| import gradio as gr | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| from peft import PeftModel | |
| base_model_id = "HooshvareLab/gpt2-fa" | |
| lora_model_id = "SEDNA-AI/sedna-gpt2-lora" | |
| tokenizer = AutoTokenizer.from_pretrained(base_model_id) | |
| base_model = AutoModelForCausalLM.from_pretrained(base_model_id) | |
| model = PeftModel.from_pretrained(base_model, lora_model_id) | |
| def chat(user_input): | |
| prompt = f"سوال: {user_input}\nپاسخ:" | |
| inputs = tokenizer(prompt, return_tensors="pt") | |
| outputs = model.generate( | |
| **inputs, | |
| max_new_tokens=100, | |
| temperature=0.7, | |
| do_sample=True | |
| ) | |
| response = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| return response.split("پاسخ:")[-1].strip() | |
| iface = gr.Interface(fn=chat, inputs="text", outputs="text", title="SEDNA Chatbot") | |
| iface.launch() | |