import transformers
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
import chromadb
import pandas as pd
from chromadb.config import Settings
from chromadb.utils import embedding_functions

# Define the device
device = "cuda" if torch.cuda.is_available() else "cpu"

model_name = "PleIAs/OCRonos" 

llm = LLM(model_name, max_model_len=8128)

    
#CSS for references formatting
css = """
.generation {
    margin-left:2em;
    margin-right:2em;
    size:1.2em;
}
: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

# 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):
        sampling_params = SamplingParams(temperature=0.9, top_p=0.95, max_tokens=4000, presence_penalty=0, stop=["#END#"])
        detailed_prompt = correction = f"### TEXT ###\n{user_message}\n\n### CORRECTION ###\n"
        print(detailed_prompt)
        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>"
        return generated_text

# Create the Falcon chatbot instance
mistral_bot = MistralChatBot()

# Define the Gradio interface
title = "Correction d'OCR"
description = "Un outil expérimental de correction d'OCR basé sur des modèles de langue"
examples = [
    [
        "Qui peut bénéficier de l'AIP?",  # 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='JohnSmith9982/small_and_pretty', css=css) as demo:
    gr.HTML("""<h1 style="text-align:center">Correction d'OCR</h1>""")
    text_input = gr.Textbox(label="Votre texte.", type="text", lines=1)
    text_button = gr.Button("Corriger l'OCR")
    text_output = gr.HTML(label="Le texte corrigé")
    text_button.click(mistral_bot.predict, inputs=text_input, outputs=[text_output])

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