Update app.py
Browse files
app.py
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import gradio as gr
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from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
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from pptx import Presentation
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from pptx.util import Inches, Pt
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n_batch=512,
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verbose=False
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)
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else:
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# Configuration pour les modèles Transformers standards
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self.text_tokenizer = AutoTokenizer.from_pretrained(model_id, token=self.token)
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device_map="auto",
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token=self.token
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)
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def load_image_model(self, model_name):
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"""Charge le modèle de génération d'images"""
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temperature=temperature,
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echo=False
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return response['choices'][0]['text']
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else:
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inputs = self.text_tokenizer.apply_chat_template(
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max_new_tokens=max_tokens,
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temperature=temperature
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)
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def generate_image(self, prompt, negative_prompt="", num_inference_steps=30):
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"""Génère une image pour la diapositive"""
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#139
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import os
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import gradio as gr
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from huggingface_hub import hf_hub_download, login
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from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
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from pptx import Presentation
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from pptx.util import Inches, Pt
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n_batch=512,
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verbose=False
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)
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print(f"Modèle GGUF {model_id} chargé avec succès!")
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else:
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# Configuration pour les modèles Transformers standards
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self.text_tokenizer = AutoTokenizer.from_pretrained(model_id, token=self.token)
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device_map="auto",
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token=self.token
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)
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print(f"Modèle Transformers {model_id} chargé avec succès!")
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def load_image_model(self, model_name):
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"""Charge le modèle de génération d'images"""
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temperature=temperature,
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echo=False
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)
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print("Texte généré par Llama :", response['choices'][0]['text'])
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return response['choices'][0]['text']
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else:
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inputs = self.text_tokenizer.apply_chat_template(
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max_new_tokens=max_tokens,
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temperature=temperature
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)
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generated_text = self.text_tokenizer.decode(outputs[0], skip_special_tokens=True)
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print("Texte généré par Transformers :", generated_text)
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return generated_text
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def generate_image(self, prompt, negative_prompt="", num_inference_steps=30):
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"""Génère une image pour la diapositive"""
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