app.py

import streamlit as st
import requests
import os
from streamlit_chat import message
import random

@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(), response


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."


message_history = [{"text":"Let's find out the best task for your use case! Tell me about your use case :)", "is_user":False}]


for msg in message_history:
    message(msg["text"], is_user = msg["is_user"])   # display all the previous message
  
input = st.text_input("Ask me 🤗")

message_history.append({"text":input, "is_user" : True})


placeholder = st.empty()  # placeholder for latest message

with placeholder.container():
    last_message = message_history[-1]
    if last_message != "":
    
        message(last_message["text"], last_message["is_user"]) # display the latest message
      

data, resp = query(
    {
        "inputs": {
            "question": input,
            "context": context,
        }
    }
)
if resp.status_code == 200:

    model_answer = data["answer"]
    response_templates = [f"{model_answer} is the best task for this 🤩", f"I think you should use {model_answer} 🪄", f"I think {model_answer} should work for you 🤓"]
    
    bot_answer = random.choice(response_templates)
    
    message_history.append({"text":bot_answer, "is_user" : False})