File size: 1,729 Bytes
3d2ccf5
 
 
 
 
 
 
050bcdd
 
 
39fa1f9
 
 
c7680ca
3d2ccf5
1b8a80e
 
296d90c
3d2ccf5
 
 
296d90c
 
5d2b7f0
296d90c
 
1b8a80e
 
 
 
 
 
 
 
5d2b7f0
 
3d2ccf5
 
 
 
 
1b8a80e
3d2ccf5
 
173a459
296d90c
 
 
 
 
 
c2425d0
173a459
296d90c
 
1b8a80e
3d2ccf5
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
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()




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 = ["Let's find out the best task for your use case! Tell me about your use case :)"]


for msg in message_history:
    message(msg)   # 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:
    model_answer = data["answer"]
    response_templates = [f"{model_answer} is the best task for this 🀩", f"I think you should use {model_answer} πŸͺ„", f"I think {model_answer} should work for you πŸ€“"]
    bot_answer = random.choice(response_templates)
    message_history.append(bot_answer)
    
    

except:
    message("I'm listening πŸ‘€ ")
    message_history.append("I'm listening πŸ‘€")