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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=500, num_return_sequences=1, temperature=0.1)
# responses = generator(prompt, max_length=50, num_return_sequences=1, temperature=0.5)
return responses[0]['generated_text']
input_textbox = gr.Textbox(label="We shall get back to you!", lines=3)
outputs = gr.Markdown()
# ex = gr.Examples(examples_list, input_textbox, examples_per_page = 50)
# demo = gr.Interface(
# fn=greet,
# inputs = [input_textbox],
# examples = examples_list,
# outputs = outputs,
# title = title,
# description="Your Python Projects Coding Companion!",
# allow_flagging="never",
# examples_per_page = 50,
# theme=gr.themes.Default(),
# )
# Create a Gradio Interface
iface = gr.Interface(
fn=generate_code,
# inputs=[gr.inputs.Textbox(lines=10, label="Type your code description here")],
inputs=input_textbox, #"text",
outputs=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()
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