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import gradio as gr
from wordllama import WordLlama
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np
# Load the default WordLlama model
wl = WordLlama.load()
# Initialize TF-IDF vectorizer
tfidf_vectorizer = TfidfVectorizer()
def calculate_similarities(sentence1, sentence2):
# WordLlama similarity
wordllama_score = wl.similarity(sentence1, sentence2)
# TF-IDF similarity
tfidf_matrix = tfidf_vectorizer.fit_transform([sentence1, sentence2])
tfidf_score = cosine_similarity(tfidf_matrix[0:1], tfidf_matrix[1:2])[0][0]
return {
"WordLlama Similarity": float(wordllama_score),
"TF-IDF Similarity": float(tfidf_score)
}
# Extended examples with more diverse sentences
examples = [
["I went to the car", "I went to the pawn shop"],
["The cat is on the roof", "A dog is in the yard"],
["She loves playing tennis", "She enjoys sports"],
["This is a bright day", "It's a sunny morning"],
["I bought a new phone", "I got a new mobile"],
["The restaurant serves delicious food", "This place has great cuisine"],
["Python is a programming language", "Java is used for coding"],
["The movie was entertaining", "I enjoyed watching the film"],
["Climate change affects our planet", "Global warming is a serious issue"],
["Students study in the library", "People read books in the library"]
]
# Define Gradio interface with updated layout
with gr.Blocks() as iface:
gr.Markdown("# Text Similarity Comparison")
gr.Markdown("Compare sentences using both WordLlama and TF-IDF similarity metrics")
with gr.Row():
sentence1 = gr.Textbox(lines=2, placeholder="Enter first sentence...", label="First Sentence")
sentence2 = gr.Textbox(lines=2, placeholder="Enter second sentence...", label="Second Sentence")
button = gr.Button("Calculate Similarities")
with gr.Row():
wordllama_output = gr.Number(label="WordLlama Similarity")
tfidf_output = gr.Number(label="TF-IDF Similarity")
button.click(
calculate_similarities,
inputs=[sentence1, sentence2],
outputs=[wordllama_output, tfidf_output]
)
gr.Markdown("### Example Sentence Pairs")
gr.Examples(
examples=examples,
inputs=[sentence1, sentence2]
)
iface.launch(share=True) |