File size: 1,280 Bytes
f6cb791 7dc916b f6cb791 |
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 |
import gradio as gr
from transformers import AutoTokenizer
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM
# Load the PEFT model configuration and model
config = PeftConfig.from_pretrained("isashap/contexttrained-validationloss-gpt2FINAL")
base_model = AutoModelForCausalLM.from_pretrained("gpt2")
model = PeftModel.from_pretrained(base_model, "isashap/contexttrained-validationloss-gpt2FINAL")
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained("gpt2")
# Define the prediction function
def predict_stock(input_text):
# Tokenize the input
inputs = tokenizer(input_text, return_tensors="pt")
# Generate output using the model
outputs = model.generate(**inputs, max_length=50, num_return_sequences=1)
# Decode the output
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
return generated_text
demo = gr.Interface(
fn=predict_stock,
inputs=gr.Textbox(lines=2, label="Input Text (e.g., stock-related news)"),
outputs=gr.Textbox(label="Prediction"),
title="Stock Prediction with PEFT Model",
description="A demo that uses a fine-tuned GPT-2 model with PEFT to predict stock outcomes based on input text."
)
if __name__ == "__main__":
demo.launch()
|