200 lines
8.2 KiB
Python
200 lines
8.2 KiB
Python
# ------------------------------------------
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import os
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import numpy as np
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import altair as alt
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import pandas as pd
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import streamlit as st
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import datetime
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import time
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import random
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import dashscope
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from dashscope import Generation
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from streamlit_option_menu import option_menu
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from http import HTTPStatus
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from PIL import Image
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dashscope.api_key = os.getenv("DASHSCOPE_API_KEY") # get api key from environment variable
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st.set_page_config(layout="wide", page_title='TestGenius')
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default_title = 'New Chat'
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default_messages = [('user', 'What capabilities do you offer?'),
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('assistant', 'I excel at transforming uploaded images and texts into precise, multilingual test cases, streamlining the work of Business Analysts with efficiency and accuracy.')
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]
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conversations = [{
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'id': 1,
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'title': 'Hello',
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'messages': default_messages
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}]
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def chat(user, message):
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with st.chat_message(user):
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print(user, ':', message)
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st.markdown(message)
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if 'conversations' not in st.session_state:
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st.session_state.conversations = conversations
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conversations = st.session_state.conversations
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# 当前选择的对话
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if 'index' not in st.session_state:
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st.session_state.index = 0
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AVAILABLE_MODELS = [
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"qwen-max",
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"qwen-turbo",
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]
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with st.sidebar:
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st.image('logo.png')
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st.subheader('', divider='rainbow')
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st.write('')
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llm = st.selectbox('Choose your Model', AVAILABLE_MODELS, index=0)
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# if st.button('New Chat'):
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# conversations.append({'title': default_title, 'messages': []})
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# st.session_state.index = len(conversations) - 1
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titles = []
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for idx, conversation in enumerate(conversations):
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titles.append(conversation['title'])
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option = option_menu(
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'Conversations',
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titles,
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default_index=st.session_state.index
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)
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uploaded_file = st.file_uploader("Choose a image file", type="jpg")
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if st.button("Clear Chat History"):
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st.session_state.messages.clear()
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if uploaded_file:
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image_uploaded = Image.open(uploaded_file)
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image_path = image_uploaded.filename
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def respond(prompt):
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messages = [
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{'role': 'user', 'content': prompt}]
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responses = Generation.call(model="qwen-turbo",
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messages=messages,
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result_format='message', # 设置输出为'message'格式
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stream=True, # 设置输出方式为流式输出
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incremental_output=True # 增量式流式输出
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)
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for response in responses:
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if response.status_code == HTTPStatus.OK:
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yield response.output.choices[0]['message']['content'] + " "
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else:
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yield 'Request id: %s, Status code: %s, error code: %s, error message: %s' % (
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response.request_id, response.status_code,
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response.code, response.message
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)
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def respond_nonStream(prompt, instruction):
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messages = [
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{'role': 'system', 'content': instruction},
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{'role': 'user', 'content': prompt}]
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response = Generation.call(model="qwen-max",
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messages=messages,
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result_format='message', # 设置输出为'message'格式
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temperature=1,
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top_p=0.8,
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top_k=50)
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if response.status_code == HTTPStatus.OK:
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return response.output.choices[0]['message']['content']
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else:
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return 'Request id: %s, Status code: %s, error code: %s, error message: %s' % (
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response.request_id, response.status_code,
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response.code, response.message
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)
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def respond_image(prompt, image):
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messages = [
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{'role': 'user',
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"content": [
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{"image": image},
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{"text": prompt}
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]
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}
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]
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response = dashscope.MultiModalConversation.call(model="qwen-vl-max",
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messages=messages,
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result_format='message', # 设置输出为'message'格式
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)
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if response.status_code == HTTPStatus.OK:
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return response.output.choices[0]['message']['content']
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else:
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return 'Request id: %s, Status code: %s, error code: %s, error message: %s' % (
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response.request_id, response.status_code,
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response.code, response.message
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)
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prompt = st.chat_input("Enter your Questions")
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st.session_state.messages = conversations[st.session_state.index]['messages']
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if prompt:
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if conversations[st.session_state.index]['title'] == default_title:
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conversations[st.session_state.index]['title'] = prompt[:12]
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for user, message in st.session_state.messages:
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chat(user, message)
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chat('user', prompt)
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instruction = """
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# 角色定义
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您是一位多语言测试工程师AI，你能够首先判断用户输入的语言类型并告知用户，然后根据用户提供的产品功能描述，不仅生成详细准确的测试用例，
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而且确保测试用例的编写语言与用户输入内容的语言保持一致。无论用户用何种语言（支持的范围内）描述产品功能，您都能以相同语言输出测试用例。
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# 任务需求
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- 细致分析用户提交的一个或多个产品功能需求。
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- 根据需求文本或通过解读图片中的描述，生成语言一致的测试用例。
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# 输入处理
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- 用户输入可为不同语言的文字描述、图片，或二者的结合。
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- 对于多个需求，识别并以输入的语言和顺序生成相应的测试用例。
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# 格式和规范
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- 测试用例应以Markdown格式的表格显示，包含`用例编号`、`测试步骤`及`预期结果`三栏。
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- 与用户输入的语言保持一致，确保不发生语言混淆。
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- 若需求以图片呈现，须解释图像内容，并以相同语言形式处理文字需求。
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# 输出规则
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- 针对每项产品功能需求输出一个独立的Markdown表格式测试用例。
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- 若存在多个需求，依据需求的提交顺序生成并编号每个测试用例，保证输出的序列性和准确性。
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- 对无关产品功能的输入，以专业和恰当的语言回复：“作为语言适应型测试工程师AI，我专注于生成测试用例，请您提供具体的产品功能需求，以便我以相应语言回答。”
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# 示例1
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示例
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user:“当用户点击登录按钮时，如果已正确输入用户名和密码，系统应跳转到首页。”
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assistant:
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您输入了中文的功能描述。
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| 用例编号 | 测试步骤 | 预期结果 |
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| -------- | ------------------------------------------------------ | ---------------- |
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| 1 | 1. 输入正确的用户名和密码 2. 点击登录按钮 | 系统跳转到首页 |
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# 示例2
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user: Clicking the registration button submits the data.
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assistant:
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You entered requirement in ENGLISH.
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Test cases:
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| Case Number | Test Steps | Expected Results |
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| -------- | ------------------------------------------------------ | ---------------- |
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| 1 | 1. click the registration button | data submitted |
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"""
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if uploaded_file:
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answer = respond_image(prompt, image_path)
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else:
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answer = respond_nonStream(prompt, instruction)
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st.session_state.messages.append(('user', prompt))
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st.session_state.messages.append(('assistant', answer))
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chat('assistant', answer)
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else:
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for user, message in st.session_state.messages:
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chat(user, message)
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