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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()
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