🧪4️⃣ test: level 4 gpt fix

test/integration/test_generator.py

@@ -0,0 +1,177 @@
+import os
+
+import pytest
+
+from src.options.generate.generator import Generator
+
+
+# The cognitive difficulty level is determined by the number of requirements the microservice has.
+
+def test_generation_level_0(tmpdir):
+    """
+    Requirements:
+    coding challenge: ❌
+    pip packages: ❌
+    environment: ❌
+    GPT-3.5-turbo: ❌
+    APIs: ❌
+    Databases: ❌
+    """
+    os.environ['VERBOSE'] = 'true'
+    generator = Generator(
+        "The microservice is very simple, it does not take anything as input and only outputs the word 'test'",
+        str(tmpdir),
+        'gpt-3.5-turbo'
+    )
+    assert generator.generate() == 0
+
+
+
+
+def test_generation_level_1(tmpdir):
+    """
+    Requirements:
+    coding challenge: ❌
+    pip packages: ❌
+    environment: ❌
+    GPT-3.5-turbo: ✅ (for summarizing the text)
+    APIs: ❌
+    Databases: ❌
+    """
+    os.environ['VERBOSE'] = 'true'
+    generator = Generator(
+        '''Input is a tweet that might contain passive aggressive language. The output is the positive version of that tweet.
+Example tweet: 
+\'When your coworker microwaves fish in the break room... AGAIN. 🐟🤢 
+But hey, at least SOMEONE's enjoying their lunch. #officelife\'''',
+        str(tmpdir),
+        'gpt-3.5-turbo'
+    )
+    assert generator.generate() == 0
+
+
+def test_generation_level_2(tmpdir):
+    """
+    Requirements:
+    coding challenge: ❌
+    pip packages: ✅ (pdf parser)
+    environment: ❌
+    GPT-3.5-turbo: ✅ (for summarizing the text)
+    APIs: ❌
+    Databases: ❌
+    """
+    os.environ['VERBOSE'] = 'true'
+    generator = Generator(
+        "The input is a PDF like https://www.africau.edu/images/default/sample.pdf and the output the summarized text (50 words).",
+        str(tmpdir),
+        'gpt-3.5-turbo'
+    )
+    assert generator.generate() == 0
+
+def test_generation_level_3(tmpdir):
+    """
+    Requirements:
+    coding challenge: ✅ (calculate the average closing price)
+    pip packages: ❌
+    environment: ❌
+    GPT-3.5-turbo: ✅ (for processing the text)
+    APIs: ✅ (financial data API)
+    Databases: ❌
+    """
+    os.environ['VERBOSE'] = 'true'
+    generator = Generator(
+        f'''The input is a stock symbol (e.g., AAPL for Apple Inc.). 
+1. Fetch stock data (open, high, low, close, volume) for the past 30 days using a financial data API Yahoo Finance.
+2. Calculate the average closing price over the 30 days.
+3. Generate a brief summary of the company's stock performance over the past 30 days, including the average closing price and the company name.
+4. Return the summary as a string.
+Example input: 'AAPL'
+''',
+        str(tmpdir),
+        'gpt-3.5-turbo'
+    )
+    assert generator.generate() == 0
+
+def test_generation_level_4(tmpdir):
+    """
+    Requirements:
+    coding challenge: ❌
+    pip packages: ✅ (text to speech)
+    environment: ✅ (tts library)
+    GPT-3.5-turbo: ✅ (summarizing the text)
+    APIs: ✅ (whisper for speech to text)
+    Databases: ❌
+    """
+    os.environ['VERBOSE'] = 'true'
+    generator = Generator(
+        f'''Given an audio file (1min wav) of speech, 
+1. convert it to text using the Whisper API.
+Here is the documentation on how to use the API:
+import requests
+url = "https://transcribe.whisperapi.com"
+headers = {{
+'Authorization': 'Bearer {os.environ['WHISPER_API_KEY']}'
+}}
+data = {{
+  "url": "URL_OF_STORED_AUDIO_FILE"
+}}
+response = requests.post(url, headers=headers, data=data)
+assert response.status_code == 200
+print('This is the text from the audio file:', response.json()['text'])
+2. Summarize the text (~50 words) while still maintaining the key facts.
+3. Create an audio file of the summarized text using a tts library.
+4. Return the the audio file as base64 encoded binary.
+Example input file: https://www.signalogic.com/melp/EngSamples/Orig/ENG_M.wav
+''',
+        str(tmpdir),
+        'gpt-4'
+    )
+    assert generator.generate() == 0
+
+
+def test_generation_level_5(tmpdir):
+    """
+    Requirements:
+    coding challenge: ✅ (putting text on the image)
+    pip packages: ✅ (Pillow for image processing)
+    environment: ✅ (image library)
+    GPT-3.5-turbo: ✅ (for writing the joke)
+    APIs: ✅ (scenex for image description)
+    Databases: ❌
+    """
+    os.environ['VERBOSE'] = 'true'
+    generator = Generator(f'''
+The input is an image.
+Use the following api to get the description of the image:
+Request:
+curl "https://us-central1-causal-diffusion.cloudfunctions.net/describe" \\
+  -H "x-api-key: token {os.environ['SCENEX_API_KEY']}" \\
+  -H "content-type: application/json" \\
+  --data '{{"data":[
+  {{"image": "<image url here>", "features": []}}
+  ]}}'
+Result format:
+{{
+    "result": [
+    {{
+    "text": "<image description>"
+    }}
+  ]
+}}
+The description is then used to generate a joke.
+The joke is the put on the image.
+The output is the image with the joke on it.
+Example input image: https://upload.wikimedia.org/wikipedia/commons/thumb/4/47/PNG_transparency_demonstration_1.png/560px-PNG_transparency_demonstration_1.png
+''',
+                          str(tmpdir),
+                          'gpt-3.5-turbo'
+                          )
+    assert generator.generate() == 0
+
+@pytest.fixture
+def tmpdir():
+    return 'microservice'
+
+
+# further ideas:
+# Create a wrapper around google called Joogle. It modifies the page summary preview text of the search results to insert the word Jina as much as possible.