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app-local.py
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app-local.py
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import streamlit as st
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
# Define a function to load the model and tokenizer
@st.cache_data(show_spinner=False)
def load_model_and_tokenizer(model_path):
# Load the tokenizer and model from the local directory
ft_tokenizer = AutoTokenizer.from_pretrained(model_path)
ft_model = AutoModelForCausalLM.from_pretrained(model_path)
return ft_tokenizer, ft_model
# Load the model and tokenizer
model_path = "./model/medium-tech"
ft_tokenizer, ft_model = load_model_and_tokenizer(model_path)
def main():
html_title = """
<div style="background:#5dc9c6 ;padding:10px">
<h2 style="color:white;text-align:center">Medium post opinions</h2>
</div>
<p>Start a sentence to have Medium posts complete your sentence 🤔</p>
"""
st.markdown(html_title, unsafe_allow_html=True)
# Create a text input field for user input
text = st.text_input("Enter text:")
# Generate response when the "Generate" button is clicked
if st.button("Generate"):
with st.spinner("Generating..."):
# Tokenize the input text
ft_input_ids = ft_tokenizer.encode(text, return_tensors='pt')
# Generate output using the model
output = ft_model.generate(ft_input_ids, attention_mask=torch.ones_like(ft_input_ids),
pad_token_id=ft_tokenizer.eos_token_id,
max_length=100, do_sample=True)
# Decode and display the output
response = ft_tokenizer.decode(output[0], skip_special_tokens=True)
st.write(response)
# Run the app
if __name__ == "__main__":
main()