app.py

# 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_community.vectorstores import FAISS
from langchain_huggingface import HuggingFaceEmbeddings
from docx import Document as DocxDocument
import gradio as gr

# -------------------------------
# 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)
os.makedirs(TXT_FOLDER, 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")
    if not txt_files:
        print("注意：TXT 資料夾中沒有任何文字檔，向量資料庫將為空。")
    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. 定義 REST API 呼叫函數
# -------------------------------
HEADERS = {"Authorization": f"Bearer {HF_TOKEN}"}

def call_hf_inference(model_name, prompt, max_new_tokens=512):
    api_url = f"https://api-inference.huggingface.co/models/{model_name}"
    payload = {
        "inputs": prompt,
        "parameters": {"max_new_tokens": max_new_tokens}
    }
    try:
        response = requests.post(api_url, headers=HEADERS, json=payload, timeout=180)  # timeout 拉長
        response.raise_for_status()
        data = response.json()
        if isinstance(data, list) and "generated_text" in data[0]:
            return data[0]["generated_text"]
        elif isinstance(data, dict) and "error" in data:
            return f"（生成失敗：{data['error']}，請嘗試換一個模型）"
        else:
            return str(data)
    except requests.exceptions.ReadTimeout:
        return "（生成失敗：等待超時，請嘗試換小一點的模型或增加 timeout 秒數）"
    except Exception as e:
        return f"（生成失敗：{e}）"

# -------------------------------
# 5. 查詢 API 剩餘額度
# -------------------------------
def get_hf_rate_limit():
    try:
        r = requests.get("https://huggingface.co/api/whoami", headers=HEADERS)
        r.raise_for_status()
        data = r.json()
        remaining = data.get("rate_limit", {}).get("remaining", "未知")
        return f"本小時剩餘 API 次數：約 {remaining}"
    except Exception:
        return "無法取得 API 速率資訊"

# -------------------------------
# 6. 生成文章（即時進度）
# -------------------------------
def generate_article_progress(query, model_name, 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)):
        paragraph = call_hf_inference(model_name, prompt)
        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："

        yield "\n\n".join(all_text), None

    doc.save(docx_file)
    rate_info = get_hf_rate_limit()
    yield f"{rate_info}\n\n" + "\n\n".join(all_text), docx_file

# -------------------------------
# 7. Gradio 介面
# -------------------------------
with gr.Blocks() as demo:
    gr.Markdown("# 佛教經論 RAG 系統 (HF API)")
    gr.Markdown("使用 Hugging Face REST API + FAISS RAG，生成文章並提示 API 剩餘額度。")

    query_input = gr.Textbox(lines=2, placeholder="請輸入文章主題", label="文章主題")
    model_dropdown = gr.Dropdown(
        choices=[
            "gpt2",
            "facebook/bart-large-cnn",
            "bigscience/bloom-560m",
            "bigscience/bloomz-560m"
        ],
        value="bigscience/bloomz-560m",   # 預設比較聽得懂指令
        label="選擇生成模型"
    )
    segments_input = gr.Slider(minimum=1, maximum=10, step=1, value=5, label="段落數")
    output_text = gr.Textbox(label="生成文章 + API 剩餘次數")
    output_file = gr.File(label="下載 DOCX")

    btn = gr.Button("生成文章")
    btn.click(
        generate_article_progress,
        inputs=[query_input, model_dropdown, segments_input],
        outputs=[output_text, output_file]
    )

# -------------------------------
# 8. 啟動 Gradio（Hugging Face Space 適用）
# -------------------------------
if __name__ == "__main__":
    demo.launch()