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File size: 778 Bytes
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from langchain.schema import AIMessage, HumanMessage
import gradio as gr
from langchain_community.llms import Ollama
llm = Ollama(model="llama3:8b", timeout=1000)
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))
try:
caht_response = llm.invoke(history_langchain_format)
except Exception as e:
caht_response = "Error: " + str(e)
return caht_response
def run():
demo = gr.ChatInterface(predict)
demo.launch(server_name="0.0.0.0", server_port=7860)
if __name__ == "__main__":
run()
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