create app.py

app.py

@@ -0,0 +1,34 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
+
+# Load model
+model_name = "Manish014/review-summariser-gpt-config1"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+
+summarizer = pipeline("summarization", model=model, tokenizer=tokenizer)
+
+# Define Gradio UI
+def summarize_review(review):
+    if not review.strip():
+        return "Please enter a review."
+    output = summarizer(review, max_length=60, min_length=10, do_sample=False)
+    return output[0]["summary_text"]
+
+examples = [
+    ["This is the worst coffee maker I’ve ever used. It leaks water everywhere, the buttons barely work, and the brew tastes like plastic."],
+    ["Great product! Easy to use, super fast delivery, and the quality is outstanding. Highly recommend!"],
+    ["Battery life is average but display quality and sound are top-notch."],
+    ["Not worth the money. Poor customer service and the build feels cheap."]
+]
+
+demo = gr.Interface(
+    fn=summarize_review,
+    inputs=gr.Textbox(lines=6, placeholder="Paste a product review here..."),
+    outputs="text",
+    examples=examples,
+    title="Review Summariser GPT",
+    description="Enter a product review to get a helpful summary using Config1 fine-tuned T5-small model."
+)
+
+demo.launch()