app.py

import spaces
import os
import gradio as gr
from pdf2image import convert_from_path
from byaldi import RAGMultiModalModel
from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info
import torch
import torchvision
import subprocess

def install_poppler():
    try:
        subprocess.run(["pdfinfo"], check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
    except FileNotFoundError:
        print("Poppler not found. Installing...")
        subprocess.run("apt-get update", shell=True)
        subprocess.run("apt-get install -y poppler-utils", shell=True)

install_poppler()

subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)

RAG = RAGMultiModalModel.from_pretrained("vidore/colpali-v1.2")
model = Qwen2VLForConditionalGeneration.from_pretrained("Qwen/Qwen2-VL-2B-Instruct",
                                                        trust_remote_code=True, torch_dtype=torch.bfloat16).cuda().eval()
processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", trust_remote_code=True)

@spaces.GPU()
def process_pdf_and_query(pdf_file, user_query):
    images = convert_from_path(pdf_file.name)
    num_images = len(images)

    RAG.index(
        input_path=pdf_file.name,
        index_name="image_index",
        store_collection_with_index=False,
        overwrite=True
    )

    results = RAG.search(user_query, k=1)
    if not results:
        return "No results found.", num_images

    image_index = results[0]["page_num"] - 1
    messages = [
        {
            "role": "user",
            "content": [
                {
                    "type": "image",
                    "image": images[image_index],
                },
                {"type": "text", "text": user_query},
            ],
        }
    ]

    text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    image_inputs, video_inputs = process_vision_info(messages)
    inputs = processor(
        text=[text],
        images=image_inputs,
        videos=video_inputs,
        padding=True,
        return_tensors="pt",
    )
    inputs = inputs.to("cuda")
    
    generated_ids = model.generate(**inputs, max_new_tokens=50)
    generated_ids_trimmed = [
        out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
    ]
    output_text = processor.batch_decode(
        generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
    )

    return output_text[0], num_images

css = """
body {
    font-family: Arial, sans-serif;
    background-color: #2b2b2b;
    color: #e0e0e0;
}
.container {
    max-width: 800px;
    margin: 0 auto;
    padding: 20px;
    background-color: #363636;
    border-radius: 10px;
    box-shadow: 0 0 10px rgba(0,0,0,0.3);
}
.title {
    font-size: 24px;
    font-weight: bold;
    text-align: center;
    margin-bottom: 20px;
    color: #50fa7b;
}
.submit-btn {
    background-color: #50fa7b;
    color: #282a36;
    padding: 10px 20px;
    border: none;
    border-radius: 5px;
    cursor: pointer;
    font-size: 16px;
    font-weight: bold;
}
.submit-btn:hover {
    background-color: #45c967;
}
.duplicate-button {
    background-color: #8be9fd;
    color: #282a36;
    padding: 10px 20px;
    border: none;
    border-radius: 5px;
    cursor: pointer;
    font-size: 16px;
    font-weight: bold;
    margin-top: 20px;
}
.duplicate-button:hover {
    background-color: #79c7d8;
}
a {
    color: #8be9fd;
    text-decoration: none;
}
a:hover {
    text-decoration: underline;
}
"""

explanation = """
<div style="background-color: #44475a; padding: 15px; border-radius: 5px; margin-bottom: 20px; color: #f8f8f2;">
    <h3 style="color: #50fa7b;"> MICA </h3>
    <p>
    MICA est une intelligene artificielle dédiée à la comptabilité associative, offrant analyse automatisée, recommandations personnalisées et conformité RGPD. Il simplifie la gestion comptable, optimise les décisions et détecte les anomalies. MICA complète l’expertise humaine pour un gain de temps et de précision, tout en respectant les spécificités du secteur associatif.
    </p>
</div>
"""

footer = """
<div style="text-align: center; margin-top: 20px; color: #f8f8f2;">
    <a href="https://www.inediia.com/" target="_blank">Inediia</a> |
    <br>
</div>
"""

with gr.Blocks(css=css, theme='freddyaboulton/dracula_revamped') as demo:
    gr.HTML('<h1 style="text-align: center; font-size: 32px;"><a href="https://github.com/arad1367" target="_blank" style="text-decoration: none; color: #50fa7b;"> MICA une IA créée par INEDIIA </a></h1>')
    gr.HTML(explanation)
    pdf_input = gr.File(label="Upload PDF")
    query_input = gr.Textbox(label="Poser votre question", placeholder="Poser votre question sur la comptabilité des associations")
    submit_btn = gr.Button("Submit", elem_classes="submit-btn")
    output_text = gr.Textbox(label="Réponse de MICA")
    output_images = gr.Textbox(label="Nombre de pages pdf")
    
    submit_btn.click(process_pdf_and_query, inputs=[pdf_input, query_input], outputs=[output_text, output_images])
    
    gr.HTML(footer)

demo.launch(debug=True)