| import gradio as gr | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| model_name = "CHARKA/Llama-2-7b-chat-h-maroc_edu" | |
| # Charger le modèle et le tokenizer depuis le Hub | |
| model = AutoModelForCausalLM.from_pretrained(model_name) | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| # Fonction de génération de texte | |
| def generate_text(input_text): | |
| inputs = tokenizer.encode(input_text, return_tensors="pt") | |
| outputs = model.generate(inputs, max_length=100, num_return_sequences=1) | |
| return tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| # Créer l'interface Gradio | |
| interface = gr.Interface( | |
| fn=generate_text, | |
| inputs=gr.Textbox(lines=2, placeholder="أدخل طلبك هنا"), | |
| outputs="text", | |
| title="التفاعل مع النموذج", | |
| description="اجهة تفاعل تستخدم نموذج توليد النصوص" | |
| ) | |
| # Lancer l'interface | |
| interface.launch() | |