# # -*- coding: utf-8 -*-
# """fiver-app.ipynb

# Automatically generated by Colaboratory.

# Original file is located at
#     https://colab.research.google.com/drive/1YQm_fGxa2nfiV8pTN4oBrlzzfefGadaP
# """

# !pip uninstall -y numpy
# !pip install --ignore-installed numpy==1.22.0

# !pip install langchain
# !pip install PyPDF2
# !pip install docx2txt
# !pip install gradio
# !pip install faiss-gpu
# !pip install openai
# !pip install tiktoken
# !pip install python-docx

# !pip install git+https://github.com/openai/whisper.git
# !pip install sounddevice

# import shutil
# import os

# def copy_files(source_folder, destination_folder):
#     # Create the destination folder if it doesn't exist
#     if not os.path.exists(destination_folder):
#         os.makedirs(destination_folder)

#     # Get a list of files in the source folder

#     files_to_copy = os.listdir(source_folder)
#     for file_name in files_to_copy:
#         source_file_path = os.path.join(source_folder, file_name)
#         destination_file_path = os.path.join(destination_folder, file_name)

#         # Copy the file to the destination folder
#         shutil.copy(source_file_path, destination_file_path)

#         print(f"Copied {file_name} to {destination_folder}")

# # Specify the source folder and destination folder paths
# source_folder = "/kaggle/input/fiver-app5210"
# destination_folder = "/home/user/app/local_db"

# copy_files(source_folder, destination_folder)

# import shutil
# import os

# def copy_files(source_folder, destination_folder):
#     # Create the destination folder if it doesn't exist
#     if not os.path.exists(destination_folder):
#         os.makedirs(destination_folder)

#     # Get a list of files in the source folder
#     files_to_copy = os.listdir(source_folder)

#     for file_name in files_to_copy:
#         source_file_path = os.path.join(source_folder, file_name)
#         destination_file_path = os.path.join(destination_folder, file_name)

#         # Copy the file to the destination folder
#         shutil.copy(source_file_path, destination_file_path)

#         print(f"Copied {file_name} to {destination_folder}")

# # Specify the source folder and destination folder paths
# source_folder = "/kaggle/input/fiver-app-docs"
# destination_folder = "/home/user/app/docs"

# copy_files(source_folder, destination_folder)


def api_key(key):
    import os
    import openai

    os.environ["TOKENIZERS_PARALLELISM"] = "false"
    os.environ["OPENAI_API_KEY"] = key
    openai.api_key = key

    return "Successful!"


def save_file(input_file):
    import shutil
    import os

    destination_dir = "/home/user/app/file/"
    os.makedirs(destination_dir, exist_ok=True)

    output_dir = "/home/user/app/file/"

    for file in input_file:
        shutil.copy(file.name, output_dir)

    return "File(s) saved successfully!"


def process_file():
    from langchain.document_loaders import PyPDFLoader
    from langchain.document_loaders import DirectoryLoader
    from langchain.document_loaders import TextLoader
    from langchain.document_loaders import Docx2txtLoader
    from langchain.vectorstores import FAISS
    from langchain.embeddings.openai import OpenAIEmbeddings
    from langchain.text_splitter import CharacterTextSplitter
    import openai

    loader1 = DirectoryLoader(
        "/home/user/app/file/", glob="./*.pdf", loader_cls=PyPDFLoader
    )
    document1 = loader1.load()

    loader2 = DirectoryLoader(
        "/home/user/app/file/", glob="./*.txt", loader_cls=TextLoader
    )
    document2 = loader2.load()

    loader3 = DirectoryLoader(
        "/home/user/app/file/", glob="./*.docx", loader_cls=Docx2txtLoader
    )
    document3 = loader3.load()

    document1.extend(document2)
    document1.extend(document3)

    text_splitter = CharacterTextSplitter(
        separator="\n", chunk_size=1000, chunk_overlap=200, length_function=len
    )

    docs = text_splitter.split_documents(document1)
    embeddings = OpenAIEmbeddings()

    file_db = FAISS.from_documents(docs, embeddings)
    file_db.save_local("/home/user/app/file_db/")

    return "File(s) processed successfully!"


def formatted_response(docs, response):
    formatted_output = response + "\n\nSources"

    for i, doc in enumerate(docs):
        source_info = doc.metadata.get("source", "Unknown source")
        page_info = doc.metadata.get("page", None)

        # Get the file name without the directory path
        file_name = source_info.split("/")[-1].strip()

        if page_info is not None:
            formatted_output += f"\n{file_name}\tpage no {page_info}"
        else:
            formatted_output += f"\n{file_name}"

    return formatted_output


def search_file(question):
    from langchain.embeddings.openai import OpenAIEmbeddings
    from langchain.vectorstores import FAISS
    from langchain.chains.question_answering import load_qa_chain
    from langchain.callbacks import get_openai_callback
    from langchain.llms import OpenAI
    import openai
    from langchain.chat_models import ChatOpenAI

    embeddings = OpenAIEmbeddings()
    file_db = FAISS.load_local("/home/user/app/file_db/", embeddings)
    docs = file_db.similarity_search(question)

    llm = ChatOpenAI(model_name="gpt-3.5-turbo")
    chain = load_qa_chain(llm, chain_type="stuff")
    with get_openai_callback() as cb:
        response = chain.run(input_documents=docs, question=question)
        print(cb)

    return formatted_response(docs, response)


def search_local(question):
    from langchain.embeddings.openai import OpenAIEmbeddings
    from langchain.vectorstores import FAISS
    from langchain.chains.question_answering import load_qa_chain
    from langchain.callbacks import get_openai_callback
    from langchain.llms import OpenAI
    import openai
    from langchain.chat_models import ChatOpenAI

    embeddings = OpenAIEmbeddings()
    file_db = FAISS.load_local("/home/user/app/local_db/", embeddings)
    docs = file_db.similarity_search(question)

    print(docs)
    type(docs)
    llm = ChatOpenAI(model_name="gpt-3.5-turbo")
    chain = load_qa_chain(llm, chain_type="stuff")
    with get_openai_callback() as cb:
        response = chain.run(input_documents=docs, question=question)
        print(cb)

    return formatted_response(docs, response)


def delete_file():
    import shutil

    path1 = "/home/user/app/file/"
    path2 = "/home/user/app/file_db/"

    try:
        shutil.rmtree(path1)
        shutil.rmtree(path2)
        return "Deleted Successfully"

    except:
        return "Already Deleted"


import os
import gradio as gr


def list_files():
    directory = "/home/user/app/docs"
    file_list = []
    for root, dirs, files in os.walk(directory):
        for file in files:
            file_list.append(file)
    return gr.Dropdown.update(choices=file_list)


file_list = list_files()

print("List of file names in the directory:")
for file_name in file_list:
    print(file_name)


def soap_report(doc_name, question):
    from langchain.llms import OpenAI
    from langchain import PromptTemplate, LLMChain
    import openai
    import docx

    docx_path = "/home/user/app/docs/" + doc_name

    doc = docx.Document(docx_path)
    extracted_text = "Extracted text:\n\n\n"

    for paragraph in doc.paragraphs:
        extracted_text += paragraph.text + "\n"

    question = (
        "\n\nUse the 'Extracted text' to answer the following question:\n" + question
    )
    extracted_text += question

    if extracted_text:
        print(extracted_text)
    else:
        print("failed")

    template = """Question: {question}

    Answer: Let's think step by step."""

    prompt = PromptTemplate(template=template, input_variables=["question"])
    llm = OpenAI()
    llm_chain = LLMChain(prompt=prompt, llm=llm)
    response = llm_chain.run(extracted_text)

    return response


def search_gpt(question):
    from langchain.llms import OpenAI
    from langchain import PromptTemplate, LLMChain

    template = """Question: {question}

    Answer: Let's think step by step."""

    prompt = PromptTemplate(template=template, input_variables=["question"])
    llm = OpenAI()
    llm_chain = LLMChain(prompt=prompt, llm=llm)
    response = llm_chain.run(question)

    return response


def local_gpt(question):
    from langchain.llms import OpenAI
    from langchain import PromptTemplate, LLMChain

    template = """Question: {question}

    Answer: Let's think step by step."""

    prompt = PromptTemplate(template=template, input_variables=["question"])
    llm = OpenAI()
    llm_chain = LLMChain(prompt=prompt, llm=llm)
    response = llm_chain.run(question)

    return response


global output
global response


def audio_text(filepath):
    import openai

    global output

    audio = open(filepath, "rb")
    transcript = openai.Audio.transcribe("whisper-1", audio)
    output = transcript["text"]

    return output


def transcript(text):
    from langchain.llms import OpenAI
    from langchain import PromptTemplate, LLMChain

    global response

    question = (
        "Use the following context given below to generate a detailed SOAP Report:\n\n"
    )
    question += text
    print(question)

    template = """Question: {question}

    Answer: Let's think step by step."""

    prompt = PromptTemplate(template=template, input_variables=["question"])
    llm = OpenAI()
    llm_chain = LLMChain(prompt=prompt, llm=llm)
    response = llm_chain.run(question)

    return response


def text_soap():
    from langchain.llms import OpenAI
    from langchain import PromptTemplate, LLMChain

    global output
    global response
    output = output

    question = (
        "Use the following context given below to generate a detailed SOAP Report:\n\n"
    )
    question += output
    print(question)

    template = """Question: {question}

    Answer: Let's think step by step."""

    prompt = PromptTemplate(template=template, input_variables=["question"])
    llm = OpenAI()
    llm_chain = LLMChain(prompt=prompt, llm=llm)
    response = llm_chain.run(question)

    return response


global path


def docx(name):
    global response
    response = response
    import docx

    global path
    path = f"/home/user/app/docs/{name}.docx"

    doc = docx.Document()
    doc.add_paragraph(response)
    doc.save(path)

    return "Successfully saved .docx File"


import gradio as gr

css = """
.col{
    max-width: 50%;
    margin: 0 auto;
    display: flex;
    flex-direction: column;
    justify-content: center;
    align-items: center;
}
"""

with gr.Blocks(css=css) as demo:
    gr.Markdown("File Chatting App")

    with gr.Tab("Chat with Files"):
        with gr.Column(elem_classes="col"):
            with gr.Tab("Upload and Process Files"):
                with gr.Column():
                    api_key_input = gr.Textbox(label="Enter API Key here")
                    api_key_button = gr.Button("Submit")
                    api_key_output = gr.Textbox(label="Output")

                    file_input = gr.Files(label="Upload File(s) here")
                    upload_button = gr.Button("Upload")
                    file_output = gr.Textbox(label="Output")

                    process_button = gr.Button("Process")
                    process_output = gr.Textbox(label="Output")

            with gr.Tab("Ask Questions to Files"):
                with gr.Column():
                    search_input = gr.Textbox(label="Enter Question here")
                    search_button = gr.Button("Search")
                    search_output = gr.Textbox(label="Output")

                    search_gpt_button = gr.Button("Ask ChatGPT")
                    search_gpt_output = gr.Textbox(label="Output")

                    delete_button = gr.Button("Delete")
                    delete_output = gr.Textbox(label="Output")

    with gr.Tab("Chat with Local Files"):
        with gr.Column(elem_classes="col"):
            local_search_input = gr.Textbox(label="Enter Question here")
            local_search_button = gr.Button("Search")
            local_search_output = gr.Textbox(label="Output")

            local_gpt_button = gr.Button("Ask ChatGPT")
            local_gpt_output = gr.Textbox(label="Output")

    with gr.Tab("Ask Question to SOAP Report"):
        with gr.Column(elem_classes="col"):
            refresh_button = gr.Button("Refresh")
            soap_input = gr.Dropdown(label="Choose File")
            soap_question = gr.Textbox(label="Enter Question here")
            soap_button = gr.Button("Submit")
            soap_output = gr.Textbox(label="Output")

    with gr.Tab("Convert Audio to SOAP Report"):
        with gr.Column(elem_classes="col"):
            mic_text_input = gr.Audio(
                source="microphone", type="filepath", label="Speak to the Microphone"
            )
            mic_text_button = gr.Button("Generate Transcript")
            mic_text_output = gr.Textbox(label="Output")

            upload_text_input = gr.Audio(
                source="upload", type="filepath", label="Upload Audio File here"
            )
            upload_text_button = gr.Button("Generate Transcript")
            upload_text_output = gr.Textbox(label="Output")

            transcript_input = gr.Textbox(label="Enter Transcript here")
            transcript_button = gr.Button("Generate SOAP Report")
            transcript_output = gr.Textbox(label="Output")

            text_soap_button = gr.Button("Generate SOAP Report")
            text_soap_output = gr.Textbox(label="Output")

            docx_input = gr.Textbox(label="Enter the name of .docx File")
            docx_button = gr.Button("Save .docx File")
            docx_output = gr.Textbox(label="Output")

    api_key_button.click(api_key, inputs=api_key_input, outputs=api_key_output)

    upload_button.click(save_file, inputs=file_input, outputs=file_output)

    process_button.click(process_file, inputs=None, outputs=process_output)

    search_button.click(search_file, inputs=search_input, outputs=search_output)
    search_gpt_button.click(search_gpt, inputs=search_input, outputs=search_gpt_output)

    delete_button.click(delete_file, inputs=None, outputs=delete_output)

    local_search_button.click(
        search_local, inputs=local_search_input, outputs=local_search_output
    )
    local_gpt_button.click(
        local_gpt, inputs=local_search_input, outputs=local_gpt_output
    )

    refresh_button.click(list_files, inputs=None, outputs=soap_input)
    soap_button.click(
        soap_report, inputs=[soap_input, soap_question], outputs=soap_output
    )

    mic_text_button.click(audio_text, inputs=mic_text_input, outputs=mic_text_output)
    upload_text_button.click(
        audio_text, inputs=upload_text_input, outputs=upload_text_output
    )

    transcript_button.click(
        transcript, inputs=transcript_input, outputs=transcript_output
    )
    text_soap_button.click(text_soap, inputs=None, outputs=text_soap_output)
    docx_button.click(docx, inputs=docx_input, outputs=docx_output)


demo.queue()
demo.launch()