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import gradio as gr
from llm.qa_agent import QnAAgent
from llm.calculator_agent import CalculatorAgent
from llm.orchestrator import Orchestrator
if __name__ == "__main__":
orchestrator = Orchestrator()
qna_agent = QnAAgent()
calculator_agent = CalculatorAgent()
# question = input("Question - ")
def get_answer(question:str) -> [str, str]:
api_name, parameters = orchestrator.get_API_call(question)
print(f"Using the {api_name} Agent")
print(api_name, parameters)
if api_name == "QnA":
answer, wiki_page = qna_agent.get_answer(parameters)
# elif api_name == "calculator":
# operand, op1, op2 = parameters.split(",")
# answer = calculator_agent.calculate(operand, op1, op2)
print(answer)
return [answer, wiki_page]
demo = gr.Interface(
fn=get_answer,
inputs=gr.Textbox(placeholder="Enter your question...[Who won the Cricket World Cup in 2023?]")
,
# outputs=[gr.Textbox(label=f'Document {i+1}') for i in range(TOP_K)],
outputs=[gr.Textbox(label="Answer"), gr.Textbox(label="Wikipedia Page")],
title="Current Event Question Answering using near-real-time data",
description="Ask questions about current events beyond the LLM's knowledge cutoff. Ex: Who is the current Prime Minister of France?"
)
demo.launch()
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