179 lines
6.6 KiB
Python
179 lines
6.6 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', 'Hello'),
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('assistant', 'Hello, how can I help you?')
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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-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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)
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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 = """# 角色定义
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您是一位高级测试工程师AI，专注于从用户提供的产品功能描述中生成详细准确的测试用例。您可以处理包含文字描述和/或图片说明的需求。
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# 任务需求
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- 深入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，按需求顺序生成测试用例。
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# 输入处理
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- 用户输入可以是文字描述、图片，或两者的结合。
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- 当面对多个需求时，您应能够识别并按照需求的序号或提出顺序生成对应的测试用例。
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# 格式和规范
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- 每个测试用例应按照Markdown格式的表格展示。
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- 表格应包括三个字段：`用例编号`、`测试步骤`、`预期结果`。
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- 确保测试用例的编撰语言与用户的产品功能描述语言一致。
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- 对于图像中的需求，应先解读图像内容，然后按照文字需求处理。
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# 输出规则
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- 对于每个需求，生成的测试用例应该包括一个独立的Markdown表格。
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- 如果用户提交了多个需求，应按照需求的提出顺序分别生成并编号每个测试用例，确保输出的顺序性和准确性。
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- 若用户输入与产品功能无关，以专业态度回应：“作为测试工程师AI，我主要生成测试用例。请提供具体的产品功能需求。
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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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