app.py

import json
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import gradio as gr
from huggingface_hub import hf_hub_download
from llama_cpp import Llama
import re

# -------------------- Config --------------------
TOP_K        = 5            
FINAL_TOP_N  = 1           
MIN_CONF     = 0.14        
CHUNK_LIMIT  = 300          
MAX_TOKENS   = 256           
TEMP         = 0.2
QUALITY_LOG  = "quality_feedback.jsonl"   

# -------------------- Load Dataset --------------------
DATASET_PATH = "nbb_merged_full.json"
with open(DATASET_PATH, "r", encoding="utf-8") as f:
    RAW_DATA = json.load(f)

def normalize_record(d):
    lo_text = ""
    if "content" in d and isinstance(d["content"], dict):
        lo_text = d["content"].get("lo", "")
    elif "data" in d:
        lo_text = d["data"].get("answer", "")
    return {
        "id": d.get("id", str(hash(json.dumps(d)))),
        "title": d.get("title", ""),
        "section": d.get("section", ""),
        "content": {"lo": lo_text},
    }

DOCS = [normalize_record(x) for x in RAW_DATA if normalize_record(x)["content"]["lo"].strip()]
assert DOCS, "Dataset ບໍ່ມີ content.lo"

CORPUS = [d["content"]["lo"] for d in DOCS]
IDS = [d["id"] for d in DOCS]
ID2DOC = {d["id"]: d for d in DOCS}

vectorizer = TfidfVectorizer(ngram_range=(1,2), min_df=1, max_df=0.95, sublinear_tf=True)
X = vectorizer.fit_transform(CORPUS)

# -------------------- Search --------------------
def search(query, k=TOP_K):
    qv = vectorizer.transform([query])
    sims = cosine_similarity(qv, X)[0]
    idxs = np.argsort(-sims)[:k]
    return [{"id": IDS[i], "score": sims[i]} for i in idxs]

# -------------------- Load LLM --------------------
MODEL_PATH = hf_hub_download(
    repo_id="Qwen/Qwen2.5-1.5B-Instruct-GGUF",  
    filename="qwen2.5-1.5b-instruct-q4_k_m.gguf"
)

LLM = Llama(
    model_path=MODEL_PATH,
    n_ctx=1024,       
    n_threads=4,     
    n_gpu_layers=0,  
    n_batch=128,      
    logits_all=False,
    verbose=False
)

SYSTEM_RULES = """
You are a Lao banking assistant for NAYOBY BANK (NBB).

HARD RULES (do not break):
1) Answer ONLY from the provided Context. Do NOT use outside knowledge or make assumptions.
2) If the answer is not clearly in the Context, reply in Lao: "ຂໍອະໄພ ຂ້ອຍບໍ່ພົບຂໍ້ມູນໃນຖານຄວາມຮູ້."
3) Cite the evidence ids at the end in square brackets (1–3 ids).
4) Default reply in Lao; if the whole user question is Thai/English, reply with that language; keep product terms exactly as in Context.
5) Never invent numbers, dates, fees, branches, or contacts beyond the Context.

STYLE:
- Concise (≤ 100 Lao words). Direct answer first, bullets if needed.
- Keep terminology exactly as in Context.

FORMAT:
- End the last line with citations like: [id_a, id_b]
"""

def truncate(text, limit=CHUNK_LIMIT):
    return text if len(text) <= limit else text[:limit] + "..."

def build_prompt(question, hits):
    ctx = "\n\n".join([
        truncate(ID2DOC[h['id']]['content']['lo'])
        for h in hits[:FINAL_TOP_N]
    ])
    return f"{SYSTEM_RULES}\n\nContext:\n{ctx}\n\nQuestion:\n{question}\n\nAnswer:"

# -------------------- Helper functions --------------------
def limit_words(text, max_words=100):
    words = text.split()
    return " ".join(words[:max_words])

def clean_citations(text):
    # เอา citation ซ้ำ ๆ ออก และจำกัดไม่เกิน 3 id
    match = re.findall(r"\[(.*?)\]", text)
    if not match:
        return text
    ids = match[-1].split(",")  # ใช้ citation ชุดสุดท้าย
    ids = [x.strip() for x in ids if x.strip()]
    ids = list(dict.fromkeys(ids))[:3]  # ลบซ้ำ + จำกัด 3
    text = re.sub(r"\[.*?\]$", "", text).strip()
    return f"{text} [{', '.join(ids)}]"

# -------------------- Answer --------------------
def smart_answer(message):
    hits = search(message, k=TOP_K)
    if not hits or hits[0]["score"] < MIN_CONF:
        return "ຂໍອະໄພ ບໍ່ພົບຂໍ້ມູນໃນຖານຄວາມຮູ້."
    
    prompt = build_prompt(message, hits)
    out = LLM(
        prompt,
        max_tokens=MAX_TOKENS,
        temperature=TEMP,
        stop=["\n\nQuestion:", "Context:", "Answer:", "</s>"]
    )
    answer = out["choices"][0]["text"].strip()
    answer = limit_words(answer, 100)
    answer = clean_citations(answer)
    return answer

# -------------------- Gradio Chatbot --------------------
def respond(message, history):
    answer = smart_answer(message)
    history = history + [(message, answer)]
    return history

with gr.Blocks() as demo:
    gr.Markdown("## ທົດລອງ RDB Chatbot")
    chatbot_ui = gr.Chatbot()
    msg = gr.Textbox(placeholder="ພິມຄຳຖາມບ່ອນນີ້...")
    msg.submit(respond, [msg, chatbot_ui], chatbot_ui)

if __name__ == "__main__":
    demo.launch()