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README.md

@@ -156,6 +156,12 @@ The values of TOP_P and TOP_K (i.e. token sampling for our generator model) are
 
 MRR = 0.311 implies that there's a good change that the target book will be in rank (1/.311) ~ 3 (third rank) **i.e. within top 5 recommendations**
 
+## Inference View
+
+1. I rewrote the snippets from `z_evaluate.py` to `app.py` with minor changes to expriment with view. 
+2. To make the HF space faster (previously: 234s; **NOW: 15s**) I added `gr.NO_RELOAD` context block in the module files when loading embedding and generation model.
+  Reference: https://www.gradio.app/guides/developing-faster-with-reload-mode
+
 
 
 

app.py

@@ -53,22 +53,14 @@ def get_recommendation(book_title: str) -> dict:
     summaries = df_ranked["summaries"].to_list()[:N_RECOMMENDS]
     scores = similarity[ranks][:N_RECOMMENDS]
 
-    output_data_model: list[dict] = [ {"Book": b, "Similarity Score": s, "Description": d} for b, s, d in zip(books, summaries, scores)]
+    # label wise similarity 
+    label_similarity: dict = {book: score for book, score in zip(books, scores)}
+    #
+    book_summaries: list[str] = [f"**{book}** \n {summary}" for book, summary in zip(books, summaries)]
 
+    response = [label_similarity, ] + book_summaries
+    return response
     # Generate card-style HTML
-    html = "<div style='display: flex; flex-wrap: wrap; gap: 1rem;'>"
-    for rec in output_data_model:
-        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;'>{rec['Book']}</h3>
-            <p style='margin: 0.5rem 0;'>Similarity Score: {rec['Similarity Score']}</p>
-            <p style='font-size: 0.9rem; color: #555;'>{rec['Description']}</p>
-        </div>
-        """
-    html += "</div>"
-    return html
-
-    return {book: score for book, score in zip(books, scores)} # referene: https://huggingface.co/docs/hub/en/spaces-sdks-gradio
 
     return fake_summaries[0]
     # return str(value)
@@ -76,7 +68,7 @@ def get_recommendation(book_title: str) -> dict:
 # 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.HTML(label="Recommended Books")
+output = [gr.Label(label="Result", num_top_classes=N_RECOMMENDS)] + [gr.Textbox(label="Recommendation") for i in range(N_RECOMMENDS)]
 
 demo = gr.Interface(fn=get_recommendation, inputs=textbox, outputs=output)