import gradio as gr
import ssl
from openai import OpenAI
import time
import os
import shutil
from datetime import datetime
import Arcana
from nylon import *
import pandas as pd
import json

# SSL configuration to avoid verification issues
try:
    _create_unverified_https_context = ssl._create_unverified_context
except AttributeError:
    pass
else:
    ssl._create_default_https_context = _create_unverified_https_context

def query_database2(query):
    db = ChatDatabase('memory.txt')

    sender = 'Arcana'
    N = 10
    cache = {}
    query_tag = None

    relevant_messages = db.get_relevant_messages(sender, query, N, cache, query_tag)

    print("Relevant messages:")
    for message in relevant_messages:
        print(f"Sender: {message[0]}, Time: {message[1]}, Tag: {message[3]}")
        print(f"Message: {message[2][:100]}...")
        print()

    df_data = [str(message) for message in relevant_messages]
    return ';'.join(df_data)

# OpenAI client setup
client = OpenAI(
    base_url='https://api.openai-proxy.org/v1',
    api_key='sk-Nxf8HmLpfIMhCd83n3TOr00TR57uBZ0jMbAgGCOzppXvlsx1',
)

# Function list for OpenAI API
function_list = [
    {
        "name": "query_database",
        "description": "Query the database and return a list of results as strings",
        "parameters": {
            "type": "object",
            "properties": {
                "query": {
                    "type": "string",
                    "description": "The query to execute against the database"
                },
            },
            "required": ["query"]
        }
    }
]

# Mapping of function names to actual function objects
function_map = {
    "query_database": query_database2
}

def execute_function(function_name, function_args):
    if function_name in function_map:
        return function_map[function_name](**function_args)
    else:
        return f"Error: Function {function_name} not found"

# Retry logic for OpenAI API call
def openai_api_call(messages, retries=3, delay=5):
    for attempt in range(retries):
        try:
            completion = client.chat.completions.create(
                model="gpt-3.5-turbo",  # Changed from "gpt-4o" to "gpt-4"
                messages=messages,
                functions=function_list,
                function_call='auto',
                timeout=10
            )
            response_message = completion.choices[0].message

            # Check if the model wants to call a function
            if response_message.function_call:
                function_name = response_message.function_call.name
                function_args = json.loads(response_message.function_call.arguments)
                function_response = execute_function(function_name, function_args)
                # Add the function response to the conversation
                messages.append(response_message.model_dump())  # The model's request to call the function
                messages.append({
                    "role": "function",
                    "name": function_name,
                    "content": json.dumps(function_response)
                })
                # Make a follow-up call to the model with the function response
                return openai_api_call(messages)
            else:
                return response_message.content

        except Exception as e:
            print(f"Attempt {attempt + 1} failed: {e}")
            if attempt < retries - 1:
                time.sleep(delay)
            else:
                return "Sorry, I am having trouble connecting to the server. Please try again later."

# Chatbot response function
def chatbot_response(message, history):
    messages = [{"role": "system", "content": '''You are Arcana, a dynamic study resource database designed to help students excel in their exams. Your responses should be accurate, informative, and evidence-based whenever possible. Follow these guidelines:
Your primary goal is to provide students with the most helpful and accurate study information, utilizing both your internal knowledge and the PDF resources at your disposal.'''}]
    
    for human, assistant in history:
        messages.append({"role": "user", "content": human})
        messages.append({"role": "assistant", "content": assistant})
    messages.append({"role": "user", "content": message})

    response = openai_api_call(messages)

    return response

selected = None

def upload_file(file):
    foldername = 'cache'
    if not os.path.exists(foldername):
        os.mkdir(foldername)
    file_path = os.path.join(foldername, os.path.basename(file.name))
    shutil.copy(file.name, file_path)
    return list_uploaded_files()

def list_uploaded_files():
    foldername = 'cache'
    if not os.path.exists(foldername):
        return []
    files = os.listdir(foldername)
    return [[file] for file in files]

def on_select(evt: gr.SelectData):
    global selected
    selected_value = evt.value
    selected_index = evt.index
    selected = selected_value
    print(f"Selected value: {selected_value} at index: {selected_index}")
    
    file_path = os.path.join("cache", selected_value) if selected_value else None
    status_message = f"Selected: {selected_value}" if selected_value else "No file selected"
    
    file_size = get_file_size(file_path) if file_path else ""
    file_creation_time = get_file_creation_time(file_path) if file_path else ""
    
    return file_path, status_message, file_size, file_creation_time

def get_file_size(file_path):
    if file_path and os.path.exists(file_path):
        size_bytes = os.path.getsize(file_path)
        if size_bytes < 1024:
            return f"{size_bytes} bytes"
        elif size_bytes < 1024 * 1024:
            return f"{size_bytes / 1024:.2f} KB"
        else:
            return f"{size_bytes / (1024 * 1024):.2f} MB"
    return ""

def get_file_creation_time(file_path):
    if file_path and os.path.exists(file_path):
        creation_time = os.path.getctime(file_path)
        return datetime.fromtimestamp(creation_time).strftime("%Y-%m-%d %H:%M:%S")
    return ""

def delete_file():
    global selected
    if selected:
        foldername = 'cache'
        file_path = os.path.join(foldername, selected)
        if os.path.exists(file_path):
            os.remove(file_path)
            return list_uploaded_files(), None, f"File {selected} deleted successfully", "", ""
        else:
            return list_uploaded_files(), None, f"File {selected} not found", "", ""
    else:
        return list_uploaded_files(), None, "No file selected for deletion", "", ""

def refresh_files():
    return list_uploaded_files()

def display_file(evt: gr.SelectData, df):
    file_path = os.path.join("cache", evt.value)
    return file_path, file_path if file_path.lower().endswith(('.png', '.jpg', '.jpeg', '.gif')) else None, f"Displaying: {evt.value}"

def render_to_database():
    # This function is undefined as per your request
    Arcana.main()

def change_theme(theme):
    gr.Interface.theme = theme

def rename_file(new_name):
    global selected
    if selected and new_name:
        old_path = os.path.join('cache', selected)
        new_path = os.path.join('cache', new_name+'.'+selected.split('.')[-1])
        if os.path.exists(old_path):
            os.rename(old_path, new_path)
            selected = new_name
            return list_uploaded_files(), f"File renamed to {new_name}", new_path, get_file_size(new_path), get_file_creation_time(new_path)
        else:
            return list_uploaded_files(), f"File {selected} not found", None, "", ""
    return list_uploaded_files(), "No file selected or new name not provided", None, "", ""

def query_database(query):
    # Usage example
    db = ChatDatabase('memory.txt')

    # Example 1: Get relevant messages
    sender = 'Arcana'
    N = 10
    cache = {}
    query_tag = None

    relevant_messages = db.get_relevant_messages(sender, query, N, cache, query_tag)

    print("Relevant messages:")
    for message in relevant_messages:
        print(f"Sender: {message[0]}, Time: {message[1]}, Tag: {message[3]}")
        print(f"Message: {message[2][:100]}...")
        print()

    df_data = [{"Nylon Returned Query": str(message)} for message in relevant_messages]
    
    # Create a pandas DataFrame
    df = pd.DataFrame(df_data)
    
    return df

    
example_database = [
    "What is Hydrogen Bonding?",
    "Tell me the difference between impulse and force.",
    "Tell me a joke that Calculus students will understand.",
    "How should I review for the AP Biology Exam?",
    "What kind of resources are available in PA and Indexademics?",
    "What is the StandardCAS™ group?",
    "Explain the concept of quantum entanglement.",
    "What are the main differences between mitosis and meiosis?",
    "How does the Doppler effect work?",
    "Explain the process of photosynthesis.",
    "What is the significance of the Pythagorean theorem?",
    "How does natural selection contribute to evolution?",
    "What is the most important chapter in AP Statistics?",
    "How should I prepare on the IB Chinese Exam?"
]

import random

def get_random_examples(num_examples=5):
    return random.sample(example_database, min(num_examples, len(example_database)))

# Create the Gradio interface for the chatbot
chatbot_interface = gr.ChatInterface(
    chatbot_response,
    chatbot=gr.Chatbot(height=400),
    textbox=gr.Textbox(placeholder="Type your message here...", container=True, scale=10),
    title="Review With Arcana",
    description="ArcanaUI v0.8 - Chatbot",
    theme="soft",
    examples=get_random_examples(),
    cache_examples=False,
    retry_btn=None,
    undo_btn="Delete Previous",
    clear_btn="Clear"
)


def relaunch():
    global demo
    demo.close()
    demo.launch(share=True)
    
# Combine the interfaces using Tabs
with gr.Blocks() as demo:
    gr.Markdown("# ArcanaUI v0.8")
    with gr.Tabs():
        with gr.TabItem("Welcome Page"):
            with open('introduction.txt',mode='r') as file:
                intro_content = file.read()
            gr.Markdown(intro_content)

        with gr.TabItem("Chatbot"):
            chatbot_interface.render()

        # File uploading interface
        with gr.TabItem('Upload'):
            gr.Markdown('# Upload and View Files')
            
            with gr.Row():
                # Left column: File list and buttons
                with gr.Column(scale=1):
                    uploaded_files_list = gr.DataFrame(headers=["Uploaded Files"], datatype="str", interactive=False)
                    
                    with gr.Row():
                        upload_button = gr.UploadButton('Upload File')
                        refresh_button = gr.Button('Refresh')
                        delete_button = gr.Button('Delete Selected File')

                # Right column: File viewer and Image viewer
                with gr.Column(scale=1):
                    with gr.Tab("File  Viewer"):
                        file_viewer = gr.File(label="File Restore")
                        file_status = gr.Textbox(label="File Status", interactive=False)
                        file_size = gr.Textbox(label="File Size", interactive=False)
                        file_creation_time = gr.Textbox(label="File Creation Time", interactive=False)
                        
                        with gr.Row():
                            new_file_name = gr.Textbox(label="New File Name", placeholder="Enter new file name")
                            rename_button = gr.Button("Rename File")
                        
                    
                    with gr.Tab("Image Viewer"):
                        image_viewer = gr.Image(label="Image Viewer", type="filepath")
                        
            # Event handlers
            refresh_button.click(fn=refresh_files, outputs=uploaded_files_list)
            upload_button.upload(upload_file, inputs=upload_button, outputs=uploaded_files_list)
            delete_button.click(fn=delete_file, outputs=[uploaded_files_list, file_viewer, file_status, file_size, file_creation_time])
            uploaded_files_list.select(fn=display_file, inputs=uploaded_files_list, outputs=[file_viewer, image_viewer, file_status])
            uploaded_files_list.select(fn=on_select, outputs=[file_viewer, file_status, file_size, file_creation_time])
            rename_button.click(fn=rename_file, 
                                inputs=new_file_name, 
                                outputs=[uploaded_files_list, file_status, file_viewer, file_size, file_creation_time])

            render_button = gr.Button("Render all PDFs to Database")
            render_button.click(fn=render_to_database)

        with gr.TabItem('Settings'):
            with gr.TabItem('Database'):
                gr.Markdown('Settings')

                test_nylon = gr.Textbox(label='Test Nylon', placeholder='Query')
                uploaded_files_list2 = gr.DataFrame(headers=["Nylon Returned Query"], datatype="str", interactive=False)
                
                query_button = gr.Button('Query')
                
                query_button.click(fn=query_database, inputs=test_nylon, outputs=uploaded_files_list2)
            with gr.TabItem('Theme'):
                gr.Markdown('Change Theme')

                theme_dropdown = gr.Dropdown(choices=['default', 'compact', 'huggingface', 'soft', 'dark'], label='Choose Theme')
                theme_button = gr.Button('Apply Theme')

                theme_button.click(fn=change_theme, inputs=theme_dropdown)
                relaunch_button = gr.Button('Relaunch')
                relaunch_button.click(fn=relaunch)


# Launch the interface
demo.launch(share=True)