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from langchain_openai import ChatOpenAI
from langchain.schema import AIMessage, HumanMessage
import gradio as gr
import os
key = os.getenv('key')
model = ChatOpenAI(model="gpt-4o-mini", api_key=key)
def predict(message, history):
history_langchain_format = []
for msg in history:
if msg['role'] == "user":
history_langchain_format.append(HumanMessage(content=msg['content']))
elif msg['role'] == "assistant":
history_langchain_format.append(AIMessage(content=msg['content']))
history_langchain_format.append(HumanMessage(content=message))
gpt_response = model.invoke(history_langchain_format)
return gpt_response.content
demo = gr.ChatInterface(
predict,
type="messages"
)
demo.launch()
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