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# app.py inside your Hugging Face Space
import os
import re
import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
# 1. SeaLLM model focused on SEA languages (includes Lao)
MODEL_NAME = "SeaLLMs/SeaLLMs-v3-1.5B-Chat" # 1.5B chat model:contentReference[oaicite:6]{index=6}
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(
MODEL_NAME,
torch_dtype=torch.float32, # CPU on free tier → float32
)
# 2. Load Laos history knowledge
DATA_PATH = "data/laos_history.txt"
if os.path.exists(DATA_PATH):
with open(DATA_PATH, "r", encoding="utf-8") as f:
RAW_KNOWLEDGE = f.read()
else:
RAW_KNOWLEDGE = "ຍັງບໍ່ມີຂໍ້ມູນປະຫວັດສາດຖືກໂຫຼດ."
# Split into paragraphs for simple retrieval
PARAGRAPHS = [p.strip() for p in RAW_KNOWLEDGE.split("\n\n") if p.strip()]
def retrieve_context(question: str, max_paragraphs: int = 5) -> str:
"""
VERY simple keyword-based retrieval over PARAGRAPHS.
Good enough for first prototype; later you can replace with embeddings.
"""
if not PARAGRAPHS:
return RAW_KNOWLEDGE
# tokenize question into simple lowercased words
terms = [w for w in re.split(r"\s+", question.lower()) if len(w) > 2]
if not terms:
return "\n\n".join(PARAGRAPHS[:max_paragraphs])
scored = []
for p in PARAGRAPHS:
p_lower = p.lower()
score = sum(p_lower.count(t) for t in terms)
if score > 0:
scored.append((score, p))
scored.sort(key=lambda x: x[0], reverse=True)
top = [p for _, p in scored[:max_paragraphs]]
if not top:
top = PARAGRAPHS[:max_paragraphs]
return "\n\n".join(top)
SYSTEM_PROMPT = (
"ທ່ານແມ່ນຜູ້ຊ່ວຍເຫຼືອດ້ານປະຫວັດສາດຂອງປະເທດລາວ. "
"ຕອບແຕ່ພາສາລາວ, ອະທິບາຍໃຫ້ເຂົ້າໃຈງ່າຍ ແລະສັ້ນກະທັດຮັດ. "
"ໃຫ້ອີງຈາກຂໍ້ມູນຂ້າງລຸ່ມນີ້ເທົ່ານັ້ນ. "
"ຖ້າຂໍ້ມູນບໍ່ພຽງພໍ ຫຼືບໍ່ຊັດເຈນ ໃຫ້ບອກວ່າບໍ່ແນ່ໃຈ."
)
def build_prompt(question: str) -> str:
context = retrieve_context(question)
prompt = f"""{SYSTEM_PROMPT}
ຂໍ້ມູນອ້າງອີງ:
{context}
ຄຳຖາມ: {question}
ຄຳຕອບດ້ວຍພາສາລາວ:"""
return prompt
def generate_answer(question: str) -> str:
prompt = build_prompt(question)
inputs = tokenizer(prompt, return_tensors="pt")
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=256,
do_sample=True,
temperature=0.7,
top_p=0.9,
)
# slice off the prompt part
generated_ids = outputs[0][inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(generated_ids, skip_special_tokens=True)
# clean up a bit
return answer.strip()
# 3. Gradio chat function
def laos_history_bot(message: str, history: list):
"""
Gradio ChatInterface expects (message, history) and returns a string.
We ignore history for now (you can later use it in the prompt).
"""
if not message.strip():
return "ກະລຸນາພິມຄຳຖາມກ່ອນ."
try:
answer = generate_answer(message)
except Exception as e:
# simple fallback if something goes wrong
return f"ລະບົບມີບັນຫາ: {e}"
return answer
demo = gr.ChatInterface(
fn=laos_history_bot,
title="Laos History Chatbot (Lao language)",
description="ຖາມຂໍ້ມູນກ່ຽວກັບປະຫວັດສາດຂອງປະເທດລາວ",
examples=[
"ອານາຈັກລ້ານຊ້າງເກີດຂຶ້ນໃນປີໃດ?",
"ເມືອງຫວຽງຈັນເຄີຍເປັນນະຄອນຫຼວງຂອງອານາຈັກໃດ?",
],
)
if __name__ == "__main__":
demo.launch()
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