app.py

import streamlit as st

st.title("Mistral QA")

# import chainlit as cl

import os
huggingfacehub_api_token = st.secrets["hf_token"]

from langchain import HuggingFaceHub, PromptTemplate, LLMChain

repo_id = "mistralai/Mistral-7B-v0.1"
llm = HuggingFaceHub(huggingfacehub_api_token=huggingfacehub_api_token, 
                     repo_id=repo_id, 
                     model_kwargs={"temperature":0.2, "max_new_tokens":200})

template = """Give answer for the question. 
question: {question}

At the end of the answer, just say, 'Thanks for asking'

"""
# input = st.text_input("What do you want to ask about", placeholder="Input your question here")


# # @cl.langchain_factory
# def factory():
#     prompt = PromptTemplate(template=template, input_variables=['question'])
#     llm_chain = LLMChain(prompt=prompt, llm=llm, verbose=True)

#     return llm_chain


prompt = PromptTemplate(template=template, input_variables=["question"])
llm_chain = LLMChain(prompt=prompt,verbose=True,llm=llm)

# result = llm_chain.predict(question=input)

# print(result)

def chat(query):
    # prompt = PromptTemplate(template=template, input_variables=["question"])
    # llm_chain = LLMChain(prompt=prompt,verbose=True,llm=llm)

    result = llm_chain.predict(question=query)

    return result




def main():
    input = st.text_input("What do you want to ask about", placeholder="Input your question here")
    if input:
        output = chat(input)
        st.write(output,unsafe_allow_html=True)


if __name__ == '__main__':
    main()