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app.py

@@ -1,13 +1,15 @@
 
 import gradio as gr
+from peft import PeftModel
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
 
-# Load your fine-tuned model
-model_name = "Pradnya27/codegpt-finetuned-code-generation"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
+# Load LoRA model
+base_model = AutoModelForCausalLM.from_pretrained("microsoft/CodeGPT-small-py")
+model = PeftModel.from_pretrained(base_model, "Pradnya27/codegpt-lora-code-generation")
+tokenizer = AutoTokenizer.from_pretrained("microsoft/CodeGPT-small-py")
 tokenizer.pad_token = tokenizer.eos_token
-model = AutoModelForCausalLM.from_pretrained(model_name)
+model.eval()
 
 def generate_code(question):
     prompt = "Generate code: " + question
@@ -23,10 +25,8 @@ def generate_code(question):
         )
     
     generated = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    # Return only the generated part after the prompt
     return generated[len(prompt):]
 
-# Build the UI
 demo = gr.Interface(
     fn=generate_code,
     inputs=gr.Textbox(
@@ -38,8 +38,8 @@ demo = gr.Interface(
         label="Generated Code",
         language="python"
     ),
-    title="🤖 CodeGPT — AI Code Generator",
-    description="Fine-tuned CodeGPT model by Pradnya27. Ask any coding question and get Python code!",
+    title="⚡ CodeGPT LoRA — AI Code Generator",
+    description="Fine-tuned CodeGPT with LoRA by Pradnya27. 275x smaller than full fine-tuning! Ask any coding question and get Python code.",
     examples=[
         ["Write a function to reverse a string"],
         ["Write a function to find the largest number in a list"],