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| import os | |
| import requests | |
| import gradio as gr | |
| import torch | |
| from transformers import ( | |
| RobertaForSequenceClassification, | |
| RobertaTokenizer, | |
| RobertaConfig, | |
| ) | |
| HF_TOKEN = os.environ["HF_TOKEN"] | |
| os.system( | |
| "wget https://openaipublic.azureedge.net/gpt-2/detector-models/v1/detector-base.pt" | |
| ) | |
| config = RobertaConfig.from_pretrained("roberta-base") | |
| model = RobertaForSequenceClassification(config) | |
| model.load_state_dict(torch.load("detector-base.pt")["model_state_dict"]) | |
| tokenizer = RobertaTokenizer.from_pretrained("roberta-base") | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| model.to(device) | |
| def call_inference_api(query): | |
| url = "https://api-inference.huggingface.co/models/roberta-base-openai-detector" | |
| headers = {"Authorization": f"Bearer {HF_TOKEN}"} | |
| response = requests.post(url, json={"inputs": query}, headers=headers) | |
| code = response.status_code | |
| if code == 200: | |
| fake, real = response.json()[0] | |
| fake_score = fake["score"] | |
| real_score = real["score"] | |
| return f"Fake: {fake_score:.2%} | Real: {real_score:.2%}" | |
| else: | |
| error = response.json()["error"] | |
| warning = response.json()["warnings"] | |
| return f"Error: {error} | Warning: {warning}" | |
| def local_call(query): | |
| # Copied from https://github.com/openai/gpt-2-output-dataset/tree/master/detector#L35-L46 | |
| tokens = tokenizer.encode(query) | |
| all_tokens = len(tokens) | |
| tokens = tokens[: tokenizer.max_len - 2] | |
| used_tokens = len(tokens) | |
| tokens = torch.tensor( | |
| [tokenizer.bos_token_id] + tokens + [tokenizer.eos_token_id] | |
| ).unsqueeze(0) | |
| mask = torch.ones_like(tokens) | |
| with torch.no_grad(): | |
| logits = model(tokens.to(device), attention_mask=mask.to(device))[0] | |
| probs = logits.softmax(dim=-1) | |
| fake, real = probs.detach().cpu().flatten().numpy().tolist() | |
| return f"Fake: {fake:.2%} | Real: {real:.2%} | Used tokens: {used_tokens} | All tokens: {all_tokens}" | |
| def main_function(query): | |
| hosted_output = call_inference_api(query) | |
| local_output = local_call(query) | |
| return hosted_output, local_output | |
| text_input = gr.Textbox( | |
| lines=5, | |
| label="Enter text to compare output with the model hosted here: https://huggingface.co/roberta-base-openai-detector", | |
| ) | |
| hosted_output = gr.Textbox(label="Output from model hosted on Hugging Face") | |
| local_output = gr.Textbox( | |
| label="Output from model running locally on transformers 2.0.0, tokenizers 0.7.0, and torch 1.4.0" | |
| ) | |
| description = "The original repository for the model used an older version of \ | |
| transformers, tokenziers, and torch which results in slightly different results \ | |
| compared to the model hosted on Hugging Face. This app compares the two models." | |
| demo = gr.Interface( | |
| fn=main_function, | |
| inputs="text", | |
| outputs=[hosted_output, local_output], | |
| title="Compare OpenAI detector models", | |
| description=description, | |
| ) | |
| demo.launch() | |