app.py

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("./pytorch_model.bin", config="./config.json").to(device)
    
    # Load the tokenizer from the Hugging Face space
    tokenizer = transformers.GPT2Tokenizer.from_pretrained('gpt2')

    # 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)