Update app.py

app.py

@@ -25,61 +25,159 @@ def topk_candidates(query, candidates, k):
     return topk
 
 def create_gradio_interface():
-    with gr.Blocks() as demo:
-        gr.Markdown("## WordLlama Gradio Demo")
+    with gr.Blocks(theme=gr.themes.Soft()) as demo:
+        gr.Markdown("""
+        # WordLlama Gradio Demo
+        
+        **WordLlama** is a fast, lightweight NLP toolkit that handles tasks like fuzzy deduplication, similarity, and ranking with minimal inference-time dependencies and is optimized for CPU hardware.
+
+        For more details, visit the [WordLlama GitHub repository](https://github.com/dleemiller/WordLlama).
+
+        ## Examples
+
+        **Calculate Similarity**
+
+        ```python
+        from wordllama import WordLlama
+
+        # Load the default WordLlama model
+        wl = WordLlama.load()
+
+        # Calculate similarity between two sentences
+        similarity_score = wl.similarity("i went to the car", "i went to the pawn shop")
+        print(similarity_score)  # Output: 0.06641249096796882
+        ```
+
+        **Rank Documents**
+
+        ```python
+        query = "i went to the car"
+        candidates = ["i went to the park", "i went to the shop", "i went to the truck", "i went to the vehicle"]
+        ranked_docs = wl.rank(query, candidates)
+        print(ranked_docs)
+        # Output:
+        # [
+        #   ('i went to the vehicle', 0.7441646856486314),
+        #   ('i went to the truck', 0.2832691551894259),
+        #   ('i went to the shop', 0.19732814982305436),
+        #   ('i went to the park', 0.15101404519322253)
+        # ]
+        ```
+
+        **Additional Inference Methods**
+
+        ```python
+        # Fuzzy Deduplication
+        wl.deduplicate(candidates, threshold=0.8)
+
+        # Clustering with K-means
+        wl.cluster(docs, k=5, max_iterations=100, tolerance=1e-4)
+
+        # Filtering Candidates
+        wl.filter(query, candidates, threshold=0.3)
+
+        # Top-k Candidates
+        wl.topk(query, candidates, k=3)
+        ```
+        """)
 
         with gr.Tab("Similarity"):
             with gr.Row():
                 sentence1 = gr.Textbox(label="Sentence 1", placeholder="Enter the first sentence here...")
                 sentence2 = gr.Textbox(label="Sentence 2", placeholder="Enter the second sentence here...")
             similarity_output = gr.Number(label="Similarity Score")
-            gr.Button("Calculate Similarity").click(
+            submit_similarity_btn = gr.Button("Calculate Similarity")
+            submit_similarity_btn.click(
                 fn=calculate_similarity,
                 inputs=[sentence1, sentence2],
                 outputs=[similarity_output]
             )
+            examples_similarity = gr.Examples(
+                examples=[
+                    ["I love programming.", "I enjoy coding."],
+                    ["The weather is sunny.", "It's a bright day."],
+                    ["I need coffee.", "I'm looking for a coffee shop."]
+                ],
+                inputs=[sentence1, sentence2],
+            )
 
         with gr.Tab("Rank Documents"):
             query = gr.Textbox(label="Query", placeholder="Enter the query here...")
             candidates = gr.Textbox(label="Candidates (comma separated)", placeholder="Enter candidate sentences here...")
             ranked_docs_output = gr.Dataframe(headers=["Document", "Score"])
-            gr.Button("Rank Documents").click(
+            submit_rank_btn = gr.Button("Rank Documents")
+            submit_rank_btn.click(
                 fn=lambda q, c: rank_documents(q, c.split(',')),
                 inputs=[query, candidates],
                 outputs=[ranked_docs_output]
             )
+            examples_rank = gr.Examples(
+                examples=[
+                    ["I went to the car", "I went to the park, I went to the shop, I went to the truck, I went to the vehicle"],
+                    ["Looking for a restaurant", "I need food, I'm hungry, I want to eat, Let's find a place to eat"],
+                    ["Best programming languages", "Python, JavaScript, Java, C++"]
+                ],
+                inputs=[query, candidates],
+            )
 
         with gr.Tab("Deduplicate Candidates"):
             candidates_dedup = gr.Textbox(label="Candidates (comma separated)", placeholder="Enter candidate sentences here...")
             threshold_dedup = gr.Slider(label="Threshold", minimum=0.0, maximum=1.0, step=0.01, value=0.8)
             deduplicated_output = gr.Textbox(label="Deduplicated Candidates")
-            gr.Button("Deduplicate").click(
+            submit_dedup_btn = gr.Button("Deduplicate")
+            submit_dedup_btn.click(
                 fn=lambda c, t: deduplicate_candidates(c.split(','), t),
                 inputs=[candidates_dedup, threshold_dedup],
                 outputs=[deduplicated_output]
             )
+            examples_dedup = gr.Examples(
+                examples=[
+                    ["apple, apple, orange, banana", 0.8],
+                    ["cat, dog, cat, bird, dog", 0.9],
+                    ["text, text, more text, text", 0.7]
+                ],
+                inputs=[candidates_dedup, threshold_dedup],
+            )
 
         with gr.Tab("Filter Candidates"):
             filter_query = gr.Textbox(label="Query", placeholder="Enter the query here...")
             candidates_filter = gr.Textbox(label="Candidates (comma separated)", placeholder="Enter candidate sentences here...")
             threshold_filter = gr.Slider(label="Threshold", minimum=0.0, maximum=1.0, step=0.01, value=0.3)
             filtered_output = gr.Textbox(label="Filtered Candidates")
-            gr.Button("Filter Candidates").click(
+            submit_filter_btn = gr.Button("Filter Candidates")
+            submit_filter_btn.click(
                 fn=lambda q, c, t: filter_candidates(q, c.split(','), t),
                 inputs=[filter_query, candidates_filter, threshold_filter],
                 outputs=[filtered_output]
             )
+            examples_filter = gr.Examples(
+                examples=[
+                    ["I went to the car", "I went to the park, I went to the shop, I went to the truck", 0.3],
+                    ["Looking for a restaurant", "I want to eat, I'm hungry, Let's find a place to eat", 0.4],
+                    ["Best programming languages", "Python, JavaScript, Java, C++", 0.5]
+                ],
+                inputs=[filter_query, candidates_filter, threshold_filter],
+            )
 
         with gr.Tab("Top-k Candidates"):
             topk_query = gr.Textbox(label="Query", placeholder="Enter the query here...")
             candidates_topk = gr.Textbox(label="Candidates (comma separated)", placeholder="Enter candidate sentences here...")
             k = gr.Slider(label="Top-k", minimum=1, maximum=10, step=1, value=3)
             topk_output = gr.Textbox(label="Top-k Candidates")
-            gr.Button("Get Top-k Candidates").click(
+            submit_topk_btn = gr.Button("Get Top-k Candidates")
+            submit_topk_btn.click(
                 fn=lambda q, c, k: topk_candidates(q, c.split(','), k),
                 inputs=[topk_query, candidates_topk, k],
                 outputs=[topk_output]
             )
+            examples_topk = gr.Examples(
+                examples=[
+                    ["I went to the car", "I went to the park, I went to the shop, I went to the truck, I went to the vehicle", 3],
+                    ["Looking for a restaurant", "I want to eat, I'm hungry, Let's find a place to eat", 2],
+                    ["Best programming languages", "Python, JavaScript, Java, C++", 4]
+                ],
+                inputs=[topk_query, candidates_topk, k],
+            )
 
     return demo