Create app.py

app.py

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+import gradio as gr
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+from peft import PeftModel
+
+# Load base model and LoRA fine-tuned model
+base_model = AutoModelForSeq2SeqLM.from_pretrained("google/byt5-small")
+model = PeftModel.from_pretrained(base_model, "rihebriri/byt5_lora_finetuned")
+tokenizer = AutoTokenizer.from_pretrained("google/byt5-small")
+
+# Define function to correct text
+def correct_text(text):
+    inputs = tokenizer(text, return_tensors="pt").input_ids
+    outputs = model.generate(inputs)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# Create Gradio interface
+iface = gr.Interface(fn=correct_text, inputs="text", outputs="text")
+
+# Launch API
+iface.launch()