A simple Streamlit tool that uploads conversation files, scores AI responses on custom dimensions with justifications, keeps history, and exports results to CSV/Markdown.

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Last updated

2026-07-31

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FR-AI / ANALYSIS

Why it is worth attention

It offers a lightweight, locally runnable solution for systematically evaluating AI conversation outputs with configurable scoring criteria and persistent records.

Who it is for

  • Developers testing chatbot response quality
  • Product managers evaluating AI assistant performance
  • QA or annotation teams scoring AI outputs
  • Researchers using custom scoring rubrics

Use cases

  • Scoring AI answers from uploaded conversation logs against custom quality dimensions
  • Building a historical score database for tracking AI performance over time
  • Exporting evaluation tables to CSV or Markdown for reporting or further analysis
  • Rapidly setting up a local evaluation interface with custom model API and prompts

Strengths

  • Supports txt and docx conversation file uploads
  • Allows custom scoring dimensions and prompts
  • Automatically saves scoring history with export options
  • Simple local setup via pip and Streamlit

Considerations

  • Requires manual configuration of model API, key, and model name before use
  • Only handles txt/docx file formats
  • Described as a simple test page, not a production-grade system

README quick start

这是个简单的测试网页,是个Streamlit 工具:上传对话文件(txt/docx),对每段对话中 AI 的回答按自定义维度评分,并给出评分理由。并且评分之后,会存下历史记录,以及可以导出的csv/markdown表格,便于统计评分。

代码拉到本地后,依次运行下面的命令,即可自动打开浏览器,开始使用。

运行

pip install -r requirements.txt pip install plotly streamlit run app.py

首页上传文件评分;第一次启动需要在,「配置」页设置模型(API 地址/Key/模型名)、提示词、评分维度;「历史」页可以回看往期评分。

Description

a simply test web for a intern

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