Create gtapp.py

gtapp.py

@@ -0,0 +1,27 @@
+import gradio as gr
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+
+# Load the T5 model and tokenizer
+model_name = "t5-base"
+tokenizer = T5Tokenizer.from_pretrained(model_name)
+model = T5ForConditionalGeneration.from_pretrained(model_name)
+
+def generate_text(input_text):
+    # Encode input text and generate output ids
+    input_ids = tokenizer.encode(input_text, return_tensors="pt")
+    output_ids = model.generate(input_ids)
+
+    # Decode output ids to get generated text
+    output_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+    return output_text
+
+# Gradio interface
+iface = gr.Interface(
+    fn=generate_text,
+    inputs=gr.inputs.Textbox(placeholder="Enter your prompt here (e.g. 'translate English to French: The weather is nice today.')"),
+    outputs=gr.outputs.Textbox(label="Generated Text"),
+    title="Text-to-Text Generation with T5",
+    description="A demo for text-to-text generation using the T5 model.",
+)
+
+iface.launch()