Create app.py

app.py

@@ -0,0 +1,37 @@
+import torch
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+from transformers.optimization import  Adafactor 
+import time
+import warnings
+
+model = T5ForConditionalGeneration.from_pretrained('/content/pytoch_model.bin', return_dict=True,config='t5-base-config.json')
+
+def generate(text):
+  model.eval()
+  input_ids = tokenizer.encode("WebNLG:{} </s>".format(text), return_tensors="pt")  # Batch size 1
+  # input_ids.to(dev)
+  s = time.time()
+  outputs = model.generate(input_ids)
+  gen_text=tokenizer.decode(outputs[0]).replace('<pad>','').replace('</s>','')
+  elapsed = time.time() - s
+  print('Generated in {} seconds'.format(str(elapsed)[:4]))
+
+  
+  return gen_text
+
+
+import gradio as gr
+
+
+
+# Define the Gradio interface
+iface = gr.Interface(
+    fn=generate,  # Replace with your actual function
+    inputs=gr.inputs.Textbox(lines=2, placeholder="Enter text here..."),
+    outputs=gr.outputs.Textbox(),
+    title="Text Generation App",
+    description="Enter some text and see the generated output.",
+)
+
+# Launch the Gradio interface
+iface.launch()