Updated app.py

app.py

@@ -1,3 +1,29 @@
+# Load model directly
 import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
-gr.load("models/12345deena/t5-small-ilct5").launch()
+tokenizer = AutoTokenizer.from_pretrained("12345deena/t5-small-ilct5")
+model = AutoModelForSeq2SeqLM.from_pretrained("12345deena/t5-small-ilct5")
+
+def summarize(text):
+    # Tokenize input text
+    inputs = tokenizer.encode("summarize: " + text, return_tensors="pt", max_length=512, truncation=True)
+
+    # Generate summary
+    summary_ids = model.generate(inputs, max_length=150, min_length=40, length_penalty=2.0, num_beams=4, early_stopping=True)
+
+    # Decode the summary
+    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+    return summary
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=summarize,
+    inputs="text",
+    outputs="text",
+    title="Abstractive Text Summarization",
+    description="Enter a piece of text to summarize it."
+)
+
+# Launch the interface on port 8888
+iface.launch()