Update app.py

app.py

@@ -21,27 +21,11 @@ db = Chroma(persist_directory="./chroma_langchain_db")
 # Load the document, split it into chunks, embed each chunk and load it into the vector store.
 #raw_documents = TextLoader('state_of_the_union.txt').load()
 def embed_fn(inp):
-    print("Try Embeddings")
-    print(inp)
-    print("End Embeddings")
-    #for eaa in inp:
     text_splitter = CharacterTextSplitter(chunk_size=200, chunk_overlap=10)
-    #documents = text_splitter.split_documents([eaa])
     documents = text_splitter.split_text(inp)
-    print("documents")
-    print(documents)
-    print("end documents")
     out_emb= hf.embed_documents(documents)
-    #chain = history[:-1]
     string_representation = dumps(out_emb, pretty=True)
-    print(string_representation)
-    #db = Chroma(collection_name="test1", embedding_function=HuggingFaceEmbeddings())
     db.from_texts(documents,HuggingFaceEmbeddings(model_name=emb))
-    #from_documents(documents, HuggingFaceEmbeddings)
-    print("DB")
-    print(db)
-    print("end DB")
-    #return db
 def proc_doc(doc_in):
     for doc in doc_in:
         if doc.endswith(".txt"):