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| import gradio as gr | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| # Load the model and tokenizer | |
| model_name = "Salesforce/codegen-350M-mono" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelForCausalLM.from_pretrained(model_name) | |
| # Function to translate Python code to JavaScript | |
| def translate_code(python_code): | |
| inputs = tokenizer(python_code, return_tensors="pt") | |
| outputs = model.generate(inputs["input_ids"], max_length=150, num_return_sequences=1) | |
| translated_code = tokenizer.decode(outputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=True) | |
| return translated_code | |
| # Gradio interface | |
| interface = gr.Interface( | |
| fn=translate_code, | |
| inputs="text", | |
| outputs="text", | |
| title="Python to JavaScript Code Translator", | |
| description="Paste your Python code, and it will be translated into JavaScript.", | |
| ) | |
| # Launch the app | |
| interface.launch() | |