app.py

import gradio as gr
#import peft
import transformers

device = "cpu"
is_peft = False
model_id = "treadon/promt-fungineer-355M"
# if is_peft:
#     config = peft.PeftConfig.from_pretrained(model_id)
#     model = transformers.AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, low_cpu_mem_usage=True)
#     tokenizer = transformers.AutoTokenizer.from_pretrained(config.base_model_name_or_path)
#     model = peft.PeftModel.from_pretrained(model, model_id)
# else:
model = transformers.AutoModelForCausalLM.from_pretrained(model_id, low_cpu_mem_usage=True)
tokenizer = transformers.AutoTokenizer.from_pretrained("gpt2")

def generate_text(prompt):
    
    if not prompt.startswith("BRF:"):
        prompt = "BRF: " + prompt

    model.eval()
    # SOFT SAMPLE
    inputs = tokenizer(prompt, return_tensors="pt").to(device)
    samples = []
    try:
        for i in range(1):
            outputs = model.generate(**inputs, max_length=256, do_sample=True, top_k=100, top_p=0.95, temperature=0.85, num_return_sequences=4, pad_token_id=tokenizer.eos_token_id)
            for output in outputs:
                sample = tokenizer.decode(output, skip_special_tokens=True)
                samples.append(sample)
    except Exception as e:
        print(e)

    return samples

iface = gr.Interface(fn=generate_text, inputs="text", outputs=("text","text","text","text") )
iface.launch()