Added Gradio UI with examples

app.py

@@ -0,0 +1,36 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
+
+# Load model and tokenizer
+model = AutoModelForSeq2SeqLM.from_pretrained("Manish014/review-summariser-gpt-config1")
+tokenizer = AutoTokenizer.from_pretrained("Manish014/review-summariser-gpt-config1")
+sentiment_pipeline = pipeline("sentiment-analysis")
+
+# Function to summarize + classify
+def summarize_and_classify(review):
+    if not review.strip():
+        return "Please enter a review.", "N/A"
+    inputs = tokenizer("summarize: " + review, return_tensors="pt", truncation=True)
+    output_ids = model.generate(inputs["input_ids"], max_length=60, min_length=10, num_beams=4)
+    summary = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+    sentiment = sentiment_pipeline(review)[0]['label']
+    return summary, sentiment
+
+# Gradio Interface
+iface = gr.Interface(
+    fn=summarize_and_classify,
+    inputs=gr.Textbox(label="📝 Enter a Product Review", lines=4, placeholder="Paste a review here..."),
+    outputs=[
+        gr.Textbox(label="📌 Generated Summary"),
+        gr.Textbox(label="💬 Sentiment")
+    ],
+    title="🧠 Review Summariser GPT + Sentiment Classifier",
+    description="Paste a product review to generate a short summary and detect sentiment using a fine-tuned T5 model.",
+    examples=[
+        ["This is hands down the best vacuum cleaner I’ve ever owned. It’s lightweight, powerful, and the battery lasts forever!"],
+        ["Product arrived broken and late. Extremely disappointed with the quality and packaging."],
+        ["Good value for the price. The headphones sound great, but the build feels a bit cheap."]
+    ]
+)
+
+iface.launch()