ashwinR/PythonCodeExplainer

1import gradio as gr2 3from transformers import (4    AutoModelForSeq2SeqLM,5    AutoTokenizer,6    AutoConfig,7    pipeline,8)9 10model_name = "ashwinR/CodeExplainer"11 12tokenizer = AutoTokenizer.from_pretrained(model_name, padding=True)13 14model = AutoModelForSeq2SeqLM.from_pretrained(model_name)15 16config = AutoConfig.from_pretrained(model_name)17 18model.eval()19 20pipe = pipeline("summarization", model=model_name, config=config, tokenizer=tokenizer)21 22def generate_text(text_prompt):23  response = pipe(text_prompt)24  return response[0]['summary_text']25 26textbox1 = gr.Textbox(value = """27class Solution(object):28    def isValid(self, s):29        stack = []30        mapping = {")": "(", "}": "{", "]": "["}31        for char in s:32            if char in mapping:33                top_element = stack.pop() if stack else '#'34                if mapping[char] != top_element:35                    return False36            else:37                stack.append(char)38        return not stack""")39 40textbox2 = gr.Textbox()41 42if __name__ == "__main__":43    gr.Textbox("The Inference Takes about 1 min 30 seconds")44    with gr.Blocks() as demo:45        gr.Interface(fn = generate_text, inputs = textbox1, outputs = textbox2)46        with gr.Row():47            gr.Image(value = "output.jpg", label = "Sample Code for Checking if a Binary Tree is Mirrored")48            gr.Image(value = "code.jpg", label = "Sample Output Explaination in Natural language")49    demo.launch()