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import streamlit as st
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"
#from api import Api
#import streamlit as st
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
#external class for api integrations,
#api = Api()
#llm default OpenAPI
#llm = api.llm
llm = HuggingFaceEndpoint(repo_id="mistralai/Mistral-7B-Instruct-v0.2", Temperature=0.9)
#streamlit view components
with st.form("my_form"):
st.title('Sentiment Analysis')
text_review = st.text_area('Write me a review')
option = st.selectbox(
'Select the language to evaluate:',
('Italian', 'Spanish', 'English'))
submitted = st.form_submit_button("Submit")
if submitted:
#1 prompt template
template = """
Please act as a machine learning model trained for perform a supervised learning task,
for extract the sentiment of a review in '{option}' Language.
Give your answer writing a Json evaluating the sentiment field between the dollar sign, the value must be printed without dollar sign.
The value of sentiment must be "Positive" , "Negative" or "Neutral" otherwise if the text is not valuable write "null".
Example:
field 1 named :
text_review with value: {text_review}
field 2 named :
sentiment with value: $sentiment$
Field 3 named :
language with value: {option}
Review text: '''{text_review}'''
"""
prompt = PromptTemplate(template=template, input_variables=["text_review","option"])
llm_chain = LLMChain(prompt=prompt, llm=llm)
if prompt:
response = llm_chain.run({"text_review": text_review, "option": option})
#json printed
print(response)
st.text(response)
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