refactor: rename time_frame to period

backend/funix/decorator/__init__.py

@@ -259,41 +259,37 @@ class LimitSource(Enum):
 
 class Limiter:
     call_history: dict
-    # How many calls client can send between each interval set by `time_frame`
+    # How many calls client can send between each interval set by `period`
     max_calls: int
     # Max call interval time, in seconds
-    time_frame: int
+    period: int
     source: LimitSource
 
     def __init__(
         self,
         max_calls: int = 10,
-        time_frame: int = 60,
+        period: int = 60,
         source: LimitSource = LimitSource.SESSION,
     ):
         if type(max_calls) is not int:
             raise TypeError("type of `max_calls` is not int")
-        if type(time_frame) is not int:
-            raise TypeError("type of `time_frame` is not int")
+        if type(period) is not int:
+            raise TypeError("type of `period` is not int")
         if type(source) is not LimitSource:
             raise TypeError("type of `source` is not LimitSource")
 
         self.source = source
         self.max_calls = max_calls
-        self.time_frame = time_frame
+        self.period = period
         self.call_history = {}
 
     @staticmethod
-    def ip(max_calls: int, time_frame: int = 60):
-        return Limiter(
-            max_calls=max_calls, time_frame=time_frame, source=LimitSource.IP
-        )
+    def ip(max_calls: int, period: int = 60):
+        return Limiter(max_calls=max_calls, period=period, source=LimitSource.IP)
 
     @staticmethod
-    def session(max_calls: int, time_frame: int = 60):
-        return Limiter(
-            max_calls=max_calls, time_frame=time_frame, source=LimitSource.SESSION
-        )
+    def session(max_calls: int, period: int = 60):
+        return Limiter(max_calls=max_calls, period=period, source=LimitSource.SESSION)
 
     def rate_limit(self) -> Optional[Response]:
         call_history = self.call_history
@@ -309,12 +305,12 @@ def rate_limit(self) -> Optional[Response]:
         queue = call_history[source]
         current_time = time.time()
 
-        while len(queue) > 0 and current_time - queue[0] > self.time_frame:
+        while len(queue) > 0 and current_time - queue[0] > self.period:
             queue.popleft()
 
         if len(queue) >= self.max_calls:
             time_passed = current_time - queue[0]
-            time_to_wait = int(self.time_frame - time_passed)
+            time_to_wait = int(self.period - time_passed)
             error_message = (
                 f"Rate limit exceeded. Please try again in {time_to_wait} seconds."
             )
@@ -1454,7 +1450,7 @@ def verify_secret():
                 verify_secret_endpoint(verify_secret)
                 verify_secret_id(verify_secret)
 
-            limiters: Optional[list[Limiter]] = list() # was a none
+            limiters: Optional[list[Limiter]] = list()  # was a none
 
             if isinstance(rate_limit, Limiter):
                 limiters = list()

examples/AI/DallE.py

@@ -1,25 +1,27 @@
-import os 
+import os
 
 import openai  # pip install openai
+
 openai.api_key = os.environ.get("OPENAI_KEY")
 
 import IPython.display
 
-import funix 
+import funix
+
 
 @funix.funix(  # Funix.io, the laziest way to build web apps in Python
     title="OpenAI: Dall-E",
     description="""Generate an image with DALL-E in [Funix](http://funix.io), the minimalist way to build apps in Python. An OpenAI key needs to be set. A rate limit is applied. """,
-    rate_limit=funix.decorator.Limiter.session(max_calls=1, time_frame=60*60*24), 
+    rate_limit=funix.decorator.Limiter.session(max_calls=1, period=60 * 60 * 24),
     show_source=True,
 )
 def dalle(Prompt: str = "a cat on a red jeep") -> IPython.display.Image:
     response = openai.Image.create(prompt=Prompt, size="256x256")
     return response["data"][0]["url"]
 
 
-# **Note:** 
-# * An OpenAI key needs to be set in the environment variable OPENAI_KEY. 
+# **Note:**
+# * An OpenAI key needs to be set in the environment variable OPENAI_KEY.
 # * A rate limit of 1 call per day per browser session is set.
 
-# Like us? Please star us on [GitHub](https://github.com/TexteaInc/funix)]. 
+# Like us? Please star us on [GitHub](https://github.com/TexteaInc/funix)].

examples/AI/chatGPT_advanced.py

@@ -6,7 +6,7 @@
 import openai
 
 # openai.api_key = os.environ.get("OPENAI_KEY")
-# Disable this app from being called in public hosted examples 
+# Disable this app from being called in public hosted examples
 
 import funix
 
@@ -32,7 +32,7 @@ class PromptBox(str):
     "conditional_visible": [
         {"when": {"show_advanced_options": True}, "show": ["max_tokens", "model"]}
     ],
-    "rate_limit": funix.decorator.Limiter.session(max_calls=2, time_frame=60*60*24),
+    "rate_limit": funix.decorator.Limiter.session(max_calls=2, period=60 * 60 * 24),
 }
 
 
@@ -51,4 +51,4 @@ def ChatGPT_advanced(
         model=model,
         max_tokens=max_tokens,
     )
-    return completion["choices"][0]["message"]["content"]
+    return completion["choices"][0]["message"]["content"]

examples/AI/chatGPT_lazy.py

@@ -1,18 +1,22 @@
-import os # Python's native 
-import openai  # you cannot skip it 
+import os  # Python's native
+import openai  # you cannot skip it
 
 openai.api_key = os.environ.get("OPENAI_KEY")
 
 import funix
-@funix.funix(rate_limit=funix.decorator.Limiter.session(max_calls=2, time_frame=60*60*24))
 
-# If in lazy model, the two lines above should be commented out. 
+
+@funix.funix(
+    rate_limit=funix.decorator.Limiter.session(max_calls=2, period=60 * 60 * 24)
+)
+
+# If in lazy model, the two lines above should be commented out.
 # Lazy model means run this command
 # $ funix -l chatGPT_lazy.py
 
+
 def ChatGPT(prompt: str) -> str:
     completion = openai.ChatCompletion.create(
-        messages=[{"role": "user", "content": prompt}],
-        model="gpt-3.5-turbo"
+        messages=[{"role": "user", "content": prompt}], model="gpt-3.5-turbo"
     )
     return completion["choices"][0]["message"]["content"]

examples/AI/chatGPT_muilti_turn.py

@@ -1,10 +1,12 @@
 import os
-import openai 
+import openai
+
 openai.api_key = os.environ.get("OPENAI_KEY")
 
 import IPython
 
-messages  = []  # list of dicts, dict keys: role, content, system 
+messages = []  # list of dicts, dict keys: role, content, system
+
 
 def print_messages_html(messages):
     printout = ""
@@ -16,22 +18,20 @@ def print_messages_html(messages):
         printout += f'<div style="position: relative; left: {left}; width: 70%"><b>{name}</b>: {message["content"]}</div>'
     return printout
 
-import funix 
+
+import funix
+
+
 @funix.funix(
     description="""Multi-turn chat with ChatGPT in [Funix](http://funix.io), the minimalist way to build apps in Python. An OpenAI key needs to be set. A rate limit is applied. """,
-    rate_limit=funix.decorator.Limiter.session(max_calls=3, time_frame=60*60*24),
-    direction="column-reverse") # input is below log
-def ChatGPT_multi_turn(current_message: str)  -> IPython.display.HTML:
+    rate_limit=funix.decorator.Limiter.session(max_calls=3, period=60 * 60 * 24),
+    direction="column-reverse",
+)  # input is below log
+def ChatGPT_multi_turn(current_message: str) -> IPython.display.HTML:
     current_message = current_message.strip()
     messages.append({"role": "user", "content": current_message})
-    completion = openai.ChatCompletion.create(
-        messages=messages,
-        model='gpt-3.5-turbo' 
-    )
+    completion = openai.ChatCompletion.create(messages=messages, model="gpt-3.5-turbo")
     chatgpt_response = completion["choices"][0]["message"]["content"]
     messages.append({"role": "assistant", "content": chatgpt_response})
 
     return print_messages_html(messages)
-
-
-

examples/AI/huggingface.py

@@ -1,40 +1,42 @@
 # Building a one-turn chatbot from any causal language model hosted on HuggingFace using free Inference API
 
-# Copyleft 2023 Forrest Sheng Bao http://forrestbao.github.io 
-# The purpose of this code is to demonstrate the use of Funix to turn a simple API call function to a web app. 
+# Copyleft 2023 Forrest Sheng Bao http://forrestbao.github.io
+# The purpose of this code is to demonstrate the use of Funix to turn a simple API call function to a web app.
 
 # To turn this code into a web app, run the following command in the terminal:
 # funix huggingface.py -l # the -l flag is very important. It tells Funix to load the function as a web app.
 
-import os, json, typing # Python's native 
-import requests # pip install requests
+import os, json, typing  # Python's native
+import requests  # pip install requests
 import ipywidgets
 
 # API_TOKEN = os.getenv("HF_TOKEN") # "Please set your API token as an environment variable named HF_TOKEN. You can get your token from https://huggingface.co/settings/token"
 
 import funix
 
+
 @funix.funix(
     description="""Talk to LLMs hosted at HuggingFace. A HuggingFace token needs to be set in the environment variable HF_TOKEN.""",
-    # rate_limit=funix.decorator.Limiter.session(max_calls=20, time_frame=60*60*24),
+    # rate_limit=funix.decorator.Limiter.session(max_calls=20, period=60*60*24),
 )
 def huggingface(
     model_name: typing.Literal[
-        "gpt2", 
-        "bigcode/starcoder", 
-        "google/flan-t5-base"] = "gpt2", 
-    prompt: str = "Who is Einstein?", 
-    API_TOKEN: ipywidgets.Password = None
-    ) -> str: 
-
-    payload = {"inputs": prompt, "max_tokens":200} # not all models use this query  and output formats.  Hence, we limit the models above. 
+        "gpt2", "bigcode/starcoder", "google/flan-t5-base"
+    ] = "gpt2",
+    prompt: str = "Who is Einstein?",
+    API_TOKEN: ipywidgets.Password = None,
+) -> str:
+    payload = {
+        "inputs": prompt,
+        "max_tokens": 200,
+    }  # not all models use this query  and output formats.  Hence, we limit the models above.
 
     API_URL = f"https://api-inference.huggingface.co/models/{model_name}"
     headers = {"Authorization": f"Bearer {API_TOKEN.value}"}
 
     response = requests.post(API_URL, headers=headers, json=payload)
 
-    if "error" in response.json(): 
+    if "error" in response.json():
         return response.json()["error"]
     else:
-        return response.json()[0]["generated_text"]
+        return response.json()[0]["generated_text"]