add comments to simple app stream

app_simple_stream/app/chains.py

@@ -14,7 +14,7 @@ tokenizer = AutoTokenizer.from_pretrained(model_id)
 llm = HuggingFaceEndpoint(
     repo_id="meta-llama/Meta-Llama-3-8B-Instruct",
     huggingfacehub_api_token=os.environ['HF_TOKEN'],
-    max_new_tokens=512,
+    max_new_tokens=512, # Response will not exceed 512 words/tokens
     stop_sequences=[tokenizer.eos_token],
     streaming=True,
 )

app_simple_stream/app/database.py

@@ -1,12 +0,0 @@
-from sqlalchemy import create_engine
-from sqlalchemy.ext.declarative import declarative_base
-from sqlalchemy.orm import sessionmaker
-
-SQLALCHEMY_DATABASE_URL = "sqlite:///./test.db"
-
-engine = create_engine(
-    SQLALCHEMY_DATABASE_URL, connect_args={"check_same_thread": False}
-)
-SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
-
-Base = declarative_base()

app_simple_stream/test.py

@@ -1,7 +1,11 @@
 from langserve import RemoteRunnable
 # Hit our enpoint with specified rout
 url = "https://simafarazi-backend-c.hf.space/simple/"
-chain = RemoteRunnable(url)
-stream = chain.stream(input={'question':'How are you?'})
-for chunk in stream:
-    print(chunk, end="", flush=True)
+chain = RemoteRunnable(url) #Client for iteracting with LangChain runnables that are hosted as LangServe endpoints
+stream = chain.stream(input={'question':'How are you?'}) # .stream() and .invoke() are standard methods to interact with hosted runnables
+
+
+for chunk in stream: # Each chunk corresponds to a token/word
+     #end="": prints worlds one after each other, an not in a separate lines
+     #flush=True: prints world to the screen immidiately without any buffer
+     print(chunk, end="", flush=True)