code cleanup

README.md

@@ -216,9 +216,9 @@ Before discussing evaluation metric let me walk you through two important pieces
 
 ### Recommendation Generation
 
-The generation is handled by script `z_hypothetical_summary.py`. Under the hood following happens
+The generation is handled by functions in script `z_hypothetical_summary.py`. Under the hood following happens
 
-![image](https://github.com/user-attachments/assets/ee174c38-a1f3-438a-afb8-be2888c590da)
+![image](.resources/eval1.png)
 
 Code Preview. I did the minimal post processing to chop of the `prompt` from the generated summaries before returning the result.
 

z_hypothetical_summary.py

@@ -11,8 +11,12 @@ TRAINED_CASUAL_MODEL = "LunaticMaestro/gpt2-book-summary-generator"
 generator_model = None 
 
 def load_model():
+    '''Work around to speed up HF cross-script loading'''
     global generator_model
-    generator_model = pipeline('text-generation', model=TRAINED_CASUAL_MODEL)
+    if generator_model is None:
+        generator_model = pipeline('text-generation', model=TRAINED_CASUAL_MODEL)
+    else: 
+        return generator_model
 
 
 def generate_summaries(book_title: str, genre: Optional[str] = None, n_samples=2, top_k = 50, top_p = 0.85, model=None) -> list[str]:
@@ -23,17 +27,13 @@ def generate_summaries(book_title: str, genre: Optional[str] = None, n_samples=2
         n_samples: (default=2) count of hypothetical summaries
         top_k: (default = 50) 
         top_p: (default=0.85)
-
         model: CASUAL LM; this is a hack to adjust for faster response in gradio
+        
     Returns: 
         summaries: list of hypothetical summaries.
     '''
-    global generator_model
-
-    if model: 
-        generator_model = model 
-    else:
-        generator_model = generator_model if generator_model is not None else load_model()
+    # select model
+    generator_model = model if model else generator_model
 
     # basic prompt very similary to one used in fine-tuning
     prompt = f'''Book Title: {book_title}

z_similarity.py

@@ -15,6 +15,8 @@ def computes_similarity_w_hypothetical(hypothetical_summaries: list[str], model
         Ranks of the books summaries based on above consine similarity Distance; Lower ranks means more similar
     '''
     global books_summaries_embs
+
+    # Select model
     model = model if model else load_model()
     
     hypothetical_summaries_embs = get_embeddings(hypothetical_summaries)