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import gradio as gr | |
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM | |
from huggingface_hub import HfApi | |
# Get the latest model from your space | |
api = HfApi() | |
space_name = "umut-bozdag/humanizer_model" # Replace with your actual space name | |
model_files = api.list_repo_files(space_name) | |
model_file = next(file for file in model_files if file.endswith('.bin')) | |
model_revision = api.get_repo_info(space_name).sha | |
# Load the model and tokenizer from the space | |
tokenizer = AutoTokenizer.from_pretrained(space_name, revision=model_revision) | |
model = AutoModelForSeq2SeqLM.from_pretrained(space_name, revision=model_revision) | |
def generate_text(input_text): | |
# Preprocess input text | |
input_text = input_text.strip() | |
# Prepare input for the model | |
input_ids = tokenizer.encode(input_text, return_tensors="pt", max_length=256, truncation=True) | |
# Generate text with parameters matching your training setup | |
outputs = model.generate( | |
input_ids, | |
max_length=256, | |
num_return_sequences=1, | |
no_repeat_ngram_size=2, | |
top_k=30, | |
top_p=0.9, | |
temperature=0.7, | |
do_sample=True, | |
early_stopping=True, | |
num_beams=4 | |
) | |
# Decode and clean up the generated text | |
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
return generated_text.strip() | |
iface = gr.Interface( | |
fn=generate_text, | |
inputs=gr.Textbox(lines=5, label="Input Text"), | |
outputs=gr.Textbox(label="Generated Text"), | |
title="Text Humanizer", | |
description="Enter text to generate a more human-like version." | |
) | |
iface.launch() |