Refactor

api/api.py

@@ -19,7 +19,7 @@
 @app.get('/', response_class=HTMLResponse)
 def index(request: Request):
     bots = []
-    bot_directories = [d for d in os.listdir(bots_dir) if os.path.isdir(os.path.join(bots_dir, d)) and d!= "gpt2tuned"]
+    bot_directories = [d for d in os.listdir(bots_dir) if os.path.isdir(os.path.join(bots_dir, d))]
     for bot_dir in bot_directories:
 
         # Dynamically load the bot's module

bots/gpt2tuned/joke_bot.py

@@ -34,8 +34,8 @@ class Bot:
 
     def __init__(self):
         # Loading models
-        self.pipeline1 = pipeline("text-classification", model='bert-base-uncased')
-        self.pipeline2 = pipeline("zero-shot-classification", model='bert-base-uncased')
+        self.pipe1 = pipeline("text-classification", model='bert-base-uncased')
+        self.pipe2 = pipeline("zero-shot-classification", model='bert-base-uncased')
         # Final mark (0-10)
         self.mark = 0
 
@@ -61,13 +61,13 @@ def tell_joke(self) -> str:
     def rate_joke(self, joke) -> int:
         self.mark = 0
         print(joke)
-        result = self.pipeline1(joke)
+        result = self.pipe1(joke)
         # Logical evaluation from bert-base-uncased on Regression
         score = result[0]['score']
         logic_score_1 = round(score*10)
 
         # Logic evaluation based zero-shot classification with two parameters
-        result = self.pipeline2(joke, candidate_labels=["logical", "not logical"])['scores'][0]
+        result = self.pipe2(joke, candidate_labels=["logical", "not logical"])['scores'][0]
         logic_score_2 = round(result*10)
 
         # average count of words - https://insidegovuk.blog.gov.uk/2014/08/04/sentence-length-why-25-words-is-our-limit/

setup.py

@@ -11,7 +11,8 @@
         "transformers>=4.0",
         "textblob>=0.15.3",
         "torch>=1.8.1",
-        "pytest>=6.2.2"
+        "pytest>=6.2.2",
+        "gpt-2-simple",
     ],
 
     author="Konfuzio",