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import gradio as gr
from gen_ai import traditional_model,llm_model
# Functions that return class instances
def function_a(query):
return traditional_model().predict(query)
def function_b(query):
return llm_model(query)
# Function to handle user input
def handle_query(function_choice, query):
function_map = {
"FinBERT": function_a,
"Dolly Finetuned": function_b,
}
if function_choice in function_map:
result = function_map[function_choice](query)
return result.response
else:
return "Invalid selection."
# Gradio Interface
iface = gr.Interface(
fn=handle_query,
inputs=[
gr.Radio(["FinBERT", "Dolly Finetuned"], label="Select Function"),
gr.Textbox(label="Enter Query")
],
outputs=gr.Textbox(label="Response"),
title="Function Selector",
description="Select a function, enter a query, and get a response.",
# Adding footer details
article="""
**About this application:**
This tool allows users to select a function, input a query, and get a response based on the selected function.
Developed using Gradio.
"""
)
iface.launch()
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