.

Author: ChristianOellers

3rd-party/openai-api/run.py

@@ -1,6 +1,5 @@
 ##
-#
-#
+# API access snippet.
 #
 # Source origin
 # - DeepLearning.ai / OpenAI course: https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers
@@ -13,6 +12,9 @@
 _ = load_dotenv(find_dotenv())
 openai.api_key = os.getenv('OPENAI_API_KEY')
 
+
+# ---------------------------------------------------------------------------------------- FUNCTIONS
+
 def get_completion(user_prompt, model="gpt-3.5-turbo"):
     messages = [{"role": "user", "content": user_prompt}]
     ai_response = openai.ChatCompletion.create(
@@ -22,6 +24,9 @@ def get_completion(user_prompt, model="gpt-3.5-turbo"):
     )
     return ai_response.choices[0].message["content"]
 
+
+# -------------------------------------------------------------------------------------------- TESTS
+
 prompt = "Name five reasons to become a developer"
 result = get_completion(prompt)
 

projects/spaceshooter-item-detection/run.py

@@ -12,9 +12,7 @@
 from sklearn.compose import ColumnTransformer
 
 
-# ------------------------------------------------------------------------------
-
-# DATA
+# --------------------------------------------------------------------------------------------- DATA
 
 # Raw values
 # - Index = 0 is also expected output
@@ -29,16 +27,12 @@
 ])
 
 
-# ------------------------------------------------------------------------------
-
-# TRANSFORM
+# ---------------------------------------------------------------------------------------- TRANSFORM
 
 # - Raw data index 1-3 (ignore index 0)
 # - Raw data index 0
 inputs = data[:, 1:]
 outputs = data[:, 0].astype(int)
-#print(inputs)
-#print(outputs)
 
 
 # Strings become integers (here: Asteroid=0, Particle=1)
@@ -52,13 +46,11 @@
     transformers=[("encoder", OneHotEncoder(), [0])],
     remainder="passthrough"
 )
-inputs = np.array(ct.fit_transform(inputs), dtype=float)
-#print(inputs)
 
+inputs = np.array(ct.fit_transform(inputs), dtype=float)
 
-# ------------------------------------------------------------------------------
 
-# MODEL
+# -------------------------------------------------------------------------------------------- MODEL
 
 # Hidden layers = Between in/out, not visible/usable?
 # - 8 neurons with 4 inputs
@@ -78,33 +70,28 @@
 model.fit(inputs, outputs, epochs=1000, batch_size=2)
 
 
-# ------------------------------------------------------------------------------
-# PREDICTIONS
+# -------------------------------------------------------------------------------------- PREDICTIONS
 
 def predict_collision(category: str, pos_x_item: int, pos_x_player: int):
     # Convert the input data into a NumPy array
     input_data = np.array([[category, pos_x_item, pos_x_player]])
-    #print(input_data)
 
     # Transform the input data using the ColumnTransformer
     input_data_transformed = ct.transform(input_data).astype(float)
-    #print(input_data_transformed)
 
     # Use the model to predict the probability of a collision
     prediction = model.predict(input_data_transformed)
-    #print(prediction)
 
     return prediction[0][0] > 0.5
 
 
-# ------------------------------------------------------------------------------
-# TESTS
+# -------------------------------------------------------------------------------------------- TESTS
 
 # Test model with examples
-print(predict_collision("ASTEROID", 100, 100))  # Expected output: True
-print(predict_collision("ASTEROID", 0, 100))    # Expected output: False
-print(predict_collision("ASTEROID", 100, 0))    # Expected output: False
-print(predict_collision("PARTICLE", 100, 100))  # Expected output: False
-print(predict_collision("PARTICLE", 0, 100))    # Expected output: False
-print(predict_collision("PARTICLE", 100, 0))    # Expected output: False
-print(predict_collision("ASTEROID", 0, 0))    # Expected output: True
+print(predict_collision("ASTEROID", 100, 100)) # Expected output: True
+print(predict_collision("ASTEROID", 0, 100))   # Expected output: False
+print(predict_collision("ASTEROID", 100, 0))   # Expected output: False
+print(predict_collision("PARTICLE", 100, 100)) # Expected output: False
+print(predict_collision("PARTICLE", 0, 100))   # Expected output: False
+print(predict_collision("PARTICLE", 100, 0))   # Expected output: False
+print(predict_collision("ASTEROID", 0, 0))     # Expected output: True