Update app.py

app.py

@@ -1,28 +1,28 @@
 import gradio as gr
 from transformers import pipeline
 
-# ✅ Fast model
+# ✅ Fast + available model
 generator = pipeline("text2text-generation", model="google/flan-t5-small")
 
 # 🧠 Prompt Template
 TEMPLATE = (
-    "You are a polite, humble, and professional customer support agent. "
-    "Respond to the following customer message:\n\n{input}\n\nReply:"
+    "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=60, do_sample=False)[0]["generated_text"]
+    response = generator(prompt, max_length=80, do_sample=False)[0]["generated_text"]
     return response.strip()
 
-# 🎛️ Gradio Interface
+# 🎛️ Interface
 iface = gr.Interface(
     fn=generate_reply,
     inputs=gr.Textbox(lines=6, label="Customer Message", placeholder="Enter complaint or question..."),
-    outputs=gr.Textbox(label="Polite Support Reply"),
-    title="⚡ Ultra-Fast Auto-Reply Generator for Customer Support",
-    description="Get polite, helpful replies in seconds using FLAN-T5-small. Built for speed + tone.",
+    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?"],
@@ -31,4 +31,4 @@ iface = gr.Interface(
     ]
 )
 
-iface.launch()
+iface.launch()