import os
import streamlit as st
from transformers import pipeline
from huggingface_hub.inference_api import InferenceApi

API_TOKEN = os.getenv('My_Token')

inference = InferenceApi(repo_id="bert-base-uncased", token=API_TOKEN)
# inference(inputs="The goal of life is [MASK].")

text1 = st.text_area("enter some text 111")
if text1:
    res = inference(inputs="The goal of life is [MASK].")
    st.json(res)

pipe = pipeline("sentiment-analysis")
text2 = st.text_area("enter some text 222")

if text2:
  out = pipe(text)
  st.json(out)

# import streamlit as st

# # adding the text that will show in the text box as default
# default_value = "See how a modern neural network auto-completes your text 🤗 This site, built by the    Hugging Face team, lets you write a whole document directly from your browser, and you can trigger the Transformer anywhere using the Tab key. Its like having a smart machine that completes your thoughts 😀 Get started by typing a custom snippet, check out the repository, or try one of the examples. Have fun!"

# sent = st.text_area("Text", default_value, height = 275)
# max_length = st.sidebar.slider("Max Length", min_value = 10, max_value=30)
# temperature = st.sidebar.slider("Temperature", value = 1.0, min_value = 0.0, max_value=1.0, step=0.05)
# top_k = st.sidebar.slider("Top-k", min_value = 0, max_value=5, value = 0)
# top_p = st.sidebar.slider("Top-p", min_value = 0.0, max_value=1.0, step = 0.05, value = 0.9)
# num_return_sequences = st.sidebar.number_input('Number of Return Sequences', min_value=1, max_value=5, value=1, step=1)