## Setup
# Import the necessary Libraries
import os
import uuid
import joblib
import json
import tiktoken
import pandas as pd
import gradio as gr
from openai import OpenAI
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_core.documents import Document
from langchain_community.document_loaders import PyPDFDirectoryLoader
from langchain_community.embeddings.sentence_transformer import (
    SentenceTransformerEmbeddings
)
from langchain_community.vectorstores import Chroma
from huggingface_hub import CommitScheduler
from pathlib import Path

# Create Client
os.environ['OPENAI_API_KEY']  = "gl-U2FsdGVkX1+0bNWD6YsVLZUYsn0m1WfLxUzrP0xUFbtWFAfk9Z1Cz+mD8u1yqKtV"; # e.g. gl-U2FsdGVkX19oG1mRO+LGAiNeC7nAeU8M65G4I6bfcdI7+9GUEjFFbplKq48J83by
os.environ["OPENAI_BASE_URL"] =  "https://aibe.mygreatlearning.com/openai/v1" # e.g. "https://aibe.mygreatlearning.com/openai/v1";
client = OpenAI()

# Define the embedding model and the vectorstore
model_name = 'gpt-4o-mini'
embedding_model = SentenceTransformerEmbeddings(model_name='thenlper/gte-large')

# Load the persisted vectorDB
persisted_vectordb_location = '10k-reports_db'
collection_name = '10k-reports'
vectorstore_persisted = Chroma(
    collection_name=collection_name,
    persist_directory=persisted_vectordb_location,
    embedding_function=embedding_model
)
vectorstore_persisted.get()

# Prepare the logging functionality
log_file = Path("logs/") / f"data_{uuid.uuid4()}.json"
log_folder = log_file.parent

scheduler = CommitScheduler(
    repo_id="Keytaro/10K-reports-mlops-logs",
    repo_type="dataset",
    folder_path=log_folder,
    path_in_repo="data",
    every=2
)

# Define the Q&A system message
qna_system_message = """
You are an assistant to a Gen AI Data Scientist. Your task is to automate the extraction, summarization, and analysis of information from the 10-K reports.

User input will include the necessary context for you to answer their questions. This context will begin with the token: ###Context.
The context contains references to specific portions of documents relevant to the user's query, along with source links.
The source for a context will begin with the token ###Source

When crafting your response:
1. Select only context relevant to answer the question.
2. Include the source links in your response.
3. User questions will begin with the token: ###Question.
4. If the question is irrelevant to streamlit respond with - "I am an assistant for Gen AI Data Scientist. I can only help you with questions related to 10-K reports."

Please adhere to the following guidelines:
- Your response should only be about the question asked and nothing else.
- Answer only using the context provided.
- Do not mention anything about the context in your final answer.
- If the answer is not found in the context, it is very very important for you to respond with "I don't know. Please check the 10-K reports"
- Always quote the source when you use the context. Cite the relevant source at the end of your response under the section - Source:
- Do not make up sources. Use the links provided in the sources section of the context and nothing else. You are prohibited from providing other links/sources.

Here is an example of how to structure your response:

Answer:
[Answer]

Source:
[Source]
"""

# Define the user message template
qna_user_message_template = """
###Context
Here are some documents and their source links that are relevant to the question mentioned below.
{context}

###Question
{question}
"""

# Define the predict function that runs when 'Submit' is clicked or when a API request is made
def predict(user_input,company):

    companyfile = {
        "Amazon": "aws",
        "Google": "google",
        "Microsoft": "msft",
        "Meta": "Meta",
        "IBM": "IBM"
    }.get(company, None)
    if companyfile is not None:
        user_input = user_input.replace("the company", company)

    filter = "dataset/"+companyfile+"-10-k-2023.pdf"
    relevant_document_chunks = vectorstore_persisted.similarity_search(user_input, k=5, filter={"source":filter})

    # Create context_for_query
    context_list = [d.page_content + f"\n ###Source: \'{d.metadata['source']}\', p.{d.metadata['page']}\n\n " for d in relevant_document_chunks]
    context_for_query = ". ".join(context_list)

    # Create messages
    prompt = [
        {'role':'system', 'content': qna_system_message},
        {'role': 'user', 'content': qna_user_message_template.format(
                context=context_for_query,
                question=user_input
            )
        }
    ]

    # Get response from the LLM
    try:
        response = client.chat.completions.create(
            model=model_name,
            messages=prompt,
            temperature=0
        )

        prediction = response.choices[0].message.content.strip()

    except Exception as e:
        prediction = f'Sorry, I encountered the following error: \n {e}'


    # While the prediction is made, log both the inputs and outputs to a local log file
    # While writing to the log file, ensure that the commit scheduler is locked to avoid parallel
    # access

    with scheduler.lock:
        with log_file.open("a") as f:
            f.write(json.dumps(
                {
                    'user_input': user_input,
                    'retrieved_context': context_for_query,
                    'model_response': prediction
                }
            ))
            f.write("\n")

    return (prediction, user_input, context_for_query)

# Set-up the Gradio UI
# Add text box and radio button to the interface
# The radio button is used to select the company 10k report in which the context needs to be retrieved.

textbox = gr.Textbox()
company = gr.Radio()

inputs = [
    gr.Radio(label="user_input", choices=["Has the company made any significant acquisitions in the AI space, and how are these acquisitions being integrated into the company's strategy?",
                                          "How much capital has been allocated towards AI research and development by the company?",
                                          "What initiatives has the company implemented to address ethical concerns surrounding AI, such as fairness, accountability, and privacy?",
                                          "How does the company plan to differentiate itself in the AI space relative to competitors?",
                                          "What are the company’s policies and frameworks regarding AI ethics, governance, and responsible AI use as detailed in their 10-K reports?",
                                          "What are the primary business segments of the company, and how does each segment contribute to the overall revenue and profitability?",
                                          "What are the key risk factors identified in the 10-K report that could potentially impact the company’s business operations and financial performance?"
                                          ]),
    gr.Radio(label="Company", choices=["Amazon", "Google", "Microsoft", "Meta", "IBM"]),
]

output = [
    gr.Textbox(label="Answer"),
    gr.Textbox(label="query"),
    gr.Textbox(label="context_for_query")
]

# Create the interface
# For the inputs parameter of Interface provide [textbox,company]
demo = gr.Interface(
    fn=predict,
    inputs=inputs,
    outputs=output,
    title="10-K reports RAG system",
    description="This API allows you to answer one of the 5 questions based on 10-K reports.",
    allow_flagging="auto", #
    concurrency_limit=8 #
)

demo.queue()
demo.launch()