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app.py

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+import gradio as gr
+from transformers import T5ForConditionalGeneration, T5Tokenizer
+
+# Load model and tokenizer
+model_name = "vennify/t5-base-grammar-correction"
+print("Loading model...")
+tokenizer = T5Tokenizer.from_pretrained(model_name)
+model = T5ForConditionalGeneration.from_pretrained(model_name)
+print("Model loaded.")
+
+def correct_text(text):
+    if not text.strip():
+        return ""
+    # "vennify/t5-base-grammar-correction" requires "grammar: " prefix
+    inputs = tokenizer("grammar: " + text, return_tensors="pt")
+    
+    # Generate prediction
+    outputs = model.generate(
+        **inputs, 
+        num_beams=5,
+        max_length=128
+    )
+    
+    corrected = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return corrected
+
+# Simple Gradio interface
+iface = gr.Interface(
+    fn=correct_text,
+    inputs=gr.Textbox(lines=5, placeholder="Enter text with grammar errors..."),
+    outputs=gr.Textbox(label="Corrected text"),
+    title="Grammar Correction API",
+    description="A simple API for the local Windows grammar autocorrect."
+)
+
+iface.launch()

requirements.txt

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+transformers
+torch
+sentencepiece
+gradio