Delete app.py

app.py

@@ -1,31 +0,0 @@
-import streamlit as st
-from transformers import pipeline, AutoModel, AutoTokenizer
-
-# Load the model and tokenizer
-model_name = "sentence-transformers/all-MiniLM-L6-v2"
-model = AutoModel.from_pretrained(model_name)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-# Create a Streamlit app
-st.title("Sentence Similarity App")
-
-# Ask the user for input
-user_input = st.text_area("Enter a sentence:")
-
-# Define a function to calculate sentence similarity
-def calculate_similarity(input_text):
-    similarity_pipeline = pipeline("feature-extraction", model=model, tokenizer=tokenizer)
-    user_embedding = similarity_pipeline(input_text)[0]
-    return user_embedding
-
-if user_input:
-    # Calculate similarity with a reference sentence
-    reference_sentence = "Hugging Face is an AI research organization."
-    user_embedding = calculate_similarity(user_input)
-    reference_embedding = calculate_similarity(reference_sentence)
-
-    # Calculate cosine similarity
-    similarity_score = round(float(user_embedding.dot(reference_embedding.T)), 4)
-
-    # Display the similarity score
-    st.write(f"Similarity Score with Reference Sentence: {similarity_score}")