app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
import argparse

# Load the pre-trained model and tokenizer
models = {
    "EleutherAI/gpt-neo-2.7B": "EleutherAI/gpt-neo-2.7B",
    "BlinkDL/rwkv-4-pile-430m": "BlinkDL/rwkv-4-pile-430m",
    "BlinkDL/rwkv-4-pile-1b5": "BlinkDL/rwkv-4-pile-1b5",
    "BlinkDL/RWKV-4-Raven": "BlinkDL/RWKV-4-Raven"
}

def generate_text(prompt, model_name):
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForCausalLM.from_pretrained(model_name)

    # Tokenize the input
    input_ids = tokenizer.encode(prompt, return_tensors="pt")

    # Generate a response
    output = model.generate(input_ids, max_length=100, num_return_sequences=1)

    # Decode the output
    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)

    return generated_text

def main():
    # Create a Gradio interface
    model_dropdown = gr.inputs.Dropdown(choices=list(models.keys()), label="Select Model")
    prompt_input = gr.inputs.Textbox(lines=5, placeholder="Enter your prompt here...", label="Prompt")
    output_text = gr.outputs.Textbox(label="Generated Text")

    interface = gr.Interface(
        fn=generate_text,
        inputs=[prompt_input, model_dropdown],
        outputs=output_text,
        title="Chat-bot using RWKV LLM",
        description="Select a model and enter a prompt to generate text using the chat-bot."
    )

    # Launch the interface
    interface.launch()

if __name__ == '__main__':
    main()