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| # app.py | |
| import gradio as gr | |
| from handler import generate_text | |
| def infer(prompt, max_tokens, temperature, top_p, top_k, repetition_penalty, trim_output): | |
| return generate_text( | |
| prompt, | |
| max_tokens=max_tokens, | |
| temperature=temperature, | |
| top_p=top_p, | |
| top_k=top_k, | |
| repetition_penalty=repetition_penalty, | |
| trim_output=trim_output | |
| ) | |
| iface = gr.Interface( | |
| fn=infer, | |
| inputs=[ | |
| gr.Textbox(label="Prompt", placeholder="Type your prompt here...", lines=4), | |
| gr.Slider(50, 512, step=10, value=250, label="Max Tokens"), | |
| gr.Slider(0.1, 1.5, step=0.1, value=0.7, label="Temperature"), | |
| gr.Slider(0.1, 1.0, step=0.05, value=0.95, label="Top-p (nucleus sampling)"), | |
| gr.Slider(0, 100, step=1, value=50, label="Top-k"), | |
| gr.Slider(0.5, 2.0, step=0.1, value=1.2, label="Repetition Penalty"), | |
| gr.Checkbox(label="Trim Prompt From Output", value=True) | |
| ], | |
| outputs=gr.Textbox(label="Generated Text", lines=10), | |
| title="🧠 Your Custom AI Text Generator", | |
| description="Using Hugging Face inference API with your own model (no token required)" | |
| ) | |
| if __name__ == "__main__": | |
| iface.launch() | |