app.py

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 difflib
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;
}
.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%;
    white-space: pre-wrap;
    width: 500px;
    max-width: 500px;
    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);
}
/* New styles for diff */
.deleted {
    background-color: #ffcccb;
    text-decoration: line-through;
}
.inserted {
    background-color: #90EE90;
}
"""

#Curtesy of claude
def generate_html_diff(old_text, new_text):
    d = difflib.Differ()
    diff = list(d.compare(old_text.split(), new_text.split()))
    
    html_diff = []
    for word in diff:
        if word.startswith('  '):
            html_diff.append(word[2:])
        elif word.startswith('- '):
            html_diff.append(f'<span style="background-color: #ffcccb; text-decoration: line-through;">{word[2:]}</span>')
        elif word.startswith('+ '):
            html_diff.append(f'<span style="background-color: #90EE90;">{word[2:]}</span>')
    
    return ' '.join(html_diff)

# 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 = 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
        
        # Generate HTML diff
        html_diff = generate_html_diff(user_message, generated_text)
        
        generated_text = '<h2 style="text-align:center">Réponse</h3>\n<div class="generation">' + html_diff + "</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()