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# app.py
# -------------------------------
# 1. 套件載入
# -------------------------------
import os, glob, requests
from langchain.docstore.document import Document
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chains import RetrievalQA
from langchain_huggingface import HuggingFaceEmbeddings, HuggingFaceEndpoint
from docx import Document as DocxDocument
import gradio as gr
from langchain_community.vectorstores import FAISS
# -------------------------------
# 2. 環境變數與資料路徑
# -------------------------------
TXT_FOLDER = "./out_texts"
DB_PATH = "./faiss_db"
os.makedirs(DB_PATH, exist_ok=True)
HF_TOKEN = os.environ.get("HUGGINGFACEHUB_API_TOKEN")
if not HF_TOKEN:
raise ValueError(
"請在 Hugging Face Space 的 Settings → Repository secrets 設定 HUGGINGFACEHUB_API_TOKEN"
)
# -------------------------------
# 3. 建立或載入向量資料庫
# -------------------------------
EMBEDDINGS_MODEL_NAME = "sentence-transformers/all-MiniLM-L6-v2"
embeddings_model = HuggingFaceEmbeddings(model_name=EMBEDDINGS_MODEL_NAME)
if os.path.exists(os.path.join(DB_PATH, "index.faiss")):
print("載入現有向量資料庫...")
db = FAISS.load_local(DB_PATH, embeddings_model, allow_dangerous_deserialization=True)
else:
print("沒有資料庫，開始建立新向量資料庫...")
txt_files = glob.glob(f"{TXT_FOLDER}/*.txt")
docs = []
for filepath in txt_files:
with open(filepath, "r", encoding="utf-8") as f:
docs.append(
Document(page_content=f.read(), metadata={"source": os.path.basename(filepath)})
)
splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
split_docs = splitter.split_documents(docs)
db = FAISS.from_documents(split_docs, embeddings_model)
db.save_local(DB_PATH)
retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": 5})
# -------------------------------
# 4. LLM 設定（Hugging Face Endpoint）
# -------------------------------
llm = HuggingFaceEndpoint(
repo_id="google/flan-t5-large",
task="text2text-generation", # 明確指定 task
huggingfacehub_api_token=HF_TOKEN,
model_kwargs={"temperature": 0.7, "max_new_tokens": 512},
)
qa_chain = RetrievalQA.from_chain_type(
llm=llm,
retriever=retriever,
return_source_documents=True
)
# -------------------------------
# 5. 查詢 API 剩餘額度
# -------------------------------
def get_hf_rate_limit():
headers = {"Authorization": f"Bearer {HF_TOKEN}"}
try:
r = requests.get("https://huggingface.co/api/whoami", headers=headers)
r.raise_for_status()
data = r.json()
used = data.get("rate_limit", {}).get("used", 0)
remaining = 300 - used if used is not None else "未知"
return f"本小時剩餘 API 次數：約 {remaining}"
except:
return "無法取得 API 速率資訊"
# -------------------------------
# 6. 生成文章
# -------------------------------
def generate_article_with_rate(query, segments=5):
docx_file = "/tmp/generated_article.docx"
doc = DocxDocument()
doc.add_heading(query, level=1)
all_text = []
prompt = f"請依據下列主題生成段落：{query}\n\n每段約150-200字。"
for i in range(int(segments)):
try:
result = qa_chain({"query": prompt})
paragraph = result["result"].strip()
if not paragraph:
paragraph = "（本段生成失敗，請嘗試減少段落或改用較小模型。）"
except Exception as e:
paragraph = f"（本段生成失敗：{e}）"
all_text.append(paragraph)
doc.add_paragraph(paragraph)
prompt = f"請接續上一段生成下一段：\n{paragraph}\n\n下一段："
doc.save(docx_file)
full_text = "\n\n".join(all_text)
rate_info = get_hf_rate_limit()
return f"{rate_info}\n\n{full_text}", docx_file
# -------------------------------
# 7. Gradio 介面
# -------------------------------
iface = gr.Interface(
fn=generate_article_with_rate,
inputs=[
gr.Textbox(lines=2, placeholder="請輸入文章主題", label="文章主題"),
gr.Slider(minimum=1, maximum=10, step=1, value=5, label="段落數")
],
outputs=[
gr.Textbox(label="生成文章 + API 剩餘次數"),
gr.File(label="下載 DOCX")
],
title="佛教經論 RAG 系統 (HF API)",
description="使用 Hugging Face Endpoint LLM + FAISS RAG，生成文章並提示 API 剩餘額度。"
)
if __name__ == "__main__":
iface.launch()
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