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import gradio as gr
from transformers import T5ForConditionalGeneration, T5Tokenizer
from textwrap import fill
# Load fine-tuned model and tokenizer
last_checkpoint = "Jyotiyadav/InsuranceModel1.0"
finetuned_model = T5ForConditionalGeneration.from_pretrained(last_checkpoint)
tokenizer = T5Tokenizer.from_pretrained(last_checkpoint)
# Define inference function
def answer_question(question):
# Format input
inputs = ["Please answer this question: " + question]
inputs = tokenizer(inputs, return_tensors="pt")
# Generate answer
outputs = finetuned_model.generate(**inputs)
answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
# Wrap answer for better display
return fill(answer, width=80)
# Create Gradio interface
iface = gr.Interface(
fn=answer_question,
inputs="text",
outputs="text",
title="Insurance Claim Prediction Using T5 Model",
description="Enter your question to get the answer.",
examples=[
["For a Male customer with an annual income of $850000, who bought a Pale White Mitsubishi Diamante (Overhead Camshaft engine) from Classic Chevy in Riga on 2022-Jan-2, priced at $12000, what was the claim amount?"],
["For a Male customer with an annual income of $13500, who bought a Pale White Chrysler Sebring Coupe (Overhead Camshaft engine) from Suburban Ford in Ventspils on 2022-Jan-3, priced at $26000, what was the claim amount?"],
["For a Male customer with an annual income of $13500, who bought a Black Lexus LS400 (Double\u00c3\u201a\u00c2\u00a0Overhead Camshaft engine) from Saab-Belle Dodge in Liepaja on 2022-Jan-12, priced at $39000, what was the claim amount?"]
]
)
# Launch Gradio interface
iface.launch(inline=True, debug=True)
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