Update app.py

app.py

@@ -1,42 +0,0 @@
-from sentence_transformers import SentenceTransformer, util
-import gradio as gr
-
-# Load the model
-model = SentenceTransformer('sartifyllc/AviLaBSE')
-
-# Function to compute similarities
-def compute_similarity(original_sentence, sentences_to_compare):
-    # Encode the original sentence and the sentences to compare
-    embeddings_original = model.encode([original_sentence], convert_to_tensor=True)
-    embeddings_sentences_to_compare = model.encode(sentences_to_compare, convert_to_tensor=True)
-
-    # Compute cosine similarities
-    similarities = util.cos_sim(embeddings_original, embeddings_sentences_to_compare)
-
-    # Prepare results
-    results = []
-    for i, sentence in enumerate(sentences_to_compare):
-        results.append((sentence, similarities[0][i].item()))
-
-    return results
-
-# Interface using Gradio
-def sentence_similarity(original_sentence, sentence1, sentence2, sentence3):
-    sentences_to_compare = [sentence1, sentence2, sentence3]
-    results = compute_similarity(original_sentence, sentences_to_compare)
-    return results
-
-iface = gr.Interface(
-    fn=sentence_similarity,
-    inputs=[
-        gr.Textbox(label="Original Sentence"),
-        gr.Textbox(label="Sentence 1"),
-        gr.Textbox(label="Sentence 2"),
-        gr.Textbox(label="Sentence 3")
-    ],
-    outputs=gr.Table(label="Similarity Scores", headers=["Sentence", "Similarity Score"]),
-    title="Sentence Similarity Calculator",
-    description="Enter an original sentence and three sentences to compare their similarity."
-)
-
-iface.launch()