updated app.py

app.py

@@ -1,3 +1,16 @@
 import gradio as gr
+from transformers import T5Tokenizer, T5ForConditionalGeneration
 
-gr.load("kurry/models/t5_small_finetuned").launch()
+def generate_text(input_text):
+    # Load pre-trained model and tokenizer
+    model_name = 'kurry/t5_small_finetuned'
+    model = T5ForConditionalGeneration.from_pretrained(model_name)
+    tokenizer = T5Tokenizer.from_pretrained(model_name)
+
+    # Generate summary
+    inputs = tokenizer.encode("summarize: " + input_text, return_tensors="pt", truncation=True)
+    outputs = model.generate(inputs)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+iface = gr.Interface(fn=generate_text, inputs='text', outputs='text')
+iface.launch()