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import gradio as gr | |
from transformers import pipeline | |
# Load conversational model | |
generator = pipeline("text-generation", model="microsoft/DialoGPT-medium") | |
# Template for polite brand-consistent tone | |
TEMPLATE = ( | |
"You are a helpful and professional customer support agent. " | |
"Respond politely and concisely to the following customer complaint or question:\n\n" | |
"Customer: {input}\nSupport:" | |
) | |
def generate_reply(user_input): | |
prompt = TEMPLATE.format(input=user_input) | |
response = generator(prompt, max_length=100, do_sample=True, top_k=50, top_p=0.95)[0]['generated_text'] | |
# Extract only the agent's reply | |
support_response = response.split("Support:")[-1].strip() | |
return support_response | |
iface = gr.Interface( | |
fn=generate_reply, | |
inputs=gr.Textbox(lines=6, placeholder="Enter customer message here..."), | |
outputs=gr.Textbox(label="Auto-Generated Reply"), | |
title="🤖 Auto-Reply Generator for Customer Support", | |
description="Generate polite and brand-consistent replies to customer questions using AI." | |
) | |
iface.launch() | |