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app.py

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+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}")

requirements (1).txt

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+torch
+transformers
+altair<5