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import gradio as gr
import os
from huggingface_hub import InferenceClient
# Get the token from the "HF_TOKEN" environment variable
token = os.getenv("HF_TOKEN")
# Create a client for the Salesforce/codet5-large model using the token
client = InferenceClient("Salesforce/codet5-large", token=token)
def generate_code(
task_description,
max_tokens,
temperature,
top_p,
):
# 2. Create a prompt using task description
prompt = task_description
# 3. Generate code based on the description
response = client.text_generation(
prompt,
max_new_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
)
# Since the response is already a string, just return it
generated_code = response
return generated_code
# 4. Create Gradio interface
with gr.Blocks() as demo:
gr.Markdown("# 🚀 CodeT5 Code Generator")
with gr.Row():
task_input = gr.Textbox(
lines=3, placeholder="Describe the task in natural language...", label="Task Description"
)
with gr.Row():
max_tokens = gr.Slider(1, 2048, value=100, step=1, label="Max Tokens")
temperature = gr.Slider(0.1, 4.0, value=0.7, step=0.1, label="Temperature")
top_p = gr.Slider(0.1, 1.0, value=0.95, step=0.05, label="Top-p")
with gr.Row():
submit_button = gr.Button("Generate Code 🚀")
output = gr.Textbox(lines=10, label="Generated Code")
# 5. Button click triggers code generation
submit_button.click(
generate_code,
inputs=[task_input, max_tokens, temperature, top_p],
outputs=output,
)
if __name__ == "__main__":
demo.launch()
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