| import gradio as gr |
| from transformers import pipeline |
|
|
| |
| MODEL_ID = "Nebulixlabs/Nutral-Reasoning" |
|
|
| print("Loading model...") |
| generator = pipeline("text-generation", model=MODEL_ID) |
| print("Model loaded successfully!") |
|
|
| def generate_text(prompt, max_length, temperature): |
| results = generator( |
| prompt, |
| max_new_tokens=max_length, |
| temperature=temperature, |
| do_sample=True if temperature > 0 else False |
| ) |
| return results[0]['generated_text'] |
|
|
| with gr.Blocks() as demo: |
| gr.Markdown(f"# Nutral Reasoning") |
| |
| with gr.Row(): |
| with gr.Column(): |
| input_text = gr.Textbox(lines=4, label="Input Prompt", placeholder="Type something here...") |
| max_tokens = gr.Slider(minimum=10, maximum=512, value=100, step=10, label="Max New Tokens") |
| temp = gr.Slider(minimum=0.1, maximum=1.0, value=0.7, step=0.1, label="Temperature") |
| submit_btn = gr.Button("Generate") |
| |
| with gr.Column(): |
| output_text = gr.Textbox(lines=6, label="Generated Text") |
| |
| submit_btn.click( |
| fn=generate_text, |
| inputs=[input_text, max_tokens, temp], |
| outputs=output_text |
| ) |
|
|
| demo.launch() |