File size: 2,185 Bytes
6b0f038 e2ba5b8 6b0f038 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 |
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import gradio as gr
# Setup model dan tokenizer
torch.random.manual_seed(0)
model = AutoModelForCausalLM.from_pretrained(
"microsoft/Phi-3-mini-128k-instruct",
device_map="cpu", # Gunakan 'cpu' jika tidak ada GPU
torch_dtype="auto",
trust_remote_code=True,
attn_implementation="eager" # Menggunakan eager untuk menghindari masalah flash-attention
)
tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-128k-instruct")
# Pipeline untuk text-generation
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
)
# Fungsi untuk menghasilkan respons
def generate_response(input_text):
messages = [
{"role": "system", "content": "You are a helpful AI assistant."},
{"role": "user", "content": input_text}
]
generation_args = {
"max_new_tokens": 500,
"return_full_text": False,
"temperature": 0.7, # Bisa disesuaikan untuk variasi output
"do_sample": True, # Mengaktifkan sampling untuk variasi output
}
output = pipe(messages, **generation_args)
return output[0]['generated_text']
# Membuat antarmuka menggunakan Gradio Blocks
with gr.Blocks() as demo:
gr.Markdown("# AI Chatbot Assistant\nTanya apapun, saya siap membantu!")
# Pesan pemberitahuan untuk penggunaan CPU
gr.Markdown(
"### ⚠ Sorry for the inconvenience. The Space is currently running on the CPU, which might affect performance. We appreciate your understanding."
)
# Tata letak output di atas input
with gr.Row():
output_box = gr.Textbox(
label="AI Response",
placeholder="Respons akan muncul di sini...",
lines=10,
interactive=False # Tidak dapat diisi manual
)
with gr.Row():
input_box = gr.Textbox(label="Ask me anything!", placeholder="Tanyakan sesuatu...")
with gr.Row():
submit_button = gr.Button("Submit")
# Aksi untuk submit
submit_button.click(generate_response, inputs=input_box, outputs=output_box)
# Menjalankan antarmuka
demo.launch() |