == "__main__":
+    fire.Fire(main)

examples/di/ml_engineer_with_tools.py

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+import asyncio
+
+from metagpt.roles.di.ml_engineer import MLEngineer
+
+
+async def main(requirement: str):
+    role = MLEngineer(auto_run=True, use_tools=True)
+    await role.run(requirement)
+
+
+if __name__ == "__main__":
+    data_path = "your_path_to_icr/icr-identify-age-related-conditions"
+    train_path = f"{data_path}/your_train_data.csv"
+    eval_path = f"{data_path}/your_eval_data.csv"
+    requirement = f"This is a medical dataset with over fifty anonymized health characteristics linked to three age-related conditions. Your goal is to predict whether a subject has or has not been diagnosed with one of these conditions.The target column is Class. Perform data analysis, data preprocessing, feature engineering, and modeling to predict the target. Report f1 score on the eval data. Train data path: {train_path}, eval data path:{eval_path}."
+    asyncio.run(main(requirement))

examples/di/ocr_receipt.py

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+from metagpt.roles.di.data_interpreter import DataInterpreter
+
+
+async def main():
+    # Notice: pip install metagpt[ocr] before using this example
+    image_path = "image.jpg"
+    language = "English"
+    requirement = f"""This is a {language} receipt image.
+    Your goal is to perform OCR on images using PaddleOCR, output text content from the OCR results and discard 
+    coordinates and confidence levels, then recognize the total amount from ocr text content, and finally save as table. 
+    Image path: {image_path}.
+    NOTE: The environments for Paddle and PaddleOCR are all ready and has been fully installed."""
+    di = DataInterpreter()
+
+    await di.run(requirement)
+
+
+if __name__ == "__main__":
+    import asyncio
+
+    asyncio.run(main())

examples/di/rm_image_background.py

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+import asyncio
+
+from metagpt.roles.di.data_interpreter import DataInterpreter
+
+
+async def main(requirement: str = ""):
+    di = DataInterpreter(use_tools=False)
+    await di.run(requirement)
+
+
+if __name__ == "__main__":
+    image_path = "/your/path/to/the/image.jpeg"
+    save_path = "/your/intended/save/path/for/image_rm_bg.png"
+    requirement = f"This is a image, you need to use python toolkit rembg to remove the background of the image and save the result. image path:{image_path}; save path:{save_path}."
+    asyncio.run(main(requirement))

examples/di/sd_tool_usage.py

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+# -*- coding: utf-8 -*-
+# @Date    : 1/11/2024 7:06 PM
+# @Author  : stellahong (stellahong@fuzhi.ai)
+# @Desc    :
+import asyncio
+
+from metagpt.roles.di.data_interpreter import DataInterpreter
+
+
+async def main(requirement: str = ""):
+    di = DataInterpreter(use_tools=True, goal=requirement)
+    await di.run(requirement)
+
+
+if __name__ == "__main__":
+    sd_url = "http://your.sd.service.ip:port"
+    requirement = (
+        f"I want to generate an image of a beautiful girl using the stable diffusion text2image tool, sd_url={sd_url}"
+    )
+
+    asyncio.run(main(requirement))

examples/di/solve_math_problems.py

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+import asyncio
+
+from metagpt.roles.di.data_interpreter import DataInterpreter
+
+
+async def main(requirement: str = ""):
+    di = DataInterpreter(use_tools=False)
+    await di.run(requirement)
+
+
+if __name__ == "__main__":
+    requirement = "Solve this math problem: The greatest common divisor of positive integers m and n is 6. The least common multiple of m and n is 126. What is the least possible value of m + n?"
+    asyncio.run(main(requirement))