import openai
import pinecone
import gradio as gr
import os
import json

# initiates pinecone


def init_pinecone(apikey, environment):
    pinecone.init(api_key=apikey, environment=environment)


def qa_gita(query, openaikey):
    # Basic Checks
    if not query:
        return "Please enter your query."
    if not openaikey:
        return "Please enter openaikey."

    openai.api_key = openaikey

    response = openai.Embedding.create(
        input=[query], model="text-embedding-ada-002")
    embedding = response["data"][0]["embedding"]

    pinecone_api_key_gita = os.getenv("PINECONE_API_KEY_GITA")
    pinecone_environment = os.getenv("PINECONE_ENVIRONMENT_GITA")

    init_pinecone(pinecone_api_key_gita, pinecone_environment)

    index = pinecone.Index("gita")

    with open("data.json", "r") as f:
        data = json.loads(f.read())

    res = index.query(vector=(embedding), top_k=8)

    ids = [i["id"] for i in res["matches"]]

    context = ""

    for id in ids:
        context = context + str(id) + ": " + data["gita"][str(id)] + "\n\n"

    systemprompt = f"You are not an AI Language model. You will be a Bhagwad Gita Assistant to the user. Restrict Yourself to the context of the question."

    userprompt = f"Verses:\n\n{context}\n\nQuestion: {query}\n\nAnswer:\n\n"

    response = openai.ChatCompletion.create(
        model="gpt-3.5-turbo",
        messages=[
            {"role": "system", "content": systemprompt},
            {"role": "user", "content": userprompt},
        ],
        max_tokens=256,
        temperature=0.0,
    )

    answer = response["choices"][0]["message"]["content"]

    return answer, context


def qa_bible(query, openaikey):
    # Basic Checks
    if not query:
        return "Please enter your query."
    if not openaikey:
        return "Please enter openaikey."

    openai.api_key = openaikey

    response = openai.Embedding.create(
        input=[query], model="text-embedding-ada-002")
    embedding = response["data"][0]["embedding"]

    pinecone_api_key_bible = os.getenv("PINECONE_API_KEY_BIBLE")
    pinecone_environment = os.getenv("PINECONE_ENVIRONMENT_BIBLE")

    init_pinecone(pinecone_api_key_bible, pinecone_environment)

    index = pinecone.Index("bible")

    with open("data.json", "r") as f:
        data = json.loads(f.read())

    res = index.query(vector=(embedding), top_k=8)

    ids = [i["id"] for i in res["matches"]]

    context = ""

    for id in ids:
        context = context + str(id) + ": " + data["bible"][str(id)] + "\n\n"

    systemprompt = f"You are not an AI Language model. You will be a Bible Assistant to the user. Restrict Yourself to the context of the question."

    userprompt = f"Verses:\n\n{context}\n\nQuestion: {query}\n\nAnswer:\n\n"

    response = openai.ChatCompletion.create(
        model="gpt-3.5-turbo",
        messages=[
            {"role": "system", "content": systemprompt},
            {"role": "user", "content": userprompt},
        ],
        max_tokens=256,
        temperature=0.0,
    )

    answer = response["choices"][0]["message"]["content"]

    return answer, context


def qa_quran(query, openaikey):
    # Basic Checks
    if not query:
        return "Please enter your query."
    if not openaikey:
        return "Please enter openaikey."

    openai.api_key = openaikey

    response = openai.Embedding.create(
        input=[query], model="text-embedding-ada-002")
    embedding = response["data"][0]["embedding"]

    pinecone_api_key_quran = os.getenv("PINECONE_API_KEY_QURAN")
    pinecone_environment = os.getenv("PINECONE_ENVIRONMENT_QURAN")

    init_pinecone(pinecone_api_key_quran, pinecone_environment)

    index = pinecone.Index("quran")

    with open("data.json", "r") as f:
        data = json.loads(f.read())

    res = index.query(vector=(embedding), top_k=8)

    ids = [i["id"] for i in res["matches"]]

    context = ""

    for id in ids:
        context = context + str(id) + ": " + data["quran"][str(id)] + "\n\n"

    systemprompt = f"You are not an AI Language model. You will be a Quran Assistant to the user. Restrict Yourself to the context of the question."

    userprompt = f"Verses:\n\n{context}\n\nQuestion: {query}\n\nAnswer:\n\n"

    response = openai.ChatCompletion.create(
        model="gpt-3.5-turbo",
        messages=[
            {"role": "system", "content": systemprompt},
            {"role": "user", "content": userprompt},
        ],
        max_tokens=256,
        temperature=0.0,
    )

    answer = response["choices"][0]["message"]["content"]

    return answer, context


def cleartext(query, output, references):
    """
    Function to clear text
    """
    return ["", "", ""]


with gr.Blocks() as demo:
    gr.Markdown(
        """
    <h1><center><b>HolyBot</center></h1>
    """
    )
    gr.Markdown(
        """
    HolyBot answers your queries and gives relevant verses based on Bhagwad Gita/ Quran/ Bible holy books, built using OpenAI ChatGPT, and Pinecone Index.
    - Get your [OpenAI API Key](https://platform.openai.com/account/api-keys) before proceeding further.
    - Refer to the codebase for this project on [GitHub](https://github.com/ravi03071991/HolyBot)."""
    )
    with gr.Tabs():
        openaikey = gr.Textbox(lines=1, label="Enter Your OpenAI Key")
        with gr.TabItem("Bhagwad Gita"):
            with gr.Row():
                with gr.Column():
                    query1 = gr.Textbox(
                        lines=2, label="Enter Your Situation/ Query.")
                    submit_button1 = gr.Button("Submit")
                with gr.Column():
                    ans_output1 = gr.Textbox(lines=5, label="Answer.")
                    references1 = gr.Textbox(
                        lines=10, label="Relevant Verses.")
                    clear_button1 = gr.Button("Clear")
        with gr.TabItem("Quran"):
            with gr.Row():
                with gr.Column():
                    query2 = gr.Textbox(
                        lines=2, label="Enter Your Situation/ Query.")
                    submit_button2 = gr.Button("Submit")
                with gr.Column():
                    ans_output2 = gr.Textbox(lines=5, label="Answer.")
                    references2 = gr.Textbox(
                        lines=10, label="Relevant Verses.")
                    clear_button2 = gr.Button("Clear")
        with gr.TabItem("Bible"):
            with gr.Row():
                with gr.Column():
                    query3 = gr.Textbox(
                        lines=2, label="Enter Your Situation/ Query.")
                    submit_button3 = gr.Button("Submit")
                with gr.Column():
                    ans_output3 = gr.Textbox(lines=5, label="Answer.")
                    references3 = gr.Textbox(
                        lines=10, label="Relevant Verses.")
                    clear_button3 = gr.Button("Clear")

    # For Bhagwad Gita
    # Submit button for submitting query.
    submit_button1.click(qa_gita, inputs=[query1, openaikey], outputs=[
                         ans_output1, references1])
    # Clear button for clearing query and answer.
    clear_button1.click(
        cleartext,
        inputs=[query1, ans_output1, references1],
        outputs=[query1, ans_output1, references1],
    )

    # For Quran
    # Submit button for submitting query.
    submit_button2.click(qa_quran, inputs=[query2, openaikey], outputs=[
                         ans_output2, references2])
    # Clear button for clearing query and answer.
    clear_button2.click(
        cleartext,
        inputs=[query2, ans_output2, references2],
        outputs=[query2, ans_output2, references2],
    )

    # For Bible
    # Submit button for submitting query.
    submit_button3.click(qa_bible, inputs=[query3, openaikey], outputs=[
                         ans_output3, references3])
    # Clear button for clearing query and answer.
    clear_button3.click(
        cleartext,
        inputs=[query3, ans_output3, references3],
        outputs=[query3, ans_output3, references3],
    )
demo.launch(debug=True)