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| import gradio as gr | |
| from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM | |
| def load_model(): | |
| model_name = "Salesforce/codet5-small" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelForSeq2SeqLM.from_pretrained(model_name) | |
| return pipeline("summarization", model=model, tokenizer=tokenizer) | |
| def explain_code(files): | |
| explainer = load_model() | |
| explanations = [] | |
| for file in files: | |
| filename = file.name if hasattr(file, 'name') else 'Uploaded File' | |
| if isinstance(file, bytes): | |
| code = file.decode('utf-8') | |
| elif hasattr(file, 'read'): | |
| code = file.read().decode('utf-8') | |
| else: | |
| code = str(file) | |
| lines = code.splitlines() | |
| line_explanations = [] | |
| for idx, line in enumerate(lines): | |
| if not line.strip(): | |
| continue | |
| input_length = len(line.split()) | |
| max_length = max(1, input_length // 2) | |
| result = explainer(line, max_length=max_length, do_sample=False)[0].get('summary_text', '') | |
| line_explanations.append(f"Line {idx+1}: {line}\nExplanation: {result.strip()}\n") | |
| explanations.append(f"## {filename}\n" + "\n".join(line_explanations)) | |
| return "\n\n".join(explanations) | |
| def main(): | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# CodeExplainerBot (Local CodeT5-small)") | |
| code_input = gr.File(label="Upload code files", file_count="multiple", type="binary") | |
| output = gr.Textbox(label="Line-by-line Explanation", lines=20) | |
| explain_btn = gr.Button("Explain Code") | |
| explain_btn.click(fn=explain_code, inputs=[code_input], outputs=output) | |
| demo.launch() | |
| if __name__ == "__main__": | |
| main() | |