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import os

import gradio as gr

from openai import OpenAI

from langchain.text_splitter import RecursiveCharacterTextSplitter

from langchain_community.document_loaders import PyPDFLoader
from langchain_community.embeddings.sentence_transformer import SentenceTransformerEmbeddings
from langchain_community.vectorstores import Chroma

client = OpenAI(api_key=os.environ['OPENAI_API_KEY'])

embedding_model = SentenceTransformerEmbeddings(model_name='thenlper/gte-small')

tesla_10k_collection = 'tesla-10k-2019-to-2023'

vectorstore_persisted = Chroma(
    collection_name=tesla_10k_collection,
    persist_directory='./tesla_db',
    embedding_function=embedding_model
)

retriever = vectorstore_persisted.as_retriever(
    search_type='similarity',
    search_kwargs={'k': 5}
)

qna_system_message = """
You are an assistant to a financial services firm who answers user queries on annual reports.
Users will ask questions delimited by triple backticks, that is, ```.
User input will have the context required by you to answer user questions.
This context will begin with the token: ###Context.
The context contains references to specific portions of a document relevant to the user query.
Please answer only using the context provided in the input.
If the answer is not found in the context, respond "I don't know".
"""

qna_user_message_template = """
###Context
Here are some documents that are relevant to the question.
{context}
```
{question}
```
"""

def predict(user_input):

    relevant_document_chunks = retriever.get_relevant_documents(user_input)
    context_list = [d.page_content for d in relevant_document_chunks]
    context_for_query = ".".join(context_list)
    
    prompt = [
        {'role':'system', 'content': qna_system_message},
        {'role': 'user', 'content': qna_user_message_template.format(
            context=context_for_query,
            question=user_input
            )
        }
    ]

    try:
        response = client.chat.completions.create(
            model="gpt-3.5-turbo",
            messages=prompt,
            temperature=0
        )

        prediction = response.choices[0].message.content

    except Exception as e:
        prediction = e
        
    return prediction


textbox = gr.Textbox(placeholder="Enter your query here", lines=6)

interface = gr.Interface(
    inputs=textbox, fn=predict, outputs="text",
    title="AMA on Tesla 2022 10-K",
    description="This web API presents an interface to ask questions on contents of the Tesla 2022 10-K report.",
    article="Note that questions that are not relevent to the Tesla 10-K report will not be answered.",
    allow_flagging="manual", flagging_options=["Useful", "Not Useful"]
)

with gr.Blocks() as demo:
    interface.launch()

demo.queue(concurrency_count=16)
demo.launch()