app.py

import gradio as gr
from transformers import GPT2LMHeadModel, GPT2Tokenizer

# Load pre-trained model and tokenizer
model_name = "gpt2"  # You can use other models like gpt-2-large or gpt-3 for better performance
model = GPT2LMHeadModel.from_pretrained(model_name)
tokenizer = GPT2Tokenizer.from_pretrained(model_name)

# Function to generate keywords based on a prompt
def generate_keywords(prompt):
    # Encode input prompt with a more direct instruction for only keywords
    prompt_with_instruction = prompt + " Only provide a list of keywords, no additional text."
    inputs = tokenizer.encode(prompt_with_instruction, return_tensors="pt")

    # Generate output from model
    outputs = model.generate(inputs, max_length=50, num_return_sequences=1, no_repeat_ngram_size=2, top_k=50, top_p=0.95)

    # Decode generated tokens
    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
    
    # Clean up the text to remove unnecessary parts
    # Remove anything after 'Only provide a list of keywords'
    clean_text = generated_text.split("Only provide a list of keywords")[0].strip()
    
    # Return the keywords only
    return clean_text

# Gradio interface
iface = gr.Interface(fn=generate_keywords, 
                     inputs=gr.Textbox(label="Enter Ad Prompt", placeholder="E.g., Generate ad keywords for wireless headphones"),
                     outputs=gr.Textbox(label="Generated Keywords"),
                     live=True)

# Launch interface
iface.launch()