Initial demo

app.py

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+""" Script for streamlit demo
+    @author: AbinayaM02
+"""
+
+# Install necessary libraries
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+import streamlit as st
+#from streamlit_tags import st_tags
+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
+@st.cache(allow_output_mutation=True)
+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 Mode", index=0, options=["Top Sampling", "Beam Search"])

config.json

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+{
+    "model_name_or_path": "flax-community/gpt-2-tamil/gpt-2-tamil",
+    "Text Generation": ["example_1", "example_2"],
+    "Text Classification": ["example_2", "example_2"]
+}

requirements.txt

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+streamlit
+transformers