app.py

import gradio as gr
from transformers import pipeline

# ✅ Fast + available model
generator = pipeline("text2text-generation", model="google/flan-t5-small")

# 🧠 Prompt Template
TEMPLATE = (
    "You are a polite and professional customer support agent. "
    "Please respond to the customer's message:\n\n{input}"
)

# 🔁 Generate Reply
def generate_reply(user_input):
    prompt = TEMPLATE.format(input=user_input)
    response = generator(prompt, max_length=80, do_sample=False)[0]["generated_text"]
    return response.strip()

# 🎛️ Interface
iface = gr.Interface(
    fn=generate_reply,
    inputs=gr.Textbox(lines=6, label="Customer Message", placeholder="Enter complaint or question..."),
    outputs=gr.Textbox(label="Support Reply"),
    title="⚡ Fast Auto-Reply Generator for Customer Support",
    description="Generate fast, polite, and professional replies to customer messages using Google's FLAN-T5.",
    examples=[
        ["I still haven't received my order and it's been 10 days."],
        ["Why was I charged twice for my subscription?"],
        ["Thanks for the quick response yesterday!"],
        ["My login isn't working since the update."]
    ]
)

iface.launch()