Update app.py

app.py

@@ -1,28 +1,33 @@
+from z_utils import get_dataframe
+
 # CONST
 CLEAN_DF_UNIQUE_TITLES = "unique_titles_books_summary.csv"
 N_RECOMMENDS = 5
 
-# from transformers import pipeline, set_seed
+from transformers import pipeline, set_seed
 
 # # CONST
 # set_seed(42)
-# TRAINED_CASUAL_MODEL = "LunaticMaestro/gpt2-book-summary-generator"
+TRAINED_CASUAL_MODEL = "LunaticMaestro/gpt2-book-summary-generator"
 
 
-# generator_model = pipeline('text-generation', model=TRAINED_CASUAL_MODEL)
+if gr.NO_RELOAD:
+    # Load store books
+    books_df = get_dataframe(CLEAN_DF_UNIQUE_TITLES)
 
-import gradio as gr
+    generator_model = pipeline('text-generation', model=TRAINED_CASUAL_MODEL)
 
-if gr.NO_RELOAD:
-    from z_similarity import computes_similarity_w_hypothetical
-    from z_hypothetical_summary import generate_summaries
-    
-from z_utils import get_dataframe
+import gradio as gr
 
-books_df = get_dataframe(CLEAN_DF_UNIQUE_TITLES)
+# if gr.NO_RELOAD:
+#     from z_similarity import computes_similarity_w_hypothetical
+#     from z_hypothetical_summary import generate_summaries
 
 
 def get_recommendation(book_title: str) -> str:
+    global generator_model
+    output = generator_model("Love")
+    return str(output)
     fake_summaries = generate_summaries(book_title=book_title, n_samples=5) # other parameters are set to default in the function
     
     return fake_summaries[0]