import transformers
import numpy as np
import re
from transformers import AutoConfig, AutoTokenizer, AutoModel, AutoModelForCausalLM
from vllm import LLM, SamplingParams
import torch
import gradio as gr
import json
import os
import shutil
import requests
from pprint import pprint
import chromadb
import pandas as pd
from sklearn.metrics.pairwise import cosine_similarity

pd.set_option('display.max_columns', None)

#sampling_params = SamplingParams(temperature=.7, top_p=.95, max_tokens=2000, presence_penalty = 1.5, stop = ["``"])

# Define the device
device = "cuda" if torch.cuda.is_available() else "cpu"
#Define variables 
temperature=0.2
max_new_tokens=1000
top_p=0.92
repetition_penalty=1.7

model_name = "Inagua/code-model"

llm = LLM(model_name, max_model_len=4096)



#CSS for references formatting
css = """
.generation {
    margin-left:2em;
    margin-right:2em;
}

:target {
    background-color: #CCF3DF; /* Change the text color to red */
  }

.source {
    float:left;
    max-width:17%;
    margin-left:2%;
}

.tooltip {
    position: relative;
    cursor: pointer;
    font-variant-position: super;
    color: #97999b;
  }
  
  .tooltip:hover::after {
    content: attr(data-text);
    position: absolute;
    left: 0;
    top: 120%; /* Adjust this value as needed to control the vertical spacing between the text and the tooltip */
    white-space: pre-wrap; /* Allows the text to wrap */
    width: 500px; /* Sets a fixed maximum width for the tooltip */
    max-width: 500px; /* Ensures the tooltip does not exceed the maximum width */
    z-index: 1;
    background-color: #f9f9f9;
    color: #000;
    border: 1px solid #ddd;
    border-radius: 5px;
    padding: 5px;
    display: block;
    box-shadow: 0 4px 8px rgba(0,0,0,0.1); /* Optional: Adds a subtle shadow for better visibility */
  }"""

#Curtesy of chatgpt
def format_references(text):
    # Define start and end markers for the reference
    ref_start_marker = '<ref text="'
    ref_end_marker = '</ref>'
    
    # Initialize an empty list to hold parts of the text
    parts = []
    current_pos = 0
    ref_number = 1
    
    # Loop until no more reference start markers are found
    while True:
        start_pos = text.find(ref_start_marker, current_pos)
        if start_pos == -1:
            # No more references found, add the rest of the text
            parts.append(text[current_pos:])
            break
        
        # Add text up to the start of the reference
        parts.append(text[current_pos:start_pos])
        
        # Find the end of the reference text attribute
        end_pos = text.find('">', start_pos)
        if end_pos == -1:
            # Malformed reference, break to avoid infinite loop
            break
        
        # Extract the reference text
        ref_text = text[start_pos + len(ref_start_marker):end_pos].replace('\n', ' ').strip()
        ref_text_encoded = ref_text.replace("&", "&amp;").replace("<", "&lt;").replace(">", "&gt;")
        
        # Find the end of the reference tag
        ref_end_pos = text.find(ref_end_marker, end_pos)
        if ref_end_pos == -1:
            # Malformed reference, break to avoid infinite loop
            break
        
        # Extract the reference ID
        ref_id = text[end_pos + 2:ref_end_pos].strip()
        
        # Create the HTML for the tooltip
        tooltip_html = f'<span class="tooltip" data-refid="{ref_id}" data-text="{ref_id}: {ref_text_encoded}"><a href="#{ref_id}">[' + str(ref_number) +']</a></span>'
        parts.append(tooltip_html)
        
        # Update current_pos to the end of the current reference
        current_pos = ref_end_pos + len(ref_end_marker)
        ref_number = ref_number + 1
    
    # Join and return the parts
    parts = ''.join(parts)

    return parts

# Class to encapsulate the Falcon chatbot
class MistralChatBot:
    def __init__(self, system_prompt="Le dialogue suivant est une conversation"):
        self.system_prompt = system_prompt

    def predict(self, user_message, context):
        detailed_prompt = """### Question ###\n""" + user_message + "\n\n### Contexte ###\n" + context + "\n\n### Formule ###\n"
        prompts = [detailed_prompt]
        outputs = llm.generate(prompts, sampling_params, use_tqdm = False)
        generated_text = outputs[0].outputs[0].text
        generated_text = '<h2 style="text-align:center">Réponse</h3>\n<div class="generation">' + generated_text + "</div>"
        fiches_html = ""
        return generated_text, fiches_html

# Create the Falcon chatbot instance
mistral_bot = MistralChatBot()

# Define the Gradio interface
title = "Inagua"
description = "An experimental LLM to interact with DAMAaaS documentation"
examples = [
    [
        "How to calculate a linear regression?",  # user_message
        0.7  # temperature
    ]
]

additional_inputs=[
    gr.Slider(
        label="Température",
        value=0.2,  # Default value
        minimum=0.05,
        maximum=1.0,
        step=0.05,
        interactive=True,
        info="Des valeurs plus élevées donne plus de créativité, mais aussi d'étrangeté",
    ),
]


demo = gr.Blocks()

with gr.Blocks(theme='gradio/monochrome', css=css) as demo:
    gr.HTML("""<h1 style="text-align:center">SkikitLLM</h1>""")
    text_input = gr.Textbox(label="Your question", type="text", lines=1)
    context_input = gr.Textbox(label="Your context", type="text", lines=1)
    text_button = gr.Button("Query SkikitLLM")
    text_output = gr.HTML(label="Answer")
    text_button.click(mistral_bot.predict, inputs=[text_input, context_input], outputs=[text_output])

if __name__ == "__main__":
    demo.queue().launch()