app.py

import gradio as gr
from transformers import T5Tokenizer, T5ForConditionalGeneration

# Load CodeT5 model and tokenizer
tokenizer = T5Tokenizer.from_pretrained("Salesforce/codet5-base")
model = T5ForConditionalGeneration.from_pretrained("Salesforce/codet5-base")

# Function to explain code
def explain_code(code_snippet):
    if not code_snippet.strip():
        return "❗ Please enter some code."
    
    input_text = f"summarize: {code_snippet.strip()}"
    input_ids = tokenizer.encode(input_text, return_tensors="pt", truncation=True, max_length=512)
    outputs = model.generate(input_ids, max_length=150, num_beams=4, early_stopping=True)
    explanation = tokenizer.decode(outputs[0], skip_special_tokens=True)
    
    return explanation

# Gradio Interface
demo = gr.Interface(
    fn=explain_code,
    inputs=gr.Textbox(lines=15, label="Paste your code here"),
    outputs=gr.Textbox(label="Explanation"),
    title="🧠 Code Explainer using Hugging Face",
    description="This tool uses Salesforce's CodeT5 to convert your code into a human-readable explanation. Works on CPU!",
    theme="default"
)

demo.launch()