fix: updated temp scale to be 0-2; some type hints

widgets/advanced.py

@@ -80,7 +80,7 @@ def betterChatGptSentiment(prompt: str) -> str:
 
 
 # convert sentiment score to label
-def convertSentimentToLabel(sentiment):
+def convertSentimentToLabel(sentiment: float) -> str:
     # map the sentiment to a human readable label
     if sentiment >= 0.75:
         return "Very Positive"
@@ -99,15 +99,15 @@ def convertSentimentToLabel(sentiment):
 
 
 # analyze the sentiment of a string of text
-def analyzeBasicSentiment(text):
+def analyzeBasicSentiment(text: str) -> dict[str, float]:
     if not text:
         return ""
 
     # use VADER to get the +/- sentiment of the string
     return analyzer.polarity_scores(text)
 
 
-def getNRCEmotion(text):
+def getNRCEmotion(text: str) -> list[tuple[str, float]]:
     emotion = NRCLex(text)
 
     return emotion.top_emotions

widgets/config.py

@@ -1,57 +1,62 @@
 import ipywidgets as widgets
 
-openAiHeader = widgets.Label('OpenAI API', style=dict(font_size="1.2rem", font_weight="bold"))
-hackerNewsHeader = widgets.Label('Hacker News API', style=dict(font_size="1.2rem", font_weight="bold"))
+openAiHeader = widgets.Label(
+    "OpenAI API", style=dict(font_size="1.2rem", font_weight="bold")
+)
+hackerNewsHeader = widgets.Label(
+    "Hacker News API", style=dict(font_size="1.2rem", font_weight="bold")
+)
 
 apiKeyInput = widgets.Text(
-  value='',
-  placeholder='Enter your OpenAI API key',
-  description='API Key',
+    value="",
+    placeholder="Enter your OpenAI API key",
+    description="API Key",
 )
 
 apiKeyUpdateButton = widgets.Button(
-  description='Update Key',
-  disabled=False,
-  button_style='primary' if not apiKeyInput.value else 'danger',
+    description="Update Key",
+    disabled=False,
+    button_style="primary" if not apiKeyInput.value else "danger",
 )
 
 modelDropdown = widgets.Dropdown(
-  options=['gpt-3.5-turbo', 'gpt-4'],
-  value='gpt-3.5-turbo',
-  description='Model',
+    options=["gpt-3.5-turbo", "gpt-4"],
+    value="gpt-3.5-turbo",
+    description="Model",
 )
 
 temperatureSlider = widgets.FloatSlider(
-  value=0,
-  min=0,
-  max=1,
-  step=0.01,
-  description='Temp',
-  continuous_update=False,
-  orientation='horizontal',
-  readout=True,
-  readout_format='.2f',
+    value=0,
+    min=0,
+    max=2,
+    step=0.01,
+    description="Temp",
+    continuous_update=False,
+    orientation="horizontal",
+    readout=True,
+    readout_format=".2f",
 )
 
 sampleSizeSlider = widgets.IntSlider(
-  value=1,
-  min=1,
-  max=100,
-  step=1,
-  description='Samples',
-  continuous_update=False,
-  orientation='horizontal',
-  readout=True,
-  readout_format='d'
+    value=1,
+    min=1,
+    max=100,
+    step=1,
+    description="Samples",
+    continuous_update=False,
+    orientation="horizontal",
+    readout=True,
+    readout_format="d",
 )
 
 sampleSizeWarningLabelPrefix = widgets.Label(
-  value='Warning:',
-  style=dict(font_weight='bold')
+    value="Warning:", style=dict(font_weight="bold")
 )
 
 sampleSizeWarningLabelContent = widgets.Label(
-  value='Increasing sample size will result in higher costs.'
+    value="Increasing sample size will result in higher costs."
 )
 
-sampleSizeWarningLabel = widgets.HBox([sampleSizeWarningLabelPrefix, sampleSizeWarningLabelContent])
+sampleSizeWarningLabel = widgets.HBox(
+    [sampleSizeWarningLabelPrefix, sampleSizeWarningLabelContent]
+)

widgets/tokens.py

@@ -2,56 +2,60 @@
 
 import tiktoken
 
-MODEL = 'gtp-3.5-turbo'
+MODEL = "gtp-3.5-turbo"
 
-def configureModel(model):
-  global MODEL
-  MODEL = model
 
-def tokenize(text):
-  tokens = []
-  ids = []
+def configureModel(model: str):
+    global MODEL
+    MODEL = model
 
-  # To get the tokeniser corresponding to a specific model in the OpenAI API:
-  encoding = tiktoken.encoding_for_model(MODEL)
 
-  tokenized = encoding.encode(text)
+def tokenize(text: str) -> (list[str], list[int]):
+    tokens = []
+    ids = []
 
-  for tokenId in tokenized:
-    ids.append(tokenId)
-    tokens.append(encoding.decode_single_token_bytes(tokenId).decode('utf-8'))
+    # To get the tokeniser corresponding to a specific model in the OpenAI API:
+    encoding = tiktoken.encoding_for_model(MODEL)
 
-  return (tokens, ids)
+    tokenized = encoding.encode(text)
 
-def getTokens(change):
-  (tokens, ids) = tokenize(change['new'].strip())
+    for tokenId in tokenized:
+        ids.append(tokenId)
+        tokens.append(encoding.decode_single_token_bytes(tokenId).decode("utf-8"))
 
-  if tokens:
-    with tokenCount:
-      tokenCount.clear_output(wait=True)
-      print(f"{len(tokens)} tokens")
+    return (tokens, ids)
 
-    with tokenIds:
-      tokenIds.clear_output(wait=True)
-      print(f"{ids}")
 
-    with tokenAnalysis:
-      tokenAnalysis.clear_output(wait=True)
-      print(f"{tokens}")
-  else:
-    tokenCount.clear_output()
-    tokenIds.clear_output()
-    tokenAnalysis.clear_output()
+def getTokens(change: dict[str, str]):
+    (tokens, ids) = tokenize(change["new"].strip())
+
+    if tokens:
+        with tokenCount:
+            tokenCount.clear_output(wait=True)
+            print(f"{len(tokens)} tokens")
+
+        with tokenIds:
+            tokenIds.clear_output(wait=True)
+            print(f"{ids}")
+
+        with tokenAnalysis:
+            tokenAnalysis.clear_output(wait=True)
+            print(f"{tokens}")
+    else:
+        tokenCount.clear_output()
+        tokenIds.clear_output()
+        tokenAnalysis.clear_output()
+
 
 tokenAnalysis = widgets.Output()
 tokenIds = widgets.Output()
 tokenCount = widgets.Output()
 
 tokenString = widgets.Text(
-    value='',
-    placeholder='Type something',
+    value="",
+    placeholder="Type something",
 )
 
-tokenString.observe(getTokens, names='value')
+tokenString.observe(getTokens, names="value")
 
-tokenAnalysisWidget = widgets.VBox([tokenString, tokenCount, tokenAnalysis, tokenIds])
+tokenAnalysisWidget = widgets.VBox([tokenString, tokenCount, tokenAnalysis, tokenIds])