Spaces:
Sleeping
Sleeping
File size: 2,970 Bytes
840b176 028bd08 840b176 15e2a67 028bd08 15e2a67 840b176 028bd08 15e2a67 028bd08 840b176 028bd08 840b176 028bd08 840b176 028bd08 840b176 028bd08 840b176 028bd08 840b176 028bd08 840b176 028bd08 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 |
import gradio as gr
import torch
from transformers import GPT2LMHeadModel, GPT2Tokenizer
# Load model and tokenizer
def load_model():
try:
# Load the fine-tuned model
model = GPT2LMHeadModel.from_pretrained("aayushraina/gpt2shakespeare")
# Use the base GPT-2 tokenizer
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
model.eval()
print("Model and tokenizer loaded successfully!")
return model, tokenizer
except Exception as e:
print(f"Error loading model: {e}")
return None, None
# Text generation function
def generate_text(prompt, max_length=500, temperature=0.8, top_k=40, top_p=0.9):
if model is None or tokenizer is None:
return "Error: Model not loaded properly"
try:
# Encode the input prompt
input_ids = tokenizer.encode(prompt, return_tensors='pt')
# Generate text
with torch.no_grad():
output = model.generate(
input_ids,
max_length=max_length,
temperature=temperature,
top_k=top_k,
top_p=top_p,
do_sample=True,
pad_token_id=tokenizer.eos_token_id,
num_return_sequences=1
)
# Decode and return the generated text
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
return generated_text
except Exception as e:
return f"Error during generation: {str(e)}"
# Load model and tokenizer globally
print("Loading model and tokenizer...")
model, tokenizer = load_model()
# Create Gradio interface
demo = gr.Interface(
fn=generate_text,
inputs=[
gr.Textbox(label="Enter your prompt", placeholder="Start your text here...", lines=2),
gr.Slider(minimum=10, maximum=1000, value=500, step=10, label="Maximum Length"),
gr.Slider(minimum=0.1, maximum=2.0, value=0.8, step=0.1, label="Temperature"),
gr.Slider(minimum=1, maximum=100, value=40, step=1, 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", lines=10),
title="Shakespeare-style Text Generator",
description="""Generate Shakespeare-style text using a fine-tuned GPT-2 model.
Parameters:
- Temperature: Higher values make the output more random, lower values more focused
- Top-k: Number of highest probability vocabulary tokens to keep for top-k filtering
- Top-p: Cumulative probability for nucleus sampling
""",
examples=[
["First Citizen:", 500, 0.8, 40, 0.9],
["To be, or not to be,", 500, 0.8, 40, 0.9],
["Friends, Romans, countrymen,", 500, 0.8, 40, 0.9],
["O Romeo, Romeo,", 500, 0.8, 40, 0.9],
["Now is the winter of our discontent", 500, 0.8, 40, 0.9]
]
)
# Launch the app
if __name__ == "__main__":
demo.launch() |