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import gradio as gr
from transformers import pipeline
# β
Load FLAN-T5 model for polite, instruction-based replies
generator = pipeline("text2text-generation", model="google/flan-t5-small")
# π Instruction template
TEMPLATE = (
"You are a helpful, polite, and professional customer support agent. "
"Reply to this customer message in a brand-consistent tone:\n\n"
"{input}"
)
# π Reply Generator
def generate_reply(user_input):
prompt = TEMPLATE.format(input=user_input)
response = generator(prompt, max_length=150, do_sample=False)[0]["generated_text"]
return response.strip()
# ποΈ Gradio Interface
iface = gr.Interface(
fn=generate_reply,
inputs=gr.Textbox(lines=6, label="Customer Message", placeholder="Enter complaint or question..."),
outputs=gr.Textbox(label="Auto-Generated Support Reply"),
title="π€ Auto-Reply Generator for Customer Support",
description=(
"Generate fast, polite, and professional replies to customer queries using Google's FLAN-T5 model. "
"Perfect for CRM bots, helpdesk automation, and ticket response."
),
examples=[
["I still haven't received my order and it's been 10 days."],
["My refund hasn't been processed yet."],
["Your app keeps crashing on my iPhone."],
["Great service, just wanted to say thanks!"]
]
)
iface.launch()
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