| import gradio as gr
|
| from transformers import AutoModelForCausalLM, AutoTokenizer
|
|
|
| model_name = "THUDM/chatglm2-6b"
|
| tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
|
|
|
|
|
| if tokenizer.pad_token is None:
|
| tokenizer.pad_token = tokenizer.eos_token
|
|
|
| model = AutoModelForCausalLM.from_pretrained(
|
| model_name,
|
| device_map="auto",
|
| torch_dtype="float16",
|
| low_cpu_mem_usage=True,
|
| trust_remote_code=True
|
| )
|
|
|
| def analyze_fake_news(text):
|
| inputs = tokenizer(text, return_tensors="pt", padding=False).to(model.device)
|
| outputs = model.generate(**inputs, max_length=512, use_cache=False)
|
| return tokenizer.decode(outputs[0], skip_special_tokens=True)
|
|
|
| iface = gr.Interface(
|
| fn=analyze_fake_news,
|
| inputs="text",
|
| outputs="text",
|
| title="Détection de Fake News",
|
| description="Entrez un texte et l’IA analysera."
|
| )
|
|
|
| iface.launch(server_name="0.0.0.0", server_port=7860) |
