Create app.py

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# Load the model and tokenizer
+model_name = 'abinayam/gpt-2-tamil'
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+def correct_text(input_text):
+    # Tokenize the input text
+    input_ids = tokenizer.encode(input_text, return_tensors='pt')
+    
+    # Generate corrected text
+    with torch.no_grad():
+        output = model.generate(input_ids, max_length=100, num_return_sequences=1, temperature=0.7)
+    
+    # Decode the generated text
+    corrected_text = tokenizer.decode(output[0], skip_special_tokens=True)
+    
+    return corrected_text
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=correct_text,
+    inputs=gr.Textbox(lines=5, placeholder="Enter Tamil text here..."),
+    outputs=gr.Textbox(label="Corrected Text"),
+    title="Tamil Spell Corrector and Grammar Checker",
+    description="This app uses the 'abinayam/gpt-2-tamil' model to correct spelling and grammar in Tamil text.",
+)
+
+# Launch the app
+iface.launch()