File size: 1,433 Bytes
887ce17 |
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 |
import streamlit as st
#from langchain_openai import OpenAI
#from langchain.llms import HuggingFaceEndpoint
from langchain_community.llms import HuggingFaceEndpoint
#When deployed on huggingface spaces, this values has to be passed using Variables & Secrets setting, as shown in the video :)
#import os
#os.environ["OPENAI_API_KEY"] = "sk-PLfFwPq6y24234234234FJ1Uc234234L8hVowXdt"
#Function to return the response
def load_answer(question):
# "text-davinci-003" model is depreciated, so using the latest one https://platform.openai.com/docs/deprecations
#llm = OpenAI(model_name="gpt-3.5-turbo-instruct",temperature=0)
llm = HuggingFaceEndpoint(repo_id="mistralai/Mistral-7B-Instruct-v0.2", Temperature=0.9)
#Last week langchain has recommended to use invoke function for the below please :)
answer=llm.invoke(question)
return answer
#App UI starts here
st.set_page_config(page_title="Sentiment Analysis", page_icon=":robot:")
st.header("Sentiment Analysis")
#Gets the user input
def get_text():
input_text = st.text_input("You:", "Pls Write Your Something.......")
if input_text.isalpha():
st.write(text, 'string', )
else:
st.write('Please type in a string Only')
return input_text
user_input=get_text()
response = load_answer(user_input)
submit = st.button('Generate')
#If generate button is clicked
if submit:
st.subheader("Answer:")
st.write(response)
|