| import gradio as gr | |
| from transformers import pipeline | |
| # Load a GPT-Neo model fine-tuned for code generation | |
| generator = pipeline('text-generation', model='EleutherAI/gpt-neo-2.7B') | |
| def generate_code(prompt): | |
| # Generate code from the model | |
| responses = generator(prompt, max_length=50, num_return_sequences=1, temperature=0.5) | |
| return responses[0]['generated_text'] | |
| # Create a Gradio Interface | |
| iface = gr.Interface( | |
| fn=generate_code, | |
| inputs=[gr.inputs.Textbox(lines=10, label="Type your code description here")], | |
| outputs=[gr.outputs.Textbox(label="Generated Code")], | |
| examples=[["Create a Python function to add two numbers"]], | |
| ) | |
| # Run the interface | |
| if __name__ == "__main__": | |
| iface.launch() | |
| #import gradio as gr | |
| #from transformers import pipeline | |
| # Load a small GPT model fine-tuned for Python code generation | |
| #generator = pipeline('text-generation', model='microsoft/CodeGPT-small-py') | |
| #def generate_code(prompt): | |
| # # Generate code from the model | |
| # responses = generator(prompt, max_length=150, num_return_sequences=1, temperature=0.5) | |
| # return responses[0]['generated_text'] | |
| # Create a Gradio Interface | |
| #iface = gr.Interface( | |
| # fn=generate_code, | |
| # inputs=[gr.inputs.Textbox(lines=10, label="Type your code description here")], | |
| # outputs=[gr.outputs.Textbox(label="Generated Code")], | |
| # examples=[["Define a Python function to calculate factorial."]], | |
| #) | |
| # Run the interface | |
| #if __name__ == "__main__": | |
| # iface.launch() | |