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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() |