Update app.py

app.py

@@ -27,11 +27,17 @@ Built by **Mihir Naik** 🚀
 )
 
 
-all_MiniLM_L6_V2_model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
+try:
+    all_MiniLM_L6_V2_model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
+except Exception as e:
+    raise RuntimeError("Failed to load the all-MiniLM-L6-v2 model.") from e
 
-intfloat_e5_large_v2_model = SentenceTransformer('intfloat/e5-large-v2')
+try:
+    intfloat_e5_large_v2_model = SentenceTransformer('intfloat/e5-large-v2')
+except Exception as e:
+    raise RuntimeError("Failed to load the intfloat/e5-large-v2 model.") from e
 
-@app.get("/")
+@app.get("/", include_in_schema=False)
 def redirect_to_docs():
     """
     Redirects to the FastAPI documentation.
@@ -55,6 +61,9 @@ def generate_embeddings_all_MiniLM_L6_V2_model(sentences: List[str]):
     Returns:
         dict: A dictionary containing the embeddings as a JSON-compatible list.
     """
+    if not sentences:
+        raise ValueError("The input list of sentences must not be empty.")
+    
     embeddings = all_MiniLM_L6_V2_model.encode(sentences)
     return {"embeddings": embeddings.tolist()}  # Return embeddings as a JSON-compatible list
 
@@ -70,5 +79,9 @@ def generate_embeddings_intfloat_e5_large_v2_model(sentences: List[str]):
     Returns:
         dict: A dictionary containing the embeddings as a JSON-compatible list.
     """
+
+    if not sentences:
+        raise ValueError("The input list of sentences must not be empty.")
+    
     embeddings = intfloat_e5_large_v2_model.encode(sentences)
     return {"embeddings": embeddings.tolist()}