asdf

app.py

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+from transformers import T5Tokenizer, T5ForConditionalGeneration
+import torch
+import gradio as gr
+
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+model = T5ForConditionalGeneration.from_pretrained("vennify/t5-base-grammar-correction")
+tokenizer = T5Tokenizer.from_pretrained("vennify/t5-base-grammar-correction")
+model.to(device)
+model.eval()
+
+
+def generate_text(text):
+    text = f'grammar: {text}'
+    input_ids = tokenizer(
+        text, return_tensors="pt"
+    ).input_ids 
+    input_ids = input_ids.to(device)
+
+    outputs = model.generate(input_ids)
+
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+
+with gr.Blocks() as deeplearning:
+    with gr.Row():
+        with gr.Column():
+            text = gr.inputs.Textbox(lines=10, placeholder="Enter your text here...")
+            button = gr.Button(label="Correct")
+            output = gr.outputs.Textbox(label="Corrected Text")
+        
+            
+        button.click(generate_text, inputs=text, outputs=output)
+            
+
+deeplearning.launch()

requirements.txt

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+transformers
+torch