Upload 2 files

app.py

@@ -14,8 +14,10 @@ from langchain_community.document_loaders import WebBaseLoader, TextLoader
 from langchain.prompts import PromptTemplate
 from langchain.schema import Document
 import gradio as gr
+from openai import OpenAI
 import re
 import time
+import requests
 
 def load_documents(sources):
     documents = []
@@ -34,23 +36,23 @@ def load_documents(sources):
     return documents
 
 sources = [
-    "TaxQADataSet_Slim1.txt",
-    "TaxQADataSet_Slim2.txt",
-    "TaxQADataSet_Slim3.txt",
-    "TaxQADataSet_Slim4.txt",
-    "TaxQADataSet_Slim5.txt",
-    "TaxQADataSet_Slim6.txt",
-    "TaxQADataSet_ntpc1.txt",
-    "TaxQADataSet_ntpc2.txt",
-    "TaxQADataSet_kctax.txt",
-    "TaxQADataSet_chutax.txt",
-    "LandTaxAct1100623.txt",
-    "TheEnforcementRulesoftheLandTaxAct1100923.txt",
-    "HouseTaxAct1130103.txt",
-    "VehicleLicenseTaxAct1101230.txt",
-    "TaxCollectionAct1101217.txt",
-    "AmusementTaxAct960523.txt",
-    "StampTaxAct910515.txt",
+    # "TaxQADataSet_Slim1.txt",
+    # "TaxQADataSet_Slim2.txt",
+    # "TaxQADataSet_Slim3.txt",
+    # "TaxQADataSet_Slim4.txt",
+    # "TaxQADataSet_Slim5.txt",
+    # "TaxQADataSet_Slim6.txt",
+    # "TaxQADataSet_ntpc1.txt",
+    # "TaxQADataSet_ntpc2.txt",
+    # "TaxQADataSet_kctax.txt",
+    # "TaxQADataSet_chutax.txt",
+    # "LandTaxAct1100623.txt",
+    # "TheEnforcementRulesoftheLandTaxAct1100923.txt",
+    # "HouseTaxAct1130103.txt",
+    # "VehicleLicenseTaxAct1101230.txt",
+    # "TaxCollectionAct1101217.txt",
+    # "AmusementTaxAct960523.txt",
+    # "StampTaxAct910515.txt",
     "DeedTaxAct990505.txt"
 ]
 
@@ -69,21 +71,22 @@ split_docs = text_splitter.split_documents(documents)
 print(f"分割後的文件數量：{len(split_docs)}")
 
 embeddings = HuggingFaceEmbeddings(model_name="intfloat/multilingual-e5-large")
-print(f"\n成功初始化 Microsoft 嵌入模型")
+print(f"\n成功初始化 Microsoft 嵌入型")
 
 print(f"\n開始建立向量資料庫")
 vectorstore = Chroma.from_documents(split_docs, embeddings, persist_directory="./Knowledge-base")
-print(f"成功建立 Chroma 向量資料庫")
+print(f"成功建立 Chroma 向量資料庫，共有 {len(split_docs)} 個文檔")
 
 retriever = vectorstore.as_retriever(
     search_type="mmr",
     search_kwargs={
-        "k": 4,
-        "fetch_k": 20,
+        "k": min(4, len(split_docs)),
+        "fetch_k": min(20, len(split_docs)),
         "lambda_mult": 0.8
     }
 )
 print(f"檢索演算法：Maximum Marginal Relevance Retrieval")
+print(f"檢索文檔數量：k={min(4, len(split_docs))}, fetch_k={min(20, len(split_docs))}")
 
 template = """Let's work this out in a step by step way to be sure we have the right answer. Must reply to me in Taiwanese Traditional Chinese.
 在回答之前，請仔細分析檢索到的上下文，確保你的回答準確完整反映了上下文中的訊息，而不是依賴先前的知識，在回應的答案中絕對不要提到是根據上下文回答。
@@ -179,8 +182,93 @@ def answer_multiple_questions(query, api_key):
     selected_insight_questions = all_insight_questions[:3]
     return combined_answer, selected_insight_questions
 
-def convert_punctuation(text):
-    return text.replace('?', '？').replace(',', '，').replace('!', '！').replace(' ', '　')
+def get_tax_law(tax_type):
+    tax_law_dict = {
+        "房屋稅": "房屋稅條例",
+        "地價稅": "土地稅法",
+        "契稅": "契稅條例",
+        "娛樂稅": "娛樂稅法",
+        "印花稅": "印花稅法",
+        "牌照稅": "使用牌照稅法",
+        "稅捐稽徵法": "稅捐稽徵法"
+    }
+    return tax_law_dict.get(tax_type, "無稅法")
+
+def fetch_law_summary(tax_law, keywords):
+    url = "https://ttc.mof.gov.tw/Api/GetData"
+    headers = {
+        "Content-Type": "application/x-www-form-urlencoded; charset=UTF-8",
+        "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/130.0.0.0 Safari/537.36"
+    }
+    gr.Info("檢索法令彙編函釋中......")
+    all_results = []
+    
+    for keyword in keywords:
+        payload = {
+            "FunctionID": "FB10001",
+            "ObjParams[TaxAct]": tax_law,
+            "ObjParams[TaxVer]": "請選擇",
+            "ObjParams[Chapter]": "請選擇",
+            "ObjParams[Article]": "請選擇",
+            "ObjParams[Content]": keyword,
+            "ObjParams[Operator01]": "0",
+            "ObjParams[Content01]": "",
+            "ObjParams[Operator02]": "0",
+            "ObjParams[Content02]": ""
+        }
+        
+        try:
+            response = requests.post(url, data=payload, headers=headers)
+            response.raise_for_status()
+            data = response.json()
+            
+            if data["Code"] == "1" and "Table" in data["Data"]:
+                all_results.extend(data["Data"]["Table"])
+        except Exception as e:
+            print(f"Error fetching law summary for keyword '{keyword}': {str(e)}")
+    
+    if all_results:
+        summary = "<h3>法令彙編函釋檢索結果：</h3>"
+        for index, result in enumerate(all_results[:20]):  # Limit to first 20 results across all keywords
+            summary += f"""
+            <details>
+                <summary style="cursor: pointer; color: #0066cc;">{result['Title']}</summary>
+                <p>{result['Content']}</p>
+            </details>
+            """
+        return summary
+    else:
+        return "<p>未找到相關法令彙編函釋。</p>"
+
+def llm_openai_api(query, answer):
+    client = OpenAI(
+        api_key=os.environ.get("YOUR_API_TOKEN"),
+        base_url="https://api.sambanova.ai/v1",
+    )
+
+    user_prompt = f"""
+    「題目：{query}
+    答案：{answer}」
+    請詳細分析答案內容後，依據與題目相關性最高的稅目名稱及最多3個重點關鍵字回應我，問題與答案中的稅目名稱列入TaxName，關鍵字列入KeyWord，只須根據格式回應，不要寫其他的。
+
+    # 回應字典格式範例：
+    {{"TaxName": "地價稅", "KeyWord": "宿舍用地,醫護人員"}}
+    """
+
+    try:
+        response = client.chat.completions.create(
+            model='Meta-Llama-3.1-405B-Instruct',
+            messages=[
+                {"role": "system", "content": "Must reply to user in Traditional Chinese."},
+                {"role": "user", "content": user_prompt}
+            ],
+            temperature=0.7,
+            top_p=1
+        )
+        return response.choices[0].message.content.strip()
+    except Exception as e:
+        print(f"請輸入正確的 API Key！Error： {str(e)}")
+        return '{"TaxName": "", "KeyWord": ""}'
 
 def handle_interaction(query, api_key, state):
     gr.Info("開始處理問題，請稍待片刻......")
@@ -191,12 +279,32 @@ def handle_interaction(query, api_key, state):
         api_key = os.getenv("YOUR_API_KEY")
     query = convert_punctuation(query)
     answer, insight_questions = answer_multiple_questions(query, api_key)
+    api_response = llm_openai_api(query, answer)
+    tax_name = ""
+    keywords = []
+    print(api_response)
+    try:
+        response_dict = eval(api_response)
+        tax_name = response_dict.get("TaxName", "")
+        keywords = response_dict.get("KeyWord", "").split(",")
+    except:
+        print("Error parsing api_response")
+    
+    tax_law = get_tax_law(tax_name)
+    law_summary_content = fetch_law_summary(tax_law, keywords)
+    
     state["history"].append((query, answer))
     while len(insight_questions) < 3:
         insight_questions.append("提供更多地方稅資訊")
     end_time = time.time()
     gr.Info(f"Model 已答覆，執行時間： {(end_time - start_time):.2f} 秒。")
-    return answer, insight_questions[0], insight_questions[1], insight_questions[2], state, query
+    return answer, insight_questions[0], insight_questions[1], insight_questions[2], state, query, law_summary_content
+
+def convert_punctuation(text):
+    return text.replace('?', '？').replace(',', '，').replace('!', '！').replace(' ', '　')
+
+def clear_outputs():
+    return "", "", gr.update(value="", visible=False)
 
 custom_css = """
 .query-input {
@@ -231,6 +339,7 @@ custom_css = """
 }
 #submit-btn {
     border-radius: 10px !important;
+    border: none !important;
     background-color: #ff4081 !important;
     color: white !important;
     font-weight: bold !important;
@@ -243,6 +352,7 @@ custom_css = """
 }
 .insight-btn {
     border-radius: 10px !important;
+    border: none !important;
     background-color: #00bcd4 !important;
 }
 .insight-btn:hover {
@@ -259,6 +369,13 @@ custom_css = """
     border-radius: 10px !important;
     margin: 0 !important;
 }
+.text-background {
+    font-size: 18px !important;
+    padding: 5px !important;
+    border-radius: 10px !important;
+    border: 2px solid #B7E0FF !important;
+    margin: 0 !important;
+}
 .clear-button {
     color: white !important;
     background-color: #000000 !important;
@@ -272,7 +389,7 @@ custom_css = """
 }
 """
 
-with gr.Blocks(theme=gr.themes.Monochrome(), css=custom_css) as iface:
+with gr.Blocks(theme=gr.themes.Soft(), css=custom_css) as iface:
     gr.Markdown("""
     # 地方稅知識庫系統 - 財政部財政資訊中心
     > ### **※ RAG-based 系統部署：江信宗，LLM：Llama-3.1-70B，以地方稅極少知識資料示範，僅供參考，準確資訊請依據地方稅稽徵機關回覆為準。**
@@ -280,33 +397,40 @@ with gr.Blocks(theme=gr.themes.Monochrome(), css=custom_css) as iface:
     with gr.Row():
         query_input = gr.Textbox(label="輸入您的問題，系統將基於學習到的知識資料提供相關答案。", placeholder="請輸入您的問題（支援同時輸入多個問題，例如：問題1？問題2？）", autofocus=True, scale=3, max_lines=5, elem_classes="query-input")
         api_key_input = gr.Textbox(label="請輸入您的 API Key", type="password", placeholder="API authentication key", scale=1, elem_classes="api-key-input")
-    answer_output = gr.Textbox(label="答案：", interactive=False, max_lines=40, elem_classes="answer-box")
+    answer_output = gr.Textbox(label="知識��答案", interactive=False, max_lines=40, elem_classes="answer-box")
     with gr.Row():
         insight_q1 = gr.Button("洞察問題 1", visible=False, elem_classes=["insight-btn"])
         insight_q2 = gr.Button("洞察問題 2", visible=False, elem_classes=["insight-btn"])
         insight_q3 = gr.Button("洞察問題 3", visible=False, elem_classes=["insight-btn"])
     state = gr.State()
     current_question = gr.Textbox(lines=2, label="當前問題", visible=False)
+    law_summary = gr.HTML(label="法令彙編函釋檢索", elem_classes="text-background", visible=False)  # Set initial visibility to False
     with gr.Row():
         submit_btn = gr.Button("傳送", variant="primary", scale=3, elem_id="submit-btn")
         clear_button = gr.Button("清除", variant="secondary", scale=1, elem_classes="clear-button")
-    def update_ui(answer, q1, q2, q3, state, current_q):
+    gr.HTML(
+        """
+        <span style="font-size: 18px; color: black;">※ 財政部各稅法令函釋檢索系統：</span><a href="https://ttc.mof.gov.tw/" title="財政部各稅法令函釋檢索系統" style="font-size: 18px; color: red;">https://ttc.mof.gov.tw/</a>
+        """
+    )
+    def update_ui(answer, q1, q2, q3, state, current_q, law_summary):
         return [
             answer,
             gr.update(value=q1, visible=bool(q1)),
             gr.update(value=q2, visible=bool(q2)),
             gr.update(value=q3, visible=bool(q3)),
             state,
-            current_q
+            current_q,
+            gr.update(value=law_summary, visible=bool(law_summary.strip()))
         ]
     submit_btn.click(
         fn=handle_interaction,
         inputs=[query_input, api_key_input, state],
-        outputs=[answer_output, insight_q1, insight_q2, insight_q3, state, current_question]
+        outputs=[answer_output, insight_q1, insight_q2, insight_q3, state, current_question, law_summary]
     ).then(
         fn=update_ui,
-        inputs=[answer_output, insight_q1, insight_q2, insight_q3, state, current_question],
-        outputs=[answer_output, insight_q1, insight_q2, insight_q3, state, current_question]
+        inputs=[answer_output, insight_q1, insight_q2, insight_q3, state, current_question, law_summary],
+        outputs=[answer_output, insight_q1, insight_q2, insight_q3, state, current_question, law_summary]
     )
     for btn in [insight_q1, insight_q2, insight_q3]:
         btn.click(
@@ -315,11 +439,11 @@ with gr.Blocks(theme=gr.themes.Monochrome(), css=custom_css) as iface:
             outputs=[query_input]
         )
     def clear_outputs():
-        return "", ""
+        return "", "", gr.update(value="", visible=False)
     clear_button.click(
         fn=clear_outputs,
         inputs=[],
-        outputs=[query_input, answer_output]
+        outputs=[query_input, answer_output, law_summary]
     )
 
 if __name__ == "__main__":

requirements.txt

@@ -1,9 +1,11 @@
+gradio
+openai
 langchain
 transformers
 langchain-groq
 chromadb
 langchain-community
 langchain-huggingface
-gradio
 python-dotenv
-beautifulsoup4
+beautifulsoup4
+requests