import random
import gradio as gr

__import__('pysqlite3')
import sys
import dotenv

from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings
from langchain.prompts import ChatPromptTemplate, SemanticSimilarityExampleSelector
from langchain.chat_models import ChatOpenAI
from langchain.schema.runnable import RunnablePassthrough

dotenv.load_dotenv()

sys.modules['sqlite3'] = sys.modules.pop('pysqlite3')
vectorstore = Chroma(persist_directory="./chroma_db.1.0", embedding_function=OpenAIEmbeddings())
retriever = vectorstore.as_retriever()
embeddings = OpenAIEmbeddings()
examples_vectorstore = Chroma(persist_directory="./chroma_db_examples.1.0", embedding_function=OpenAIEmbeddings())
example_selector = SemanticSimilarityExampleSelector(
    vectorstore=examples_vectorstore,
    k=2,
)

from langchain.prompts import (
    ChatPromptTemplate,
    FewShotChatMessagePromptTemplate,
)

few_shot_prompt = FewShotChatMessagePromptTemplate(
    input_variables=["input"],
    example_selector=example_selector,
    example_prompt=ChatPromptTemplate.from_messages(
        [("human", "{input}"), ("ai", "{output}")]
    )
)

final_prompt = ChatPromptTemplate.from_messages(
    [
        ("system",
         "You are a lawyer specialized in French Labour laws and promting Unions. You want to help but not misguide the user. If you don't know the answer, just say that you don't know. Don't hesitate to reformulate and think about the issues. Cite law articles as much as possible."),
        few_shot_prompt,
        ("human", """
Question: {question} 

Context: {context} 

Answer:
"""),
    ]
)

llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0.33)
rag_chain = (
        {"context": retriever, "question": RunnablePassthrough(), "input" : RunnablePassthrough()}
        | final_prompt
        | llm
)

def random_response(message, history):
    res = rag_chain.invoke(message)
    return res.content


demo = gr.ChatInterface(random_response)

if __name__ == "__main__":
    demo.launch()