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import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
# Load model and tokenizer locally
MODEL_NAME = "BICORP/Lake-1-12B-spe" # Replace with your local model directory if different
def load_model():
try:
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
return tokenizer, model
except Exception as e:
raise RuntimeError(f"Error loading model: {str(e)}")
tokenizer, model = load_model()
def generate_text(prompt, max_length=100, temperature=0.9):
try:
# Encode the input prompt
inputs = tokenizer.encode(prompt, return_tensors='pt')
# Generate output
outputs = model.generate(
inputs,
max_length=max_length,
temperature=temperature,
pad_token_id=tokenizer.eos_token_id,
do_sample=True
)
# Decode and return the generated text
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
return generated_text
except Exception as e:
return f"Error generating text: {str(e)}"
# Create Gradio interface
interface = gr.Interface(
fn=generate_text,
inputs=[
gr.Textbox(label="Input Prompt", lines=3),
gr.Slider(50, 500, value=100, label="Max Length"),
gr.Slider(0.1, 2.0, value=0.9, label="Temperature")
],
outputs=gr.Textbox(label="Generated Text", lines=5),
title="Local Model Demo - Text Generation",
description="A locally loaded GPT-2 model for text generation",
examples=[
["Once upon a time, in a land far away,"],
["The future of artificial intelligence", 150, 0.7],
["In a world where robots rule,", 200, 1.2]
]
)
# Run the app
if __name__ == "__main__":
interface.launch(server_name="0.0.0.0", server_port=7860) |