➕ refactor: summarize error message without line number

dev_gpt/options/generate/pm/pm.py

@@ -8,7 +8,7 @@ from dev_gpt.options.generate.chains.user_confirmation_feedback_loop import user
 from dev_gpt.options.generate.parser import identity_parser, json_parser, self_healing_json_parser
 from dev_gpt.options.generate.pm.task_tree_schema import TaskTree
 from dev_gpt.options.generate.prompt_factory import make_prompt_friendly
-from dev_gpt.options.generate.templates_user import generate_used_tools_prompt
+from dev_gpt.options.generate.templates_user import generate_used_apis_prompt
 from dev_gpt.options.generate.ui import get_random_employee
 
 
@@ -58,47 +58,47 @@ Description of the microservice:
                 'Request schema': context['request_schema'],
                 'Response schema': context['response_schema'],
             },
-            condition_question='Does the request schema provided include a property that represents a file?',
+            conditions = [
+                is_question_true('Does the request schema provided include a property that represents a file?'),
+            ],
             question_gen='Generate a question that requests for an example file url.',
             extension_name='Input Example',
         )
-        used_tools = self.get_used_tools(microservice_description)
+        used_apis_beside_tools = [x for x in self.get_used_apis(microservice_description) if x not in ['google_custom_search', 'gpt_3_5_turbo']]
+        for api in used_apis_beside_tools:
             microservice_description += self.user_input_extension_if_needed(
                 {
                     'Microservice description': microservice_description,
                 },
-            condition_question=f'''{
-            (f"Other than interacting with {' and '.join(used_tools)}, does the microservice interface with any additional external APIs?")
-            if used_tools else "Based on the microservice description, does the microservice interface with external APIs"
-            }''',
-            question_gen='Generate a question that asks for the endpoint of the API and an example of a request and response when interacting with the API.',
-            extension_name='Example of API usage',
+                conditions=[
+                    lambda:True
+                ],
+                question_gen=f'Generate a question that asks for the endpoint for {api} and an example of a request and response when interacting with the API.',
+                extension_name=f'Instructions for {api}',
                 post_transformation_fn=translation(from_format='api instruction',
                                                    to_format='python code snippet raw without formatting')
             )
             return microservice_description, test_description
 
     @staticmethod
-    def get_used_tools(microservice_description):
+    def get_used_apis(microservice_description):
         return ask_gpt(
-            generate_used_tools_prompt,
+            generate_used_apis_prompt,
             self_healing_json_parser,
             microservice_description=microservice_description
-        )
+        )['mentioned_apis']
 
     def user_input_extension_if_needed(
             self,
             context,
-            condition_question,
+            conditions,
             question_gen,
             extension_name,
             post_transformation_fn=None
     ):
         user_answer = get_user_input_if_needed(
             context=context,
-            conditions=[
-                is_question_true(condition_question),
-            ],
+            conditions=conditions,
             question_gen_prompt_part=question_gen,
         )
         if user_answer:

dev_gpt/options/generate/templates_user.py

@@ -548,19 +548,26 @@ Note that prompt.json must only contain one question.
 '''
 )
 
-generate_used_tools_prompt = '''\
+generate_used_apis_prompt = '''\
 Microservice description: 
 {microservice_description}
 
 Question:
-Respond with a list as JSON idicating which of the APIs [google_custom_search, gpt_3_5_turbo] are mentioned in the description.
-Example 1:
-["google_custom_search", "gpt_3_5_turbo"]
-Example 2:
-["google_custom_search"]
-Example 3:
-["gpt_3_5_turbo"]
-Example 4:
-[]
+Respond with a list as JSON indicating which web APIs (e.g. google_custom_search, gpt_3_5_turbo) are mentioned in the description.
+Positive Example 1:
+{{
+  "mentioned_apis": ["google_custom_search", "gpt_3_5_turbo"]
+}}
+Positive Example 2:
+{{
+    "mentioned_apis": ["google_custom_search"]
+}}
+Positive Example 3:
+{{
+    "mentioned_apis": ["gpt_3_5_turbo"]
+}}
+Positive Example 4:
+{{
+    "mentioned_apis": []
+}}
 '''
-

test/integration/test_generator.py

@@ -136,7 +136,8 @@ data = {{
 }}
 response = requests.post(url, headers=headers, data=data)
 assert response.status_code == 200
-print('This is the text from the audio file:', response.text)'''
+print('This is the text from the audio file:', response.text)''',
+            'use any library',
             #             f'''\
             # import openai
             # audio_file= open("/path/to/file/audio.mp3", "rb")
@@ -160,7 +161,7 @@ def test_generation_level_4(microservice_dir, mock_input_sequence):
         f'''Given an audio file (1min wav) of speech, 
 1. convert it to text using the Whisper API.
 2. Summarize the text while still maintaining the key facts.
-3. Create an audio file of the summarized text using a tts library.
+3. Create an audio file of the summarized text using via tts.
 4. Return the the audio file as base64 encoded binary.
 ''',
         str(microservice_dir),

test/unit/test_tools.py

@@ -17,9 +17,24 @@ def test_no_search():
 def test_get_used_tools(tmpdir):
     os.environ['VERBOSE'] = 'true'
     GPTSession(os.path.join(str(tmpdir), 'log.json'), model='gpt-3.5-turbo')
-    used_tools = PM.get_used_tools('''\
+    used_tools = PM.get_used_apis('''\
 This microservice listens for incoming requests and generates a fixed output of "test" upon receiving a request. \
 The response sent back to the requester includes the output as a string parameter. \
 No specific request parameters are required, and the response always follows a fixed schema with a single "output" parameter.'''
                                   )
     assert used_tools == []
+
+def test_get_used_tools_2(tmpdir):
+    os.environ['VERBOSE'] = 'true'
+    GPTSession(os.path.join(str(tmpdir), 'log.json'), model='gpt-3.5-turbo')
+    description = '''\
+This microservice accepts a 1-minute WAV audio file of speech, encoded as a base64 binary string, and performs the following tasks:
+
+1. Converts the audio file to text using the Whisper API.
+2. Summarizes the text while preserving key facts using gpt_3_5_turbo.
+3. Generates an audio file of the summarized text using a text-to-speech (TTS) library.
+4. Encodes the resulting audio file as a base64 binary string.
+
+The microservice returns the summarized text converted to audio and encoded as a base64 binary string.'''
+    used_tools = PM.get_used_apis(description)
+    assert used_tools == ['Whisper API', 'gpt_3_5_turbo', 'text-to-speech (TTS) library']