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31793e1 ec32b31 28d09c0 9de2232 28d09c0 31793e1 28d09c0 31793e1 f8cab49 31793e1 9de2232 31793e1 408f80c 28d09c0 31793e1 ad0cc09 31793e1 408f80c 7d9941e f8cab49 31793e1 408f80c 31793e1 | 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 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 | from unsloth import FastLanguageModel
import torch
import gradio as gr
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.
alpaca_prompt = """Berikut adalah instruksi yang deskripsikan tugas dan sepasang input dan konteksnya. Tulis response sesuai dengan permintaan.
### Instruction:
{}
### Input:
{}
### Response:
{}"""
if True:
from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained(
model_name = "abdfajar707/llama3_8B_lora_model_rkp_pn2025_v3", # YOUR MODEL YOU USED FOR TRAINING
max_seq_length = max_seq_length,
dtype = dtype,
load_in_4bit = load_in_4bit,
)
FastLanguageModel.for_inference(model) # Enable native 2x faster inference
# Fungsi untuk menghasilkan respons
def generate_response(prompt, max_length=1000):
inputs = tokenizer(
[
alpaca_prompt.format(
prompt, # instruction
"", # input
"", # output - leave this blank for generation!
)
], return_tensors = "pt").to("cuda")
outputs = model.generate(**inputs, max_length=max_length, pad_token_id=tokenizer.eos_token_id)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
return response
history = []
def wrapper_chat_history(chat_history, history):
chat_history = history[1:]
return chat_history
def converse(message, chat_history):
response = generate_response(message)
print(response)
user_msg = {"role": "user", "content": message}
history.append(user_msg)
ai_msg = {"role": "assistant", "content": response}
history.append(ai_msg)
return history[-1]["content"]
DESCRIPTION = '''
<div style="padding: 5px; text-align: left; display: flex; flex-direction: column; align-items: left;">
<img src="https://sdgs.bappenas.go.id/repository/assets/bappenas_logo_square.png" style="width: 40%; max-width: 200px; height: auto; opacity: 0.55; ">
<h2 style="font-size: 28px; margin-bottom: 2px; opacity: 0.55;">AI-Interlinked System/Bappenas GPT</h2>
</div>
'''
LICENSE = """
<p/>
---
Dibangun dari Meta Llama 3
"""
PLACEHOLDER = """
<div style="padding: 100px; text-align: center; display: flex; flex-direction: column; align-items: center;">
<img src="https://cdn3.iconfinder.com/data/icons/human-resources-flat-3/48/150-4096.png" style="width: 1000; max-width: 200px; height: auto; opacity: 0.55; ">
<h2 style="font-size: 20px; margin-bottom: 2px; opacity: 0.55;">Asisten Virtual Perencana</h2>
<p style="font-size: 18px; margin-bottom: 2px; opacity: 0.65;">Silakan mulai tanya...</p>
</div>
"""
css = """
h1 {
text-align: center;
display: block;
}
#duplicate-button {
margin: auto;
color: white;
background: #1565c0;
border-radius: 100vh;
}
"""
chatbot=gr.Chatbot(height=600, placeholder=PLACEHOLDER, label='Interlinked Sytem ChatInterface')
with gr.Blocks(css=css) as interface:
chatbot=chatbot,
with gr.Row():
with gr.Column(scale=1):
gr.HTML('<img src="https://datahub.data.go.id/data/static/Kementerian%20PPN%20Bappenas%20Tanpa%20Teks.png" width="100px" alt="Image" style="max-width: 100%;">')
with gr.Row():
with gr.Column(scale=1, elem_id='col'):
gr.ChatInterface(fn=converse, title=("""
<center>
<h1>KemenPPN/Bappenas</h1>
<b>AI-Interlinked System/Bappenas GPT<b>
</center>
"""
))
interface.launch() |