Update app.py

app.py

@@ -2,9 +2,6 @@
 CLEAN_DF_UNIQUE_TITLES = "unique_titles_books_summary.csv"
 N_RECOMMENDS = 5
 
-# def get_recommendation(book_title: str) -> str:
-#     return book_title
-
 # from transformers import pipeline, set_seed
 
 # # CONST
@@ -14,19 +11,6 @@ N_RECOMMENDS = 5
 
 # generator_model = pipeline('text-generation', model=TRAINED_CASUAL_MODEL)
 
-
-
-# def sanity_check():
-#     '''Validates whether the vectors count is of same as summaries present else RAISES Error
-#     '''
-#     global BOOKS_CSV, SUMMARY_VECTORS
-#     df = get_dataframe(BOOKS_CSV)
-#     vectors = np.load(SUMMARY_VECTORS)
-#     assert df.shape[0] == vectors.shape[0]
-    
-
-# Reference: https://huggingface.co/learn/nlp-course/en/chapter9/2
-
 import gradio as gr
 from z_similarity import computes_similarity_w_hypothetical
 from z_hypothetical_summary import generate_summaries
@@ -36,10 +20,6 @@ books_df = get_dataframe(CLEAN_DF_UNIQUE_TITLES)
 
 
 def get_recommendation(book_title: str) -> dict:
-    global generator_model
-    # return "Hello"
-    # # Generate hypothetical summary
-    # value = generator_model("hello", max_length=50)
     fake_summaries = generate_summaries(book_title=book_title, n_samples=5) # other parameters are set to default in the function
     
     # Compute Simialrity 
@@ -58,31 +38,25 @@ def get_recommendation(book_title: str) -> dict:
     #
     # book_summaries: list[str] = [f"**{book}** \n {summary}" for book, summary in zip(books, summaries)]
 
-    # return response
     # Generate card-style HTML
     html = "<div style='display: flex; flex-wrap: wrap; gap: 1rem;'>"
     for book, summary in zip(books, summaries):
         html += f"""
         <div style='border: 1px solid #ddd; border-radius: 8px; padding: 1rem; width: 200px; box-shadow: 2px 2px 5px rgba(0,0,0,0.1);'>
             <h3 style='margin: 0;'>{book}</h3>
-            <p style='font-size: 0.9rem; color: #555;'>{summaries}</p>
+            <p style='font-size: 0.9rem; color: #555;'>{summary}</p>
         </div>
         """
     html += "</div>"
 
     # Club the output to be processed by gradio
-    response = [label_similarity, ] #html]
+    response = [label_similarity, html]
 
     return response
 
-    return fake_summaries[0]
-    # return str(value)
-
 # We instantiate the Textbox class
 textbox = gr.Textbox(label="Write random title", placeholder="The Man who knew", lines=2)
-# label = gr.Label(label="Result", num_top_classes=N_RECOMMENDS)
-# output = [gr.Label(label="Result", num_top_classes=N_RECOMMENDS)] + [gr.Textbox(label="Recommendation") for i in range(N_RECOMMENDS)]
-output = [gr.Label(label="Similarity"), ] # gr.HTML(label="Books Descriptions")]
+output = [gr.Label(label="Similarity"), gr.HTML(label="Books Descriptions")]
 demo = gr.Interface(fn=get_recommendation, inputs=textbox, outputs=output)
 
 demo.launch()