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import gradio as gr
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch
# Load smaller CodeT5 model (faster)
model_name = "Salesforce/codet5-small"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
# Main function
def eternos_debugger(code, error):
if not code.strip():
return "❌ Please provide some code."
if not error.strip():
return "❌ Please provide the error message you encountered."
# Smart prompting
prompt = (
f"You are an expert Python debugger.\n"
f"Given the buggy code and the error message, fix the code.\n\n"
f"Code:\n{code}\n\n"
f"Error:\n{error}\n\n"
f"Corrected Code:"
)
inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=512)
with torch.no_grad():
outputs = model.generate(
**inputs,
max_length=512,
num_beams=4,
early_stopping=True,
temperature=0.7,
top_p=0.95
)
fixed_code = tokenizer.decode(outputs[0], skip_special_tokens=True)
return fixed_code.strip()
# Gradio UI
interface = gr.Interface(
fn=eternos_debugger,
inputs=[
gr.Textbox(label="🐞 Buggy Code", lines=12, placeholder="Paste your Python code here..."),
gr.Textbox(label="🚨 Error Message", lines=3, placeholder="Paste the error message you got...")
],
outputs=gr.Code(label="✅ Suggested Fixed Code"),
title="🛠️ Eternos: AI Code Debugger",
description="Eternos uses a CodeT5 model to help debug and fix Python code. Provide your code and the error message to get a fix.",
theme="soft",
allow_flagging="never",
examples=[
[
"def add_numbers(a, b)\n return a + b",
"SyntaxError: expected ':'"
],
[
"for i in range(5)\n print(i)",
"SyntaxError: expected ':'"
],
[
"def divide(a, b):\n return a / b\nprint(divide(4, 0))",
"ZeroDivisionError: division by zero"
]
]
)
interface.launch()
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