Update app.py

app.py

@@ -1,108 +1,61 @@
-# app.py
-import os, torch
-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
-from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
-from huggingface_hub import login, snapshot_download
-import gradio as gr
-
 # -------------------------------
-# 1. 模型設定（中文 GPT2 + fallback）
+# 0. 載入向量資料庫
 # -------------------------------
-PRIMARY_MODEL = "uer/gpt2-chinese-cluecorpusmedium"
-FALLBACK_MODEL = "uer/gpt2-chinese-cluecorpussmall"
-
-HF_TOKEN = os.environ.get("HUGGINGFACEHUB_API_TOKEN")
-if HF_TOKEN:
-    login(token=HF_TOKEN)
-    print("✅ 已使用 HUGGINGFACEHUB_API_TOKEN 登入 Hugging Face")
+EMBEDDINGS_MODEL_NAME = "sentence-transformers/all-MiniLM-L6-v2"
+embeddings_model = HuggingFaceEmbeddings(model_name=EMBEDDINGS_MODEL_NAME)
 
-def try_download_model(repo_id):
-    local_dir = f"./models/{repo_id.split('/')[-1]}"
-    if not os.path.exists(local_dir):
-        print(f"⬇️ 嘗試下載模型 {repo_id} ...")
-        try:
-            snapshot_download(repo_id=repo_id, token=HF_TOKEN, local_dir=local_dir)
-        except Exception as e:
-            print(f"⚠️ 模型 {repo_id} 無法下載: {e}")
-            return None
-    return local_dir
-
-# 嘗試下載 Primary，失敗就換 Small
-LOCAL_MODEL_DIR = try_download_model(PRIMARY_MODEL)
-if LOCAL_MODEL_DIR is None:
-    print("⚠️ 切換到 fallback 模型：小型 GPT2-Chinese")
-    LOCAL_MODEL_DIR = try_download_model(FALLBACK_MODEL)
-    MODEL_NAME = FALLBACK_MODEL
+DB_PATH = "./faiss_db"
+if os.path.exists(DB_PATH):
+    print("載入現有向量資料庫...")
+    db = FAISS.load_local(DB_PATH, embeddings_model, allow_dangerous_deserialization=True)
 else:
-    MODEL_NAME = PRIMARY_MODEL
-
-print(f"👉 最終使用模型：{MODEL_NAME}")
-
-# -------------------------------
-# 2. pipeline 載入
-# -------------------------------
-tokenizer = AutoTokenizer.from_pretrained(LOCAL_MODEL_DIR)
-model = AutoModelForCausalLM.from_pretrained(LOCAL_MODEL_DIR)
-
-# 修正 pad_token 缺失問題
-if tokenizer.pad_token is None:
-    tokenizer.pad_token = tokenizer.eos_token
+    raise ValueError("❌ 沒找到 faiss_db，請先建立向量資料庫")
 
-generator = pipeline(
-    "text-generation",
-    model=model,
-    tokenizer=tokenizer,
-    device=-1  # CPU
-)
-
-def call_local_inference(prompt, max_new_tokens=256):
-    try:
-        # 強制補充中文提示
-        if "中文" not in prompt:
-            prompt += "\n（請用中文回答）"
-
-        outputs = generator(
-            prompt,
-            max_new_tokens=max_new_tokens,
-            do_sample=True,
-            temperature=0.7,
-            pad_token_id=tokenizer.pad_token_id
-        )
-        return outputs[0]["generated_text"]
-    except Exception as e:
-        return f"（生成失敗：{e}）"
+retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": 5})
 
 # -------------------------------
-# 3. 文章生成
+# 文章生成（RAG + 檢索片段 + 進度提示 + 即時寫入DOCX）
 # -------------------------------
 def generate_article_progress(query, segments=5):
     docx_file = "/tmp/generated_article.docx"
     doc = DocxDocument()
     doc.add_heading(query, level=1)
+    doc.save(docx_file)  # 先建立空的 docx，避免後面保存出錯
 
     all_text = []
-    base_prompt = f"請依據下列主題生成一篇中文文章，主題：{query}\n每段約150-200字。\n"
+
+    # 🔍 使用 RAG 從 FAISS 檢索相關文獻
+    retrieved_docs = retriever.get_relevant_documents(query)
+    context_texts = [d.page_content for d in retrieved_docs]
+    context = "\n".join([f"{i+1}. {txt}" for i, txt in enumerate(context_texts[:3])])
 
     for i in range(segments):
-        prompt = base_prompt + f"第{i+1}段："
+        progress_text = f"⏳ 正在生成第 {i+1}/{segments} 段..."
+        prompt = (
+            f"以下是佛教經論的相關段落：\n{context}\n\n"
+            f"請依據上面內容，寫一段約150-200字的中文文章，"
+            f"主題：{query}。\n第{i+1}段："
+        )
         paragraph = call_local_inference(prompt)
         all_text.append(paragraph)
+
+        # ✅ 每段生成後立即寫入 DOCX
+        doc = DocxDocument(docx_file)   # 重新打開現有的檔案
         doc.add_paragraph(paragraph)
-        yield "\n\n".join(all_text), None, f"本次使用模型：{MODEL_NAME}"
+        doc.save(docx_file)
+
+        yield "\n\n".join(all_text), None, f"本次使用模型：{MODEL_NAME}", context, progress_text
+
+    final_progress = f"✅ 已完成全部 {segments} 段生成！"
+    yield "\n\n".join(all_text), docx_file, f"本次使用模型：{MODEL_NAME}", context, final_progress
 
-    doc.save(docx_file)
-    yield "\n\n".join(all_text), docx_file, f"本次使用模型：{MODEL_NAME}"
 
 # -------------------------------
-# 4. Gradio 介面
+# Gradio 介面
 # -------------------------------
 with gr.Blocks() as demo:
     gr.Markdown("# 📺 電視弘法視頻生成文章RAG系統")
-    gr.Markdown("固定使用 GPT2-Chinese（medium 下載失敗會自動 fallback 到 small）。")
+    gr.Markdown("使用 GPT2-Chinese + FAISS RAG，生成文章。")
 
     query_input = gr.Textbox(lines=2, placeholder="請輸入文章主題", label="文章主題")
     segments_input = gr.Slider(minimum=1, maximum=10, step=1, value=5, label="段落數")