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59 lines
2.6 KiB
Python
59 lines
2.6 KiB
Python
import os
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from dev_gpt.apis.gpt import GPTSession
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from dev_gpt.options.generate.pm.pm import PM
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from dev_gpt.options.generate.tools.tools import get_available_tools
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def test_all_tools():
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tool_lines = get_available_tools().split('\n')
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assert len(tool_lines) == 2
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def test_no_search():
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os.environ['GOOGLE_API_KEY'] = ''
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tool_lines = get_available_tools().split('\n')
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assert len(tool_lines) == 1
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def test_get_used_apis(init_gpt):
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used_apis = PM.get_used_apis('''\
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This microservice listens for incoming requests and generates a fixed output of "test" upon receiving a request. \
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The response sent back to the requester includes the output as a string parameter. \
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No specific request parameters are required, and the response always follows a fixed schema with a single "output" parameter.'''
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)
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assert used_apis == []
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def test_get_used_apis_2(init_gpt):
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description = '''\
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This microservice accepts a 1-minute WAV audio file of speech, encoded as a base64 binary string, and performs the following tasks:
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1. Converts the audio file to text using the Whisper API.
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2. Summarizes the text while preserving key facts using gpt_3_5_turbo.
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3. Generates an audio file of the summarized text using a text-to-speech (TTS) library.
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4. Encodes the resulting audio file as a base64 binary string.
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The microservice returns the summarized text converted to audio and encoded as a base64 binary string.'''
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used_apis = PM.get_used_apis(description)
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assert used_apis == ['Whisper API', 'gpt_3_5_turbo', 'text-to-speech (TTS) library']
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def test_get_used_apis_3(init_gpt):
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description = '''\
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This microservice takes a PDF file as input and returns a summarized text output. \
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It uses PDF parsing and natural language processing tools to generate the summary, \
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and applies post-processing techniques to improve its quality. \
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The input parameter is the PDF file, \
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and the output parameter is the summarized text.'''
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used_apis = PM.get_used_apis(description)
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assert used_apis == []
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def test_get_used_apis_4(init_gpt):
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description = '''\
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This microservice receives a tweet as input \
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and identifies passive aggressive language using natural language processing techniques. \
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It then generates a positive version of the tweet using a text processing tool such as GPT-3. \
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The positive version of the tweet is returned as output. \
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The input tweet should be provided as a base64 encoded string \
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and the output positive tweet will also be returned as a base64 encoded string.'''
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used_apis = PM.get_used_apis(description)
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assert used_apis == ['GPT-3']
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