added endpoint cosine-similarity

main.py

@@ -1,3 +1,4 @@
+from sentence_transformers import SentenceTransformer, util
 from fastapi import FastAPI
 import joblib
 from sentence_transformers import SentenceTransformer
@@ -25,3 +26,44 @@ def get_dimension(message: str):
     prediction = loaded_model.predict(message_embedding)
     return {"Predicted Category": f"{prediction[0]}"}
 
+
+@app.post("/cosine-similarity")
+def calculate_cosine_similarity(sentence1:str, sentence2:str):
+
+    embeddings = model.encode([sentence1, sentence2])
+
+    cosine_sim = util.cos_sim(embeddings[0], embeddings[1])
+    # return str(cosine_sim[0])
+    return round(cosine_sim.item(), 3)
+    
+
+
+
+# Example usage:
+# sentence_a = "The cat sat on the mat."
+# sentence_b = "A feline rested on the rug."
+# similarity = calculate_cosine_similarity(sentence_a, sentence_b)
+
+# if similarity is not None:
+#     print(f"Cosine similarity between sentence_a and sentence_b: {similarity}")
+
+# sentence_c = "This is a completely different sentence."
+# similarity_ac = calculate_cosine_similarity(sentence_a, sentence_c)
+
+# if similarity_ac is not None:
+#     print(
+#         f"Cosine similarity between sentence_a and sentence_c: {similarity_ac}")
+
+
+# # Using a different model (you'll need to install it if you haven't already):
+# similarity_different_model = calculate_cosine_similarity(
+#     sentence_a, sentence_b, model_name="all-MiniLM-L6-v2")
+# if similarity_different_model is not None:
+#     print(f"Cosine similarity (different model): {similarity_different_model}")
+
+# # Example of error handling if model name is wrong
+# similarity_error = calculate_cosine_similarity(
+#     sentence_a, sentence_b, model_name="wrong-model-name")
+
+# if similarity_error is not None:
+#     print(f"Cosine similarity (wrong model name): {similarity_error}")