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| """ Script for streamlit demo | |
| @author: AbinayaM02 | |
| """ | |
| # Install necessary libraries | |
| from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline | |
| import streamlit as st | |
| from pprint import pprint | |
| import json | |
| # Read the config | |
| with open("config.json") as f: | |
| cfg = json.loads(f.read()) | |
| # Set page layout | |
| st.set_page_config(layout="wide") | |
| # Load the model | |
| def load_model(): | |
| tokenizer = AutoTokenizer.from_pretrained(cfg["model_name_or_path"]) | |
| model = AutoModelForCausalLM.from_pretrained(cfg["model_name_or_path"]) | |
| generator = pipeline("text2text-generation", model=model, tokenizer=tokenizer) | |
| return generator, tokenizer | |
| with st.spinner('Loading model...'): | |
| generator, tokenizer = load_model() | |
| # st.image("images/chef-transformer.png", width=400) | |
| st.header("Tamil Language Demos") | |
| st.markdown( | |
| "This demo uses [GPT2 trained on Oscar dataset](https://huggingface.co/flax-community/gpt-2-tamil) " | |
| "to show language generation and other downstream tasks" | |
| ) | |
| img = st.sidebar.image("images/tamil_logo.png", width=100) | |
| add_text_sidebar = st.sidebar.title("Select demo:") | |
| sampling_mode = st.sidebar.selectbox("select a demo", index=0, options=["Text Generation", "Text Classification"]) |