Delete app.py

app.py

@@ -1,128 +0,0 @@
-# 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()
-