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| import gradio as gr | |
| from wordllama import WordLlama | |
| # Load the default WordLlama model | |
| wl = WordLlama.load() | |
| def calculate_similarity(sentence1, sentence2): | |
| similarity_score = wl.similarity(sentence1, sentence2) | |
| return similarity_score | |
| def rank_documents(query, candidates): | |
| ranked_docs = wl.rank(query, candidates) | |
| return ranked_docs | |
| def deduplicate_candidates(candidates, threshold): | |
| deduplicated = wl.deduplicate(candidates, threshold) | |
| return deduplicated | |
| def filter_candidates(query, candidates, threshold): | |
| filtered = wl.filter(query, candidates, threshold) | |
| return filtered | |
| def topk_candidates(query, candidates, k): | |
| topk = wl.topk(query, candidates, k) | |
| return topk | |
| def create_gradio_interface(): | |
| with gr.Blocks(theme=gr.themes.Soft()) as demo: | |
| gr.markdown("# WordLlama") | |
| gr.markdown("") | |
| 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") | |
| 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"]) | |
| 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") | |
| 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") | |
| 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") | |
| 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], | |
| ) | |
| 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) | |
| ``` | |
| """) | |
| return demo | |
| # Create and launch the Gradio interface | |
| demo = create_gradio_interface() | |
| demo.launch() | |