Add application file

app.py

@@ -0,0 +1,133 @@
+# -------------------------------
+# 1. 匯入套件
+# -------------------------------
+import os, glob, time
+from langchain.docstore.document import Document
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import FAISS
+from langchain.chat_models import ChatHuggingFaceHub
+from langchain.chains import RetrievalQA
+from docx import Document as DocxDocument
+import gradio as gr
+
+# -------------------------------
+# 2. 設定路徑
+# -------------------------------
+txt_folder = "out_texts"     # 放你的 .txt 檔
+db_path = "faiss_db"
+os.makedirs(db_path, exist_ok=True)
+
+# -------------------------------
+# 3. 建立 embeddings
+# -------------------------------
+embeddings_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+
+# -------------------------------
+# 4. 建立或載入向量資料庫
+# -------------------------------
+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)}))
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
+    split_docs = text_splitter.split_documents(docs)
+    print("產生向量嵌入中...")
+    db = FAISS.from_documents(split_docs, embeddings_model)
+    db.save_local(db_path)
+    print("向量資料庫建立完成。")
+
+# -------------------------------
+# 5. Hugging Face 模型設定
+# -------------------------------
+HUGGINGFACE_API_TOKEN = os.getenv("HF_TOKEN")  # 建議在 Spaces Secrets 設定
+
+MODEL_DICT = {
+    "google/flan-t5-large": 512,
+    "tiiuae/falcon-7b-instruct": 512
+}
+
+MAX_HOURLY_REQUESTS = 50
+request_count = 0
+last_reset_time = time.time()
+
+# -------------------------------
+# 6. RAG 主函式
+# -------------------------------
+def rag_generate_hfapi(query, model_name, segments=5, max_words=1500):
+    global request_count, last_reset_time
+    if time.time() - last_reset_time > 3600:
+        request_count = 0
+        last_reset_time = time.time()
+
+    if request_count >= MAX_HOURLY_REQUESTS:
+        return f"本小時生成次數已達上限 ({MAX_HOURLY_REQUESTS})，請稍後再試。", None
+
+    llm = ChatHuggingFaceHub(
+        repo_id=model_name,
+        model_kwargs={"temperature": 0.7, "max_new_tokens": MODEL_DICT[model_name]},
+        huggingfacehub_api_token=HUGGINGFACE_API_TOKEN
+    )
+
+    qa_chain = RetrievalQA.from_chain_type(
+        llm=llm,
+        retriever=db.as_retriever(search_type="similarity", search_kwargs={"k": 5}),
+        return_source_documents=True
+    )
+
+    prompt = f"""請依據下列主題生成一篇文章：
+主題：{query}
+需求：
+- 總共 {segments} 段
+- 每段約 {max_words // segments} 字
+- 總字數請控制在 {max_words} 字以內
+- 請自動分段輸出
+"""
+
+    try:
+        result = qa_chain({"query": prompt})
+        full_text = result["result"].strip()
+        if not full_text:
+            full_text = "(生成失敗，請改用其他模型或調整段落數)"
+    except Exception as e:
+        return f"(生成失敗：{str(e)})", None
+
+    request_count += 1
+
+    paragraphs = [p.strip() for p in full_text.split("\n") if p.strip()]
+
+    docx_file = "generated_article.docx"
+    doc = DocxDocument()
+    doc.add_heading(query, level=1)
+    for p in paragraphs:
+        doc.add_paragraph(p)
+    doc.save(docx_file)
+
+    return "\n\n".join(paragraphs), docx_file
+
+# -------------------------------
+# 7. Gradio 介面
+# -------------------------------
+iface = gr.Interface(
+    fn=rag_generate_hfapi,
+    inputs=[
+        gr.Textbox(lines=2, placeholder="請輸入文章主題"),
+        gr.Dropdown(list(MODEL_DICT.keys()), value="google/flan-t5-large", label="選擇模型"),
+        gr.Slider(minimum=1, maximum=10, value=5, step=1, label="段落數"),
+        gr.Slider(minimum=500, maximum=3000, value=1500, step=100, label="文章字數上限")
+    ],
+    outputs=[
+        gr.Textbox(label="生成文章"),
+        gr.File(label="下載 DOCX")
+    ],
+    title="佛教經論 RAG 系統 (Hugging Face API)",
+    description="使用 Hugging Face API 生成文章，可選大模型，分段生成並下載 DOCX，每小時生成次數有限制"
+)
+
+iface.launch()