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
    inputs = tokenizer.encode(prompt, 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)
    
    # Extracting keywords by splitting the generated text into words
    keywords = generated_text.split()
    
    return " ".join(keywords)

# 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()