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import streamlit as st
import requests
import os
from streamlit_chat import message
@st.cache
def query(payload):
api_token = os.getenv("api_token")
model_id = "deepset/roberta-base-squad2"
headers = {"Authorization": f"Bearer {api_token}"}
API_URL = f"https://api-inference.huggingface.co/models/{model_id}"
response = requests.post(API_URL, headers=headers, json=payload)
return response.json()
st.title("Let's find out the best task for your use case! Tell me about your use case :)")
context = "To extract information from documents, use sentence similarity task. To do sentiment analysis from tweets, use text classification task. To detect masks from images, use object detection task. To extract information from invoices, use named entity recognition from token classification task."
for message_ in message_history:
message(message_) # display all the previous message
placeholder = st.empty() # placeholder for latest message
input = st.text_input("You:")
message_history.append(input)
with placeholder.container():
message(message_history[-1]) # display the latest message
message(input, is_user=True) # align's the message to the right
data = query(
{
"inputs": {
"question": input,
"context": context,
}
}
)
try:
bot_answer = data["answer"]
message(f"{bot_answer} is the best task for this :)")
except:
message("Inference API is currently loading!")
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