# Imports
import base64
import glob
import json
import math
#import mistune
import openai
import os
import pytz
import re
import requests
import streamlit as st
import textract
import time
import zipfile
import huggingface_hub
import dotenv
from audio_recorder_streamlit import audio_recorder
from bs4 import BeautifulSoup
from collections import deque
from datetime import datetime
from dotenv import load_dotenv
from huggingface_hub import InferenceClient
from io import BytesIO
from langchain.chat_models import ChatOpenAI
from langchain.chains import ConversationalRetrievalChain
from langchain.embeddings import OpenAIEmbeddings
from langchain.memory import ConversationBufferMemory
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import FAISS
from openai import ChatCompletion
from PyPDF2 import PdfReader
from templates import bot_template, css, user_template
from xml.etree import ElementTree as ET

def add_Med_Licensing_Exam_Dataset():
    import streamlit as st
    from datasets import load_dataset
    dataset = load_dataset("augtoma/usmle_step_1")['test']  # Using 'test' split
    st.title("USMLE Step 1 Dataset Viewer")
    if len(dataset) == 0:
        st.write("😢 The dataset is empty.")
    else:
        st.write("""
        🔍 Use the search box to filter questions or use the grid to scroll through the dataset.
        """)
    
        # 👩‍🔬 Search Box
        search_term = st.text_input("Search for a specific question:", "")
        # 🎛 Pagination
        records_per_page = 100
        num_records = len(dataset)
        num_pages = max(int(num_records / records_per_page), 1)
        
        # Skip generating the slider if num_pages is 1 (i.e., all records fit in one page)
        if num_pages > 1:
            page_number = st.select_slider("Select page:", options=list(range(1, num_pages + 1)))
        else:
            page_number = 1  # Only one page
        
        # 📊 Display Data
        start_idx = (page_number - 1) * records_per_page
        end_idx = start_idx + records_per_page
    
        # 🧪 Apply the Search Filter
        filtered_data = []
        for record in dataset[start_idx:end_idx]:
            if isinstance(record, dict) and 'text' in record and 'id' in record:
                if search_term:
                    if search_term.lower() in record['text'].lower():
                        filtered_data.append(record)
                else:
                    filtered_data.append(record)
    
        # 🌐 Render the Grid
        for record in filtered_data:
            st.write(f"## Question ID: {record['id']}")
            st.write(f"### Question:")
            st.write(f"{record['text']}")
            st.write(f"### Answer:")
            st.write(f"{record['answer']}")
            st.write("---")
    
        st.write(f"😊 Total Records: {num_records} | 📄 Displaying {start_idx+1} to {min(end_idx, num_records)}")



# 1. Constants and Top Level UI Variables

# My Inference API Copy
# API_URL = 'https://qe55p8afio98s0u3.us-east-1.aws.endpoints.huggingface.cloud'  # Dr Llama
# Original:
API_URL = "https://api-inference.huggingface.co/models/meta-llama/Llama-2-7b-chat-hf"
API_KEY = os.getenv('API_KEY')
MODEL1="meta-llama/Llama-2-7b-chat-hf"
MODEL1URL="https://huggingface.co/meta-llama/Llama-2-7b-chat-hf"
HF_KEY = os.getenv('HF_KEY')
headers = {
    "Authorization": f"Bearer {HF_KEY}",
    "Content-Type": "application/json"
}
key = os.getenv('OPENAI_API_KEY')
prompt = f"Write instructions to teach anyone to write a discharge plan. List the entities, features and relationships to CCDA and FHIR objects in boldface."
# page config and sidebar declares up front allow all other functions to see global class variables
# st.set_page_config(page_title="GPT Streamlit Document Reasoner", layout="wide")
should_save = st.sidebar.checkbox("💾 Save", value=True, help="Save your session data.")

# 2. Prompt label button demo for LLM
def add_witty_humor_buttons():
    with st.expander("Wit and Humor 🤣", expanded=True):
        # Tip about the Dromedary family
        st.markdown("🔬 **Fun Fact**: Dromedaries, part of the camel family, have a single hump and are adapted to arid environments. Their 'superpowers' include the ability to survive without water for up to 7 days, thanks to their specialized blood cells and water storage in their hump.")
        
        # Define button descriptions
        descriptions = {
            "Generate Limericks 😂": "Write ten random adult limericks based on quotes that are tweet length and make you laugh 🎭",
            "Wise Quotes 🧙": "Generate ten wise quotes that are tweet length 🦉",
            "Funny Rhymes 🎤": "Create ten funny rhymes that are tweet length 🎶",
            "Medical Jokes 💉": "Create ten medical jokes that are tweet length 🏥",
            "Minnesota Humor ❄️": "Create ten jokes about Minnesota that are tweet length 🌨️",
            "Top Funny Stories 📖": "Create ten funny stories that are tweet length 📚",
            "More Funny Rhymes 🎙️": "Create ten more funny rhymes that are tweet length 🎵"
        }
        
        # Create columns
        col1, col2, col3 = st.columns([1, 1, 1], gap="small")
        
        # Add buttons to columns
        if col1.button("Generate Limericks 😂"):
            StreamLLMChatResponse(descriptions["Generate Limericks 😂"])
        
        if col2.button("Wise Quotes 🧙"):
            StreamLLMChatResponse(descriptions["Wise Quotes 🧙"])
        
        if col3.button("Funny Rhymes 🎤"):
            StreamLLMChatResponse(descriptions["Funny Rhymes 🎤"])
        
        col4, col5, col6 = st.columns([1, 1, 1], gap="small")
        
        if col4.button("Medical Jokes 💉"):
            StreamLLMChatResponse(descriptions["Medical Jokes 💉"])
        
        if col5.button("Minnesota Humor ❄️"):
            StreamLLMChatResponse(descriptions["Minnesota Humor ❄️"])
        
        if col6.button("Top Funny Stories 📖"):
            StreamLLMChatResponse(descriptions["Top Funny Stories 📖"])
        
        col7 = st.columns(1, gap="small")
        
        if col7[0].button("More Funny Rhymes 🎙️"):
            StreamLLMChatResponse(descriptions["More Funny Rhymes 🎙️"])

def addDocumentHTML5(result):
    documentHTML5='''
    <!DOCTYPE html>
    <html>
    <head>
        <title>Read It Aloud</title>
        <script type="text/javascript">
            function readAloud() {
                const text = document.getElementById("textArea").value;
                const speech = new SpeechSynthesisUtterance(text);
                window.speechSynthesis.speak(speech);
            }
        </script>
    </head>
    <body>
        <h1>🔊 Read It Aloud</h1>
        <textarea id="textArea" rows="10" cols="80">
    '''
    documentHTML5 = documentHTML5 + result
    documentHTML5 = documentHTML5 + '''
        </textarea>
        <br>
        <button onclick="readAloud()">🔊 Read Aloud</button>
    </body>
    </html>
    '''

    import streamlit.components.v1 as components  # Import Streamlit
    components.html(documentHTML5, width=1280, height=1024)
    return result


# 3. Stream Llama Response
# @st.cache_resource
def StreamLLMChatResponse(prompt):

    try:
        endpoint_url = API_URL
        hf_token = API_KEY
        client = InferenceClient(endpoint_url, token=hf_token)
        gen_kwargs = dict(
            max_new_tokens=512,
            top_k=30,
            top_p=0.9,
            temperature=0.2,
            repetition_penalty=1.02,
            stop_sequences=["\nUser:", "<|endoftext|>", "</s>"],
        )
        stream = client.text_generation(prompt, stream=True, details=True, **gen_kwargs)
        report=[]
        res_box = st.empty()
        collected_chunks=[]
        collected_messages=[]
        allresults=''
        for r in stream:
            if r.token.special:
                continue
            if r.token.text in gen_kwargs["stop_sequences"]:
                break
            collected_chunks.append(r.token.text)
            chunk_message = r.token.text
            collected_messages.append(chunk_message)
            try:
                report.append(r.token.text)
                if len(r.token.text) > 0:
                    result="".join(report).strip()
                    res_box.markdown(f'*{result}*')
                    
            except:
                st.write('Stream llm issue')
        add_documentHTML5(result)
    except:
        st.write('Llama model is asleep. Starting up now on A10 - please give 5 minutes then retry as KEDA scales up from zero to activate running container(s).')

# 4. Run query with payload
def query(payload):
    response = requests.post(API_URL, headers=headers, json=payload)
    st.markdown(response.json())
    return response.json()
def get_output(prompt):
    return query({"inputs": prompt})

# 5. Auto name generated output files from time and content
def generate_filename(prompt, file_type):
    central = pytz.timezone('US/Central')
    safe_date_time = datetime.now(central).strftime("%m%d_%H%M")
    replaced_prompt = prompt.replace(" ", "_").replace("\n", "_")
    safe_prompt = "".join(x for x in replaced_prompt if x.isalnum() or x == "_")[:45]
    return f"{safe_date_time}_{safe_prompt}.{file_type}"

# 6. Speech transcription via OpenAI service
def transcribe_audio(openai_key, file_path, model):
    openai.api_key = openai_key
    OPENAI_API_URL = "https://api.openai.com/v1/audio/transcriptions"
    headers = {
        "Authorization": f"Bearer {openai_key}",
    }
    with open(file_path, 'rb') as f:
        data = {'file': f}
        response = requests.post(OPENAI_API_URL, headers=headers, files=data, data={'model': model})
    if response.status_code == 200:
        st.write(response.json())
        chatResponse = chat_with_model(response.json().get('text'), '') # *************************************
        transcript = response.json().get('text')
        filename = generate_filename(transcript, 'txt')
        response = chatResponse
        user_prompt = transcript
        create_file(filename, user_prompt, response, should_save)
        return transcript
    else:
        st.write(response.json())
        st.error("Error in API call.")
        return None

# 7. Auto stop on silence audio control for recording WAV files
def save_and_play_audio(audio_recorder):
    audio_bytes = audio_recorder(key='audio_recorder')
    if audio_bytes:
        filename = generate_filename("Recording", "wav")
        with open(filename, 'wb') as f:
            f.write(audio_bytes)
        st.audio(audio_bytes, format="audio/wav")
        return filename
    return None

# 8. File creator that interprets type and creates output file for text, markdown and code
def create_file(filename, prompt, response, should_save=True):
    if not should_save:
        return
    base_filename, ext = os.path.splitext(filename)
    has_python_code = bool(re.search(r"```python([\s\S]*?)```", response))
    if ext in ['.txt', '.htm', '.md']:
        with open(f"{base_filename}.md", 'w') as file:
            content = prompt.strip() + '\r\n' + response
            file.write(content)
        if has_python_code:
            python_code = re.findall(r"```python([\s\S]*?)```", response)[0].strip()
            with open(f"{base_filename}-Code.py", 'w') as file:
                file.write(python_code)
            with open(f"{base_filename}.md", 'w') as file:
                content = prompt.strip() + '\r\n' + response
                file.write(content)
            
def truncate_document(document, length):
    return document[:length]
def divide_document(document, max_length):
    return [document[i:i+max_length] for i in range(0, len(document), max_length)]

# 9. Sidebar with UI controls to review and re-run prompts and continue responses
@st.cache_resource
def get_table_download_link(file_path):
    with open(file_path, 'r') as file:
        data = file.read()
   
    b64 = base64.b64encode(data.encode()).decode()  
    file_name = os.path.basename(file_path)
    ext = os.path.splitext(file_name)[1]  # get the file extension
    if ext == '.txt':
        mime_type = 'text/plain'
    elif ext == '.py':
        mime_type = 'text/plain'
    elif ext == '.xlsx':
        mime_type = 'text/plain'
    elif ext == '.csv':
        mime_type = 'text/plain'
    elif ext == '.htm':
        mime_type = 'text/html'
    elif ext == '.md':
        mime_type = 'text/markdown'
    else:
        mime_type = 'application/octet-stream'  # general binary data type
    href = f'<a href="data:{mime_type};base64,{b64}" target="_blank" download="{file_name}">{file_name}</a>'
    return href


def CompressXML(xml_text):
    root = ET.fromstring(xml_text)
    for elem in list(root.iter()):
        if isinstance(elem.tag, str) and 'Comment' in elem.tag:
            elem.parent.remove(elem)
    return ET.tostring(root, encoding='unicode', method="xml")

# 10. Read in and provide UI for past files
@st.cache_resource
def read_file_content(file,max_length):
    if file.type == "application/json":
        content = json.load(file)
        return str(content)
    elif file.type == "text/html" or file.type == "text/htm":
        content = BeautifulSoup(file, "html.parser")
        return content.text
    elif file.type == "application/xml" or file.type == "text/xml":
        tree = ET.parse(file)
        root = tree.getroot()
        xml = CompressXML(ET.tostring(root, encoding='unicode'))
        return xml
    elif file.type == "text/markdown" or file.type == "text/md":
        md = mistune.create_markdown()
        content = md(file.read().decode())
        return content
    elif file.type == "text/plain":
        return file.getvalue().decode()
    else:
        return ""

# 11. Chat with GPT - Caution on quota - now favoring fastest AI pipeline STT Whisper->LLM Llama->TTS
@st.cache_resource
def chat_with_model(prompt, document_section, model_choice='gpt-3.5-turbo'):
    model = model_choice
    conversation = [{'role': 'system', 'content': 'You are a helpful assistant.'}]
    conversation.append({'role': 'user', 'content': prompt})
    if len(document_section)>0:
        conversation.append({'role': 'assistant', 'content': document_section})
    start_time = time.time()
    report = []
    res_box = st.empty()
    collected_chunks = []
    collected_messages = []
    for chunk in openai.ChatCompletion.create(model='gpt-3.5-turbo', messages=conversation, temperature=0.5, stream=True):
        collected_chunks.append(chunk)  
        chunk_message = chunk['choices'][0]['delta']  
        collected_messages.append(chunk_message) 
        content=chunk["choices"][0].get("delta",{}).get("content")
        try:
            report.append(content)
            if len(content) > 0:
                result = "".join(report).strip()
                res_box.markdown(f'*{result}*') 
        except:
            st.write(' ')
    full_reply_content = ''.join([m.get('content', '') for m in collected_messages])
    st.write("Elapsed time:")
    st.write(time.time() - start_time)
    return full_reply_content

# 12. Embedding VectorDB for LLM query of documents to text to compress inputs and prompt together as Chat memory using Langchain
@st.cache_resource
def chat_with_file_contents(prompt, file_content, model_choice='gpt-3.5-turbo'):
    conversation = [{'role': 'system', 'content': 'You are a helpful assistant.'}]
    conversation.append({'role': 'user', 'content': prompt})
    if len(file_content)>0:
        conversation.append({'role': 'assistant', 'content': file_content})
    response = openai.ChatCompletion.create(model=model_choice, messages=conversation)
    return response['choices'][0]['message']['content']

def extract_mime_type(file):
    if isinstance(file, str):
        pattern = r"type='(.*?)'"
        match = re.search(pattern, file)
        if match:
            return match.group(1)
        else:
            raise ValueError(f"Unable to extract MIME type from {file}")
    elif isinstance(file, streamlit.UploadedFile):
        return file.type
    else:
        raise TypeError("Input should be a string or a streamlit.UploadedFile object")

def extract_file_extension(file):
    # get the file name directly from the UploadedFile object
    file_name = file.name
    pattern = r".*?\.(.*?)$"
    match = re.search(pattern, file_name)
    if match:
        return match.group(1)
    else:
        raise ValueError(f"Unable to extract file extension from {file_name}")

# Normalize input as text from PDF and other formats
@st.cache_resource
def pdf2txt(docs):
    text = ""
    for file in docs:
        file_extension = extract_file_extension(file)
        st.write(f"File type extension: {file_extension}")
        if file_extension.lower() in ['py', 'txt', 'html', 'htm', 'xml', 'json']:
            text += file.getvalue().decode('utf-8')
        elif file_extension.lower() == 'pdf':
            from PyPDF2 import PdfReader
            pdf = PdfReader(BytesIO(file.getvalue()))
            for page in range(len(pdf.pages)):
                text += pdf.pages[page].extract_text() # new PyPDF2 syntax
    return text

def txt2chunks(text):
    text_splitter = CharacterTextSplitter(separator="\n", chunk_size=1000, chunk_overlap=200, length_function=len)
    return text_splitter.split_text(text)

# Vector Store using FAISS
@st.cache_resource
def vector_store(text_chunks):
    embeddings = OpenAIEmbeddings(openai_api_key=key)
    return FAISS.from_texts(texts=text_chunks, embedding=embeddings)

# Memory and Retrieval chains
@st.cache_resource
def get_chain(vectorstore):
    llm = ChatOpenAI()
    memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)
    return ConversationalRetrievalChain.from_llm(llm=llm, retriever=vectorstore.as_retriever(), memory=memory)

def process_user_input(user_question):
    response = st.session_state.conversation({'question': user_question})
    st.session_state.chat_history = response['chat_history']
    for i, message in enumerate(st.session_state.chat_history):
        template = user_template if i % 2 == 0 else bot_template
        st.write(template.replace("{{MSG}}", message.content), unsafe_allow_html=True)
        filename = generate_filename(user_question, 'txt')
        response = message.content
        user_prompt = user_question
        create_file(filename, user_prompt, response, should_save)       

def divide_prompt(prompt, max_length):
    words = prompt.split()
    chunks = []
    current_chunk = []
    current_length = 0
    for word in words:
        if len(word) + current_length <= max_length:
            current_length += len(word) + 1 
            current_chunk.append(word)
        else:
            chunks.append(' '.join(current_chunk))
            current_chunk = [word]
            current_length = len(word)
    chunks.append(' '.join(current_chunk))
    return chunks

    
# 13. Provide way of saving all and deleting all to give way of reviewing output and saving locally before clearing it
    
@st.cache_resource
def create_zip_of_files(files):
    zip_name = "all_files.zip"
    with zipfile.ZipFile(zip_name, 'w') as zipf:
        for file in files:
            zipf.write(file)
    return zip_name
    
@st.cache_resource
def get_zip_download_link(zip_file):
    with open(zip_file, 'rb') as f:
        data = f.read()
    b64 = base64.b64encode(data).decode()
    href = f'<a href="data:application/zip;base64,{b64}" download="{zip_file}">Download All</a>'
    return href

# 14. Inference Endpoints for Whisper (best fastest STT) on NVIDIA T4 and Llama (best fastest AGI LLM) on NVIDIA A10
# My Inference Endpoint
API_URL_IE = f'https://tonpixzfvq3791u9.us-east-1.aws.endpoints.huggingface.cloud'
# Original
API_URL_IE = "https://api-inference.huggingface.co/models/openai/whisper-small.en"
MODEL2 = "openai/whisper-small.en"
MODEL2_URL = "https://huggingface.co/openai/whisper-small.en"
#headers = {
#	"Authorization": "Bearer XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX",
#	"Content-Type": "audio/wav"
#}
HF_KEY = os.getenv('HF_KEY')
headers = {
    "Authorization": f"Bearer {HF_KEY}",
    "Content-Type": "audio/wav"
}

#@st.cache_resource
def query(filename):
    with open(filename, "rb") as f:
        data = f.read()
    response = requests.post(API_URL_IE, headers=headers, data=data)
    return response.json()

def generate_filename(prompt, file_type):
    central = pytz.timezone('US/Central')
    safe_date_time = datetime.now(central).strftime("%m%d_%H%M")
    replaced_prompt = prompt.replace(" ", "_").replace("\n", "_")
    safe_prompt = "".join(x for x in replaced_prompt if x.isalnum() or x == "_")[:90]
    return f"{safe_date_time}_{safe_prompt}.{file_type}"

# 15. Audio recorder to Wav file 
def save_and_play_audio(audio_recorder):
    audio_bytes = audio_recorder()
    if audio_bytes:
        filename = generate_filename("Recording", "wav")
        with open(filename, 'wb') as f:
            f.write(audio_bytes)
        st.audio(audio_bytes, format="audio/wav")
        return filename

# 16. Speech transcription to file output
def transcribe_audio(filename):
    output = query(filename)
    return output

def whisper_main():
    st.title("Speech to Text")
    st.write("Record your speech and get the text.")

    # Audio, transcribe, GPT:
    filename = save_and_play_audio(audio_recorder)
    if filename is not None:
        transcription = transcribe_audio(filename)
        try:
            transcription = transcription['text']
        except:
            st.write('Whisper model is asleep. Starting up now on T4 GPU - please give 5 minutes then retry as it scales up from zero to activate running container(s).')

        st.write(transcription)
        response = StreamLLMChatResponse(transcription)
        # st.write(response) - redundant with streaming result?
        filename = generate_filename(transcription, ".txt")
        create_file(filename, transcription, response, should_save)
        #st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)


# 17. Main
def main():

    st.title("AI Drome Llama")
    prompt = f"Write ten funny jokes that are tweet length stories that make you laugh.  Show as markdown outline with emojis for each."

    # Add Wit and Humor buttons
    add_witty_humor_buttons()

    example_input = st.text_input("Enter your example text:", value=prompt, help="Enter text to get a response from DromeLlama.")
    if st.button("Run Prompt With DromeLlama", help="Click to run the prompt."):
        try:
            StreamLLMChatResponse(example_input)
        except:
            st.write('DromeLlama is asleep. Starting up now on A10 - please give 5 minutes then retry as KEDA scales up from zero to activate running container(s).')

    openai.api_key = os.getenv('OPENAI_KEY')
    menu = ["txt", "htm", "xlsx", "csv", "md", "py"]
    choice = st.sidebar.selectbox("Output File Type:", menu)
    model_choice = st.sidebar.radio("Select Model:", ('gpt-3.5-turbo', 'gpt-3.5-turbo-0301'))        
    user_prompt = st.text_area("Enter prompts, instructions & questions:", '', height=100)
    collength, colupload = st.columns([2,3])  # adjust the ratio as needed
    with collength:
        max_length = st.slider("File section length for large files", min_value=1000, max_value=128000, value=12000, step=1000)
    with colupload:
        uploaded_file = st.file_uploader("Add a file for context:", type=["pdf", "xml", "json", "xlsx", "csv", "html", "htm", "md", "txt"])
    document_sections = deque()
    document_responses = {}
    if uploaded_file is not None:
        file_content = read_file_content(uploaded_file, max_length)
        document_sections.extend(divide_document(file_content, max_length))
    if len(document_sections) > 0:
        if st.button("👁️ View Upload"):
            st.markdown("**Sections of the uploaded file:**")
            for i, section in enumerate(list(document_sections)):
                st.markdown(f"**Section {i+1}**\n{section}")
        st.markdown("**Chat with the model:**")
        for i, section in enumerate(list(document_sections)):
            if i in document_responses:
                st.markdown(f"**Section {i+1}**\n{document_responses[i]}")
            else:
                if st.button(f"Chat about Section {i+1}"):
                    st.write('Reasoning with your inputs...')
                    response = chat_with_model(user_prompt, section, model_choice)
                    st.write('Response:')
                    st.write(response)
                    document_responses[i] = response
                    filename = generate_filename(f"{user_prompt}_section_{i+1}", choice)
                    create_file(filename, user_prompt, response, should_save)
                    st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)
    if st.button('💬 Chat'):
        st.write('Reasoning with your inputs...')
        user_prompt_sections = divide_prompt(user_prompt, max_length)
        full_response = ''
        for prompt_section in user_prompt_sections:
            response = chat_with_model(prompt_section, ''.join(list(document_sections)), model_choice)
            full_response += response + '\n'  # Combine the responses
        response = full_response
        st.write('Response:')
        st.write(response)
        filename = generate_filename(user_prompt, choice)
        create_file(filename, user_prompt, response, should_save)
        st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)

    # Compose a file sidebar of past encounters
    all_files = glob.glob("*.*")
    all_files = [file for file in all_files if len(os.path.splitext(file)[0]) >= 20]  # exclude files with short names
    all_files.sort(key=lambda x: (os.path.splitext(x)[1], x), reverse=True)  # sort by file type and file name in descending order
    if st.sidebar.button("🗑 Delete All"):
        for file in all_files:
            os.remove(file)
        st.experimental_rerun()
    if st.sidebar.button("⬇️ Download All"):
        zip_file = create_zip_of_files(all_files)
        st.sidebar.markdown(get_zip_download_link(zip_file), unsafe_allow_html=True)
    file_contents=''
    next_action=''
    for file in all_files:
        col1, col2, col3, col4, col5 = st.sidebar.columns([1,6,1,1,1])  # adjust the ratio as needed
        with col1:
            if st.button("🌐", key="md_"+file):  # md emoji button
                with open(file, 'r') as f:
                    file_contents = f.read()
                    next_action='md'
        with col2:
            st.markdown(get_table_download_link(file), unsafe_allow_html=True)
        with col3:
            if st.button("📂", key="open_"+file):  # open emoji button
                with open(file, 'r') as f:
                    file_contents = f.read()
                    next_action='open'
        with col4:
            if st.button("🔍", key="read_"+file):  # search emoji button
                with open(file, 'r') as f:
                    file_contents = f.read()
                    next_action='search'
        with col5:
            if st.button("🗑", key="delete_"+file):
                os.remove(file)
                st.experimental_rerun()

                
    if len(file_contents) > 0:
        if next_action=='open':
            file_content_area = st.text_area("File Contents:", file_contents, height=500)
            #addDocumentHTML5(file_contents)
        if next_action=='md':
            st.markdown(file_contents)
            #addDocumentHTML5(file_contents)
        if next_action=='search':
            file_content_area = st.text_area("File Contents:", file_contents, height=500)
            st.write('Reasoning with your inputs...')

            # new - llama
            response = StreamLLMChatResponse(file_contents)
            filename = generate_filename(user_prompt, ".md")
            create_file(filename, file_contents, response, should_save)
            
            #addDocumentHTML5(file_contents)
            addDocumentHTML5(response)
            
            # old - gpt
            #response = chat_with_model(user_prompt, file_contents, model_choice)
            #filename = generate_filename(file_contents, choice)
            #create_file(filename, user_prompt, response, should_save)
            
            st.experimental_rerun()

    # Feedback
    # Step: Give User a Way to Upvote or Downvote
    feedback = st.radio("Step 8: Give your feedback", ("👍 Upvote", "👎 Downvote"))
    if feedback == "👍 Upvote":
        st.write("You upvoted 👍. Thank you for your feedback!")
    else:
        st.write("You downvoted 👎. Thank you for your feedback!")
        
    load_dotenv()
    st.write(css, unsafe_allow_html=True)
    st.header("Chat with documents :books:")
    user_question = st.text_input("Ask a question about your documents:")
    if user_question:
        process_user_input(user_question)
    with st.sidebar:
        st.subheader("Your documents")
        docs = st.file_uploader("import documents", accept_multiple_files=True)
        with st.spinner("Processing"):
            raw = pdf2txt(docs)
            if len(raw) > 0:
                length = str(len(raw))
                text_chunks = txt2chunks(raw)
                vectorstore = vector_store(text_chunks)
                st.session_state.conversation = get_chain(vectorstore)
                st.markdown('# AI Search Index of Length:' + length + ' Created.')  # add timing
                filename = generate_filename(raw, 'txt')
                create_file(filename, raw, '', should_save)

# 18. Run AI Pipeline
if __name__ == "__main__":
    whisper_main()
    main()