app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load the pre-trained LLaMA model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("facebook/llama-7b")
model = AutoModelForCausalLM.from_pretrained("facebook/llama-7b")

# Function to generate keywords from input text
def generate_keywords(text):
    # Encode the input text
    inputs = tokenizer.encode(text, return_tensors="pt")

    # Generate the output from the 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 and return the generated keywords
    keywords = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return keywords.strip()

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

# Launch the Gradio interface
iface.launch()