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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+FY2024-NVIDIA-Corporate-Sustainability-Report.pdf filter=lfs diff=lfs merge=lfs -text

FY2024-NVIDIA-Corporate-Sustainability-Report.pdf

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b7baad57e34cd576400067a90b6c9b6904bb3cc786cce52aea03c25e6333ad1a
+size 6081642

README.md

@@ -1,12 +1,14 @@
 ---
 title: Rag Chatbot
-emoji: ⚡
-colorFrom: gray
-colorTo: indigo
+emoji: 🏢
+colorFrom: red
+colorTo: red
 sdk: gradio
-sdk_version: 5.6.0
+sdk_version: 5.1.0
 app_file: app.py
 pinned: false
+license: unknown
+short_description: RAG Chatbot
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+import os
+from langchain.chains import ConversationalRetrievalChain
+from langchain.text_splitter import CharacterTextSplitter
+from langchain_community.document_loaders import PyPDFLoader, Docx2txtLoader, TextLoader
+from langchain_community.vectorstores import Chroma
+from langchain_openai import ChatOpenAI, OpenAIEmbeddings
+from dotenv import load_dotenv
+
+# 加載環境變量
+load_dotenv()
+os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")
+
+# 驗證 OpenAI API Key
+api_key = os.getenv('OPENAI_API_KEY')
+if not api_key:
+    raise ValueError("請設置 'OPENAI_API_KEY' 環境變數")
+
+# OpenAI API key
+openai_api_key = api_key
+
+# 將聊天歷史轉換為適合 LangChain 的二元組格式
+def transform_history_for_langchain(history):
+    return [(chat[0], chat[1]) for chat in history if chat[0]]  # 使用整數索引來訪問元組中的元素
+
+# 將 Gradio 的歷史紀錄轉換為 OpenAI 格式
+def transform_history_for_openai(history):
+    new_history = []
+    for chat in history:
+        if chat[0]:
+            new_history.append({"role": "user", "content": chat[0]})
+        if chat[1]:
+            new_history.append({"role": "assistant", "content": chat[1]})
+    return new_history
+
+# 載入和處理文件的函數
+def load_and_process_documents(folder_path):
+    documents = []
+    for file in os.listdir(folder_path):
+        file_path = os.path.join(folder_path, file)
+        if file.endswith(".pdf"):
+            loader = PyPDFLoader(file_path)
+            documents.extend(loader.load())
+        elif file.endswith('.docx') or file.endswith('.doc'):
+            loader = Docx2txtLoader(file_path)
+            documents.extend(loader.load())
+        elif file.endswith('.txt'):
+            loader = TextLoader(file_path)
+            documents.extend(loader.load())
+
+    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=10)
+    documents = text_splitter.split_documents(documents)
+
+    vectordb = Chroma.from_documents(
+        documents,
+        embedding=OpenAIEmbeddings(),
+        persist_directory="./tmp"
+    )
+    return vectordb
+
+# 初始化向量數據庫為全局變量
+if 'vectordb' not in globals():
+    vectordb = load_and_process_documents("./")
+
+# 定義查詢處理函數
+def handle_query(user_message, temperature, chat_history):
+    try:
+        if not user_message:
+            return chat_history  # 返回不變的聊天記錄
+
+        # 使用 LangChain 的 ConversationalRetrievalChain 處理查詢
+        preface = """
+指令: 全部以繁體中文呈現，200字以內。
+除了與文件相關內容可回答之外，與文件內容不相關的問題都必須回答：這問題很深奧,需要請示JohnLiao大神...
+"""
+        query = f"{preface} 查詢內容：{user_message}"
+
+        # 提取之前的回答作為上下文，並轉換成 LangChain 支持的格式
+        previous_answers = transform_history_for_langchain(chat_history)
+
+        pdf_qa = ConversationalRetrievalChain.from_llm(
+            ChatOpenAI(temperature=temperature, model_name='gpt-4'),
+            retriever=vectordb.as_retriever(search_kwargs={'k': 6}),
+            return_source_documents=True,
+            verbose=False
+        )
+
+        # 調用模型進行查詢
+        result = pdf_qa.invoke({"question": query, "chat_history": previous_answers})
+
+        # 確保 'answer' 在結果中
+        if "answer" not in result:
+            return chat_history + [("系統", "抱歉，出現了一個錯誤。")]
+
+        # 更新對話歷史中的 AI 回應
+        chat_history[-1] = (user_message, result["answer"])  # 更新最後一個記錄，配對用戶輸入和 AI 回應
+
+        return chat_history
+
+    except Exception as e:
+        return chat_history + [("系統", f"出現錯誤: {str(e)}")]
+
+# 使用 Gradio 的 Blocks API 創建自訂聊天介面
+with gr.Blocks() as demo:
+    gr.Markdown("<h1 style='text-align: center;'>AI 小助教</h1>")
+
+    chatbot = gr.Chatbot()
+    state = gr.State([])
+
+    with gr.Row():
+        with gr.Column(scale=0.85):
+            txt = gr.Textbox(show_label=False, placeholder="請輸入您的問題...")
+        with gr.Column(scale=0.15, min_width=0):
+            submit_btn = gr.Button("提問")
+
+    # 用戶輸入後立即顯示提問文字，不添加回應部分，並清空輸入框
+    def user_input(user_message, history):
+        history.append((user_message, ""))  # 顯示提問文字，回應部分為空字符串
+        return history, "", history  # 返回清空的輸入框以及更新的聊天歷史
+
+    # 處理 AI 回應，更新回應部分
+    def bot_response(history):
+        user_message = history[-1][0]  # 獲取最新的用戶輸入
+        history = handle_query(user_message, 0.7, history)  # 調用處理函數
+        return history, history  # 返回更新後的聊天記錄
+
+    # 先顯示提問文字，然後處理 AI 回應，並清空輸入框
+    submit_btn.click(user_input, [txt, state], [chatbot, txt, state], queue=False).then(
+        bot_response, state, [chatbot, state]
+    )
+
+    # 支援按 "Enter" 提交問題，立即顯示提問文字並清空輸入框
+    txt.submit(user_input, [txt, state], [chatbot, txt, state], queue=False).then(
+        bot_response, state, [chatbot, state]
+    )
+
+# 啟動 Gradio 應用
+demo.launch()

gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
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+*.model filter=lfs diff=lfs merge=lfs -text
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+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
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+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
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+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+FY2024-NVIDIA-Corporate-Sustainability-Report[[:space:]](2).pdf filter=lfs diff=lfs merge=lfs -text
+FY2024-NVIDIA-Corporate-Sustainability-Report.pdf filter=lfs diff=lfs merge=lfs -text

requirements.txt

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+#requirements
+gradio
+langchain
+langchain-community
+langchain-openai
+openai
+chromadb
+PyPDF2
+pypdf
+docx2txt
+python-dotenv
+python-docx