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| import subprocess | |
| subprocess.run(["pip", "install","gradio","torch","transformers"]) | |
| import re | |
| import gradio as gr | |
| import torch | |
| import transformers | |
| import json | |
| from transformers import GPT2LMHeadModel, GPT2Tokenizer | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| # Define a function for generating text based on a prompt using the fine-tuned GPT-2 model and the tokenizer | |
| def generate_text(prompt, length=100, theme=None, **kwargs): | |
| model_url = "https://huggingface.co/spaces/sailormars18/Yelp-reviews-usingGPT2/blob/main/pytorch_model.bin" | |
| config_url = "https://huggingface.co/spaces/sailormars18/Yelp-reviews-usingGPT2/blob/main/config.json" | |
| generation_config_url = "https://huggingface.co/spaces/sailormars18/Yelp-reviews-usingGPT2/blob/main/generation_config.json" | |
| # Load the model from the Hugging Face space | |
| model = transformers.GPT2LMHeadModel.from_pretrained(model_url).to(device) | |
| # Load the tokenizer from the Hugging Face space | |
| tokenizer = transformers.GPT2Tokenizer.from_pretrained(config_url) | |
| # If a theme is specified, add it to the prompt as a prefix for a special token | |
| if theme: | |
| prompt = f"<{theme.strip()}> {prompt.strip()}" | |
| # Encode the input prompt | |
| input_ids = tokenizer.encode(prompt, return_tensors='pt').to(device) | |
| attention_mask = torch.ones(input_ids.shape, dtype=torch.long, device=device) | |
| pad_token_id = tokenizer.eos_token_id | |
| # Set the max length of the generated text based on the input parameter | |
| max_length = length if length > 0 else 100 | |
| # Generate the text using the model | |
| sample_outputs = model.generate( | |
| input_ids, | |
| attention_mask=attention_mask, | |
| pad_token_id=pad_token_id, | |
| do_sample=True, | |
| max_length=max_length, | |
| top_k=50, | |
| top_p=0.95, | |
| temperature=0.8, | |
| num_return_sequences=1, | |
| no_repeat_ngram_size=2, | |
| repetition_penalty=1.5, | |
| ) | |
| # Decode the generated text | |
| generated_text = tokenizer.decode(sample_outputs[0], skip_special_tokens=True) | |
| # Postprocessing of the generated text | |
| generated_text = generated_text.strip().strip('"') # Remove leading and trailing whitespace, remove any leading and trailing quotation marks | |
| generated_text = re.sub(r'<([^>]+)>', '', generated_text) # Find the special token in the generated text and remove it | |
| generated_text = re.sub(r'^\d+|^"', '', generated_text) # Remove any leading numeric characters and quotation marks | |
| generated_text = generated_text.replace('\n', '') # Remove any newline characters from the generated text | |
| generated_text = re.sub(r'[^\w\s]+', '', generated_text) # Remove any other unwanted special characters | |
| generated_text = generated_text.capitalize() | |
| return generated_text | |
| # Define a Gradio interface for the generate_text function | |
| iface = gr.Interface( | |
| fn=generate_text, | |
| inputs=[ | |
| "text", | |
| gr.inputs.Slider(minimum=10, maximum=100, default=50, label='Length of text'), | |
| gr.inputs.Textbox(default='Food', label='Theme') | |
| ], | |
| outputs=[gr.outputs.Textbox(label='Generated Text')], | |
| title='Yelp Review Generator', | |
| description='Generate a Yelp review based on a prompt, length of text, and theme.', | |
| examples=[ | |
| ['I had a great experience at this restaurant.', 50, 'Service'], | |
| ['The service was terrible and the food was cold.', 50, 'Atmosphere'], | |
| ['The food was delicious but the service was slow.', 50, 'Food'], | |
| ['The ambiance was amazing and the staff was friendly.', 75, 'Service'], | |
| ['The waitstaff was knowledgeable and attentive, but the noise level was a bit high.', 75, 'Atmosphere'], | |
| ['The menu had a good variety of options, but the portion sizes were a bit small for the price.', 75, 'Food'] | |
| ], | |
| allow_flagging="manual", | |
| flagging_options=[("🙌", "positive"), ("😞", "negative")], | |
| ) | |
| iface.launch(debug=False, share=True) |