app.py_145_OK

#https://huggingface.co/spaces/MisterAI/GenDoc_05
#app.py_145
#Separation Du Code

import os
import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
import time
from llm.list_llm import TEXT_MODELS, IMAGE_MODELS
from llm.prompt_llm import PREPROMPT
from python_pptx.python_pptx import PresentationGenerator

# Configuration du modèle par défaut
DEFAULT_MODEL = "ibm-granite/granite-3.1-3b-a800m-Instruct"

class ExecutionTimer:
    def __init__(self):
        self.start_time = None
        self.last_duration = None

    def start(self):
        self.start_time = time.time()

    def get_elapsed(self):
        if self.start_time is None:
            return 0
        return time.time() - self.start_time

    def stop(self):
        if self.start_time is not None:
            self.last_duration = self.get_elapsed()
            self.start_time = None
        return self.last_duration

    def get_status(self):
        if self.start_time is not None:
            current = self.get_elapsed()
            last = f" (précédent: {self.last_duration:.2f}s)" if self.last_duration else ""
            return f"En cours... {current:.2f}s{last}"
        elif self.last_duration:
            return f"Terminé en {self.last_duration:.2f}s"
        return "En attente..."

def generate_text(model_path, prompt, temperature=0.7, max_tokens=2048):
    try:
        tokenizer = AutoTokenizer.from_pretrained(model_path)
        model = AutoModelForCausalLM.from_pretrained(
            model_path,
            torch_dtype=torch.float32,
            device_map="auto"
        )
        model.eval()

        chat = [{"role": "user", "content": prompt}]
        formatted_prompt = tokenizer.apply_chat_template(
            chat,
            tokenize=False,
            add_generation_prompt=True
        )

        inputs = tokenizer(
            formatted_prompt,
            return_tensors="pt",
            truncation=True,
            max_length=4096
        ).to(model.device)

        with torch.no_grad():
            outputs = model.generate(
                **inputs,
                max_new_tokens=max_tokens,
                temperature=temperature,
                do_sample=True,
                pad_token_id=tokenizer.eos_token_id
            )

        return tokenizer.decode(outputs[0], skip_special_tokens=True)

    except Exception as e:
        print(f"Erreur lors de la génération: {str(e)}")
        raise

def generate_skeleton(model_name, text, temperature, max_tokens):
    """Génère le squelette de la présentation"""
    try:
        timer.start()

        model_path = TEXT_MODELS.get(model_name, DEFAULT_MODEL)
        full_prompt = PREPROMPT + "\n\n" + text
        generated_content = generate_text(model_path, full_prompt, temperature, max_tokens)

        status = timer.get_status()
        timer.stop()

        return status, generated_content, gr.update(visible=True)

    except Exception as e:
        timer.stop()
        error_msg = f"Erreur: {str(e)}"
        print(error_msg)
        return error_msg, None, gr.update(visible=False)

def create_presentation_file(generated_content):
    """Crée le fichier PowerPoint à partir du contenu généré"""
    try:
        timer.start()
        generator = PresentationGenerator()

        slides = generator.parse_presentation_content(generated_content)
        prs = generator.create_presentation(slides)

        output_path = os.path.join(os.getcwd(), "presentation.pptx")
        prs.save(output_path)

        timer.stop()
        return output_path

    except Exception as e:
        timer.stop()
        print(f"Erreur lors de la création du fichier: {str(e)}")
        return None

# Timer global pour le suivi du temps
timer = ExecutionTimer()

# Interface Gradio
with gr.Blocks(theme=gr.themes.Glass()) as demo:
    gr.Markdown(
        """
        # Générateur de Présentations PowerPoint IA

        Créez des présentations professionnelles automatiquement avec l'aide de l'IA.
        """
    )

    with gr.Row():
        with gr.Column(scale=1):
            model_selector = gr.Dropdown(
                choices=list(TEXT_MODELS.keys()) if TEXT_MODELS else ["Granite"],
                value="Granite" if not TEXT_MODELS else list(TEXT_MODELS.keys())[0],
                label="Modèle de texte"
            )
            temperature = gr.Slider(
                minimum=0.1,
                maximum=1.0,
                value=0.7,
                step=0.1,
                label="Température"
            )
            max_tokens = gr.Slider(
                minimum=1000,
                maximum=4096,
                value=2048,
                step=256,
                label="Tokens maximum"
            )

    with gr.Row():
        with gr.Column(scale=2):
            input_text = gr.Textbox(
                lines=10,
                label="Votre texte",
                placeholder="Décrivez le contenu que vous souhaitez pour votre présentation..."
            )

    with gr.Row():
        generate_skeleton_btn = gr.Button("Générer le Squelette de la Présentation", variant="primary")

    with gr.Row():
        with gr.Column():
            status_output = gr.Textbox(
                label="Statut",
                lines=2,
                value="En attente..."
            )
            generated_content = gr.Textbox(
                label="Contenu généré",
                lines=10,
                show_copy_button=True
            )
            create_presentation_btn = gr.Button("Créer Présentation", visible=False)
            output_file = gr.File(
                label="Présentation PowerPoint",
                type="filepath"
            )

    generate_skeleton_btn.click(
        fn=generate_skeleton,
        inputs=[
            model_selector,
            input_text,
            temperature,
            max_tokens
        ],
        outputs=[
            status_output,
            generated_content,
            create_presentation_btn
        ]
    )

    create_presentation_btn.click(
        fn=create_presentation_file,
        inputs=[generated_content],
        outputs=[output_file]
    )

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