File size: 1,590 Bytes
519938b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | import getpass
import os
# if "GROQ_API_KEY" not in os.environ:
os.environ["GROQ_API_KEY"] = "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"
from langchain_groq import ChatGroq
from langchain_openai import ChatOpenAI
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_anthropic import ChatAnthropic
from langchain.schema import AIMessage, HumanMessage, SystemMessage
import gradio as gr
system_message = "You are a helpful assistant"
llm = ChatGroq(
model="llama-3.2-3b-preview",
temperature=0,
max_tokens=None,
timeout=None,
max_retries=3,
streaming=True
)
def stream_response(message, history):
print(f"Input: {message}. History: {history}\n")
history_langchain_format = []
history_langchain_format.append(SystemMessage(content=system_message))
for human, ai in history:
history_langchain_format.append(HumanMessage(content=human))
history_langchain_format.append(AIMessage(content=ai))
if message is not None:
history_langchain_format.append(HumanMessage(content=message))
partial_message = ""
for response in llm.stream(history_langchain_format):
partial_message += response.content
yield partial_message
demo_interface = gr.ChatInterface(
stream_response,
textbox=gr.Textbox(placeholder="Send to the LLM...",
container=False,
autoscroll=True,
scale=7),
)
demo_interface.launch(share=True, debug=True) |