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| import gradio as gr | |
| import torch | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| import os | |
| # Get the Hugging Face token from the environment variable | |
| HF_TOKEN = os.environ.get("HF_TOKEN") | |
| # Load the tokenizer and model | |
| tokenizer = AutoTokenizer.from_pretrained('EleutherAI/GPT-NeoX-20B', use_auth_token=HF_TOKEN) | |
| model = AutoModelForCausalLM.from_pretrained('skylersterling/SentimentGPT', use_auth_token=HF_TOKEN) | |
| model.eval() | |
| model.to('cpu') | |
| # Define the function that generates text from a prompt | |
| def generate_text(prompt, temperature=0.1, top_p=0.2, max_tokens=3): | |
| prompt_with_eos = prompt + " >" # Add the "EOS" to the end of the prompt | |
| input_tokens = tokenizer.encode(prompt_with_eos, return_tensors='pt') | |
| print(prompt_with_eos) | |
| input_tokens = input_tokens.to('cpu') | |
| generated_text = prompt_with_eos # Start with the initial prompt plus "EOS" | |
| prompt_length = len(generated_text) | |
| for _ in range(max_tokens): # Adjust the range to control the number of tokens generated | |
| with torch.no_grad(): | |
| outputs = model(input_tokens) | |
| predictions = outputs.logits[:, -1, :] / temperature | |
| sorted_logits, sorted_indices = torch.sort(predictions, descending=True) | |
| cumulative_probs = torch.cumsum(torch.softmax(sorted_logits, dim=-1), dim=-1) | |
| sorted_indices_to_remove = cumulative_probs > top_p | |
| sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone() | |
| sorted_indices_to_remove[..., 0] = 0 | |
| indices_to_remove = sorted_indices[sorted_indices_to_remove] | |
| predictions[:, indices_to_remove] = -float('Inf') | |
| next_token = torch.multinomial(torch.softmax(predictions, dim=-1), 1) | |
| input_tokens = torch.cat((input_tokens, next_token), dim=1) | |
| decoded_token = tokenizer.decode(next_token.item()) | |
| generated_text += decoded_token # Append the new token to the generated text | |
| if decoded_token == "<": # Stop if the end of sequence token is generated | |
| break | |
| # Check if the generated text contains "0" or "1" and return the appropriate sentiment message | |
| if "1" in generated_text: | |
| return "The sentiment is positive." | |
| elif "0" in generated_text: | |
| return "The sentiment is negative." | |
| else: | |
| return "Invalid" | |
| # Create a Gradio interface with a text input, sliders for temperature and top_p, and a text output | |
| interface = gr.Interface( | |
| fn=generate_text, | |
| inputs=[ | |
| gr.Textbox(lines=2, placeholder="Enter your prompt here...") | |
| ], | |
| outputs=gr.Textbox(), | |
| live=False, | |
| description="SentimentGPT processes the sequence and returns a reasonably accurate guess of whether the sentiment behind the input is positive or negative." | |
| ) | |
| interface.launch() | |