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()