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####################################################
#Mit Streaming
def predict(message, history):
history_openai_format = []
for human, assistant in history:
history_openai_format.append({"role": "user", "content": human })
history_openai_format.append({"role": "assistant", "content":assistant})
history_openai_format.append({"role": "user", "content": message})
response = openai.ChatCompletion.create(
model='gpt-3.5-turbo',
messages= history_openai_format,
temperature=1.0,
stream=True
)
partial_message = ""
for chunk in response:
if len(chunk['choices'][0]['delta']) != 0:
partial_message = partial_message + chunk['choices'][0]['delta']['content']
yield partial_message
gr.ChatInterface(predict).queue().launch()
##########################################################
#OpenAI Chatinterface
from langchain.chat_models import ChatOpenAI
from langchain.schema import AIMessage, HumanMessage
import openai
import gradio as gr
os.environ["OPENAI_API_KEY"] = "sk-..." # Replace with your key
llm = ChatOpenAI(temperature=1.0, model='gpt-3.5-turbo-0613')
def predict(message, history):
history_langchain_format = []
for human, ai in history:
history_langchain_format.append(HumanMessage(content=human))
history_langchain_format.append(AIMessage(content=ai))
history_langchain_format.append(HumanMessage(content=message))
gpt_response = llm(history_langchain_format)
return gpt_response.content
gr.ChatInterface(predict).launch() |