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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import gradio as gr
# Load the model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("sagard21/python-code-explainer")
model = AutoModelForSeq2SeqLM.from_pretrained("sagard21/python-code-explainer")
def explain_code(python_code):
# Tokenize the input code
inputs = tokenizer(python_code, return_tensors="pt", truncation=True, max_length=512)
# Generate explanation
explanation_ids = model.generate(
inputs["input_ids"],
max_length=256,
num_beams=5,
early_stopping=True
)
# Decode and return the explanation
explanation = tokenizer.decode(explanation_ids[0], skip_special_tokens=True)
return explanation
# Create the Gradio interface
demo = gr.Interface(
fn=explain_code,
inputs=gr.Code(
language="python",
label="Enter Python Code",
lines=10,
placeholder="def hello_world():\n print('Hello, world!')"
),
outputs=gr.Textbox(
label="Code Explanation",
lines=5
),
title="Python Code Explainer",
description="🔍 Enter Python code and get a natural language explanation of what it does.",
examples=[
["def add(a, b):\n return a + b"],
["for i in range(5):\n print(i)"],
["x = [i**2 for i in range(10) if i % 2 == 0]"]
]
)
# Launch the app
demo.launch() |