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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() as demo:
gr.Markdown("## WordLlama Gradio Demo")
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(
fn=calculate_similarity,
inputs=[sentence1, sentence2],
outputs=[similarity_output]
)
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(
fn=lambda q, c: rank_documents(q, c.split(',')),
inputs=[query, candidates],
outputs=[ranked_docs_output]
)
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(
fn=lambda c, t: deduplicate_candidates(c.split(','), t),
inputs=[candidates_dedup, threshold_dedup],
outputs=[deduplicated_output]
)
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(
fn=lambda q, c, t: filter_candidates(q, c.split(','), t),
inputs=[filter_query, candidates_filter, threshold_filter],
outputs=[filtered_output]
)
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(
fn=lambda q, c, k: topk_candidates(q, c.split(','), k),
inputs=[topk_query, candidates_topk, k],
outputs=[topk_output]
)
return demo
# Create and launch the Gradio interface
demo = create_gradio_interface()
demo.launch()
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