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import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
class TextGenerator:
def __init__(self, model_name, device='cpu'):
self.device = device
self.load_model(model_name)
def load_model(self, model_name):
# Load model and tokenizer from Hugging Face
print("Loading model and tokenizer...")
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
self.model = AutoModelForCausalLM.from_pretrained(model_name)
self.model.to(self.device)
print("Model loaded successfully!")
def generate_text(self, prompt, max_length=100, temperature=0.7, top_k=50, top_p=0.9):
# Tokenize input
input_ids = self.tokenizer.encode(prompt, return_tensors="pt").to(self.device)
# Generate text
with torch.no_grad():
output_ids = self.model.generate(
input_ids,
max_length=max_length,
temperature=temperature,
top_k=top_k,
top_p=top_p,
do_sample=True,
pad_token_id=self.tokenizer.eos_token_id
)
# Decode output tokens
generated_text = self.tokenizer.decode(output_ids[0], skip_special_tokens=True)
return generated_text
def create_gradio_interface(model_name):
generator = TextGenerator(model_name)
def generate(prompt, max_length, temperature, top_k, top_p):
try:
return generator.generate_text(
prompt=prompt,
max_length=max_length,
temperature=temperature,
top_k=top_k,
top_p=top_p
)
except Exception as e:
return f"Error: {str(e)}"
# Define Gradio interface
interface = gr.Interface(
fn=generate,
inputs=[
gr.Textbox(label="Prompt", placeholder="Enter your prompt here..."),
gr.Slider(minimum=10, maximum=500, value=100, step=10, label="Maximum Length"),
gr.Slider(minimum=0.1, maximum=2.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(minimum=0, maximum=100, value=50, step=5, label="Top-k"),
gr.Slider(minimum=0.1, maximum=1.0, value=0.9, step=0.1, label="Top-p"),
],
outputs=gr.Textbox(label="Generated Text"),
title="Reality123b/Xylaria-1.4-Senoa-Test",
description="Generate text using the Reality123b/Xylaria-1.4-Senoa-Test model optimized for CPU usage.",
)
return interface
if __name__ == "__main__":
# Use the model from Hugging Face
model_name = "Reality123b/Xylaria-1.4-Senoa-Test"
# Create and launch Gradio interface
interface = create_gradio_interface(model_name)
interface.launch(share=True)
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