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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 = "AventIQ-AI/T5-small-grammar-correction"
+model = T5ForConditionalGeneration.from_pretrained(model_name)
+tokenizer = T5Tokenizer.from_pretrained(model_name)
+
+def correct_grammar(text):
+    input_text = "correct: " + text
+    inputs = tokenizer(input_text, return_tensors="pt", max_length=512, truncation=True)
+    outputs = model.generate(**inputs, max_length=512)
+    corrected_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return corrected_text
+
+# Example inputs
+examples = [
+    ["She go to the market yesterday."],
+    ["He don't like playing football."],
+    ["I has a new phone."]
+]
+
+# Gradio Interface
+with gr.Blocks() as demo:
+    gr.Markdown("# 📝 Grammar Correction System")
+    gr.Markdown("Enter a sentence with grammatical errors, and the model will correct it!")
+    
+    with gr.Row():
+        input_text = gr.Textbox(label="Enter Text", placeholder="Type a grammatically incorrect sentence here...")
+    
+    output_text = gr.Textbox(label="Corrected Text")
+    correct_button = gr.Button("Correct Grammar")
+    
+    correct_button.click(correct_grammar, inputs=[input_text], outputs=[output_text])
+    
+    gr.Examples(examples, inputs=[input_text])
+
+demo.launch()

requirements.txt

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+torch
+transformers
+gradio
+sentencepiece
+torchvision
+huggingface_hub
+pillow
+numpy