Create app.py

app.py

@@ -0,0 +1,215 @@
+#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()
+
+
+
+
+