| import gradio as gr |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
|
|
| MODEL_NAME = "oskaralf/model_merged" |
| model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, torch_dtype="auto", device_map="auto") |
| tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) |
|
|
| def generate_response(prompt, max_length=128, temperature=0.7, top_p=0.9): |
| inputs = tokenizer(prompt, return_tensors="pt").to(model.device) |
| outputs = model.generate( |
| **inputs, |
| max_length=max_length, |
| temperature=temperature, |
| top_p=top_p, |
| pad_token_id=tokenizer.eos_token_id |
| ) |
| response = tokenizer.decode(outputs[0], skip_special_tokens=True) |
| return response |
|
|
| def interactive_app(): |
| with gr.Blocks() as app: |
| gr.Markdown("# Coding Task Generator") |
| gr.Markdown("Generate coding tasks by entering a prompt below.") |
| |
| prompt = gr.Textbox(label="Enter your prompt:", placeholder="e.g., Create a Python task involving recursion.") |
| max_length = gr.Slider(label="Max Length", minimum=16, maximum=512, value=128, step=16) |
| temperature = gr.Slider(label="Temperature", minimum=0.1, maximum=1.0, value=0.7, step=0.1) |
| top_p = gr.Slider(label="Top-p Sampling", minimum=0.1, maximum=1.0, value=0.9, step=0.1) |
| generate_button = gr.Button("Generate Task") |
| |
| output = gr.Textbox(label="Generated Task", lines=10) |
| |
| generate_button.click( |
| generate_response, |
| inputs=[prompt, max_length, temperature, top_p], |
| outputs=output |
| ) |
| |
| return app |
|
|
| if __name__ == "__main__": |
| interactive_app().launch() |
|
|
