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app.py

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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
+from docx import Document as DocxDocument
+import gradio as gr
+from langchain_community.vectorstores import FAISS
+from langchain_community.llms import HuggingFaceHub
+
+# -------------------------------
+# 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 Hub）
+# -------------------------------
+llm = HuggingFaceHub(
+    repo_id="google/flan-t5-large",
+    model_kwargs={"temperature": 0.7, "max_new_tokens": 512},
+    huggingfacehub_api_token=HF_TOKEN
+)
+
+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)
+
+    # 取得 API 剩餘次數
+    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 Hub LLM + FAISS RAG，生成文章並提示 API 剩餘額度。"
+)
+
+if __name__ == "__main__":
+    iface.launch()
+

requirements.txt

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+langchain>=0.2.0
+langchain-community>=0.2.0
+langchain-huggingface>=0.0.5
+transformers
+sentence-transformers
+faiss-cpu
+gradio
+python-docx
+tqdm
+huggingface_hub
+requests
+