Update services.py

services.py

@@ -10,35 +10,37 @@ load_dotenv()
 
 class GeminiService:
     def __init__(self):
-        # 從環境變數讀取 Key，兼容本地 .env 與 Hugging Face Secrets
         api_key = os.getenv("GEMINI_API_KEY")
         if not api_key:
-            # 為了避免佈署時報錯，這裡僅印出警告，讓 UI 層處理
             print("警告：找不到 GEMINI_API_KEY")
         
         self.client = genai.Client(api_key=api_key) if api_key else None
+        # 建議使用最新模型以獲得最佳分析能力
         self.model_id = os.getenv("GEMINI_MODEL_ID", "gemini-2.0-flash")
 
     def _check_client(self):
         if not self.client:
-            raise ValueError("API Key 未設定，請檢查 .env 或 Hugging Face Secrets")
+            raise ValueError("API Key 未設定")
 
-    def search_professors(self, query: str, exclude_names: List[str] = []) -> List[Dict]:
+    def search_companies(self, query: str, exclude_names: List[str] = []) -> List[Dict]:
+        """
+        Step 1: 搜尋台灣公司
+        """
         self._check_client()
         exclusion_prompt = ""
         if exclude_names:
             exclusion_prompt = f"IMPORTANT: Do not include: {', '.join(exclude_names)}."
 
-        # Phase 1: Search (Pure Text)
+        # Phase 1: Google Search (廣泛搜尋)
         search_prompt = f"""
-        Using Google Search, find 10 prominent professors in universities across Taiwan who are experts in the field of "{query}".
+        Using Google Search, find 5 to 10 prominent companies in Taiwan related to the query: "{query}".
         
-        CRITICAL:
-        1. FACT CHECK: Verify they are currently faculty.
-        2. RELEVANCE: Their PRIMARY research focus must be "{query}".
+        **CRITICAL INSTRUCTIONS:**
+        1. **TARGET:** Focus on Taiwanese companies (or global companies with a major branch in Taiwan).
+        2. **IDENTIFIERS:** Try to find their distinct "Company Name" (e.g., 台積電 / 台灣積體電路製造股份有限公司).
         {exclusion_prompt}
         
-        List them (Name - University - Department) in Traditional Chinese.
+        List them (Full Name - Industry/Sector) in Traditional Chinese.
         """
 
         search_response = self.client.models.generate_content(
@@ -50,12 +52,12 @@ class GeminiService:
         )
         raw_text = search_response.text
 
-        # Phase 2: Extract JSON
+        # Phase 2: Extract JSON (結構化)
         extract_prompt = f"""
-        From the text below, extract professor names, universities, and departments.
+        From the text below, extract company names and their industry.
         Calculate a Relevance Score (0-100) based on query: "{query}".
         
-        Return ONLY a JSON array: [{{"name": "...", "university": "...", "department": "...", "relevanceScore": 85}}]
+        Return ONLY a JSON array: [{{"name": "...", "industry": "...", "relevanceScore": 85}}]
         
         Text:
         ---
@@ -77,21 +79,45 @@ class GeminiService:
             print(f"JSON Parse Error: {e}")
             return []
 
-    def get_professor_details(self, professor: Dict) -> Dict:
+    def get_company_details(self, company: Dict) -> Dict:
+        """
+        Step 2: 進行商業徵信調查 (Deep Dive)
+        """
         self._check_client()
-        name = professor.get('name')
-        uni = professor.get('university')
-        dept = professor.get('department')
-
-        prompt = f"""
-        Act as an academic consultant. Investigate Professor {name} from {dept} at {uni}.
+        name = company.get('name')
         
-        Find their "Combat Experience" (實戰經驗). Search for:
-        1. **Recent Key Publications (Last 5 Years)**: Find 2-3 top papers. **MUST try to find Citation Counts**.
-        2. **Alumni Directions**: Where do their graduates work? (e.g., TSMC, Google).
-        3. **Industry Collaboration**: Any industry projects?
-
-        Format output in Markdown (Traditional Chinese).
+        prompt = f"""
+        Act as a professional "Business Due Diligence Analyst" (商業徵信分析師). 
+        Conduct a comprehensive investigation on the Taiwanese company: "{name}".
+
+        **Investigation Targets (Must search for these specifically):**
+
+        1.  **Corporate Identity (基本資料)**:
+            - Find the **Tax ID (統一編號)**.
+            - **Registered Capital (資本額)**.
+            - **Representative (代表人)**.
+            - **Establishment Date (設立日期)**.
+            - *Source Hint: Ministry of Economic Affairs (經濟部商業司), Datagovtw.*
+
+        2.  **Scale & Business (規模與業務)**:
+            - **Employee Count**: Estimated number of employees.
+            - **Core Products/Services**: What do they actually sell or do?
+            - *Source Hint: 104 Job Bank, Company Website, LinkedIn.*
+
+        3.  **Market Reputation & Culture (評價與文化 - KEY PART)**:
+            - Search for employee reviews on **PTT (Tech_Job, Soft_Job, Salary)**, **Dcard (Work board)**, **Qollie (求職天眼通)**, or **Google Maps**.
+            - Summarize the **Pros** (e.g., high pay, free snacks) and **Cons** (e.g., toxic management, forced overtime, family business style).
+            - *Tone:* Be objective but highlight recurring complaints.
+
+        4.  **Legal & Risk Assessment (法律與風險 - CRITICAL)**:
+            - Search for keywords: "{name} 判決", "{name} 勞資糾紛", "{name} 違反勞基法", "{name} 詐騙", "{name} 吸金", "{name} 罰款".
+            - List any major lawsuits, fines, or controversies found in news or government records.
+            - If clean, state "No major public legal disputes found."
+
+        **Format Requirements**:
+        - Structure the output as a clean, readable report using Markdown.
+        - Use clear headings.
+        - **Language**: Traditional Chinese (繁體中文).
         """
 
         response = self.client.models.generate_content(
@@ -119,7 +145,7 @@ class GeminiService:
 
     def chat_with_ai(self, history: List[Dict], new_message: str, context: str) -> str:
         self._check_client()
-        system_instruction = f"Source of truth:\n{context}"
+        system_instruction = f"You are a sharp Business Analyst. Answer based on this due diligence report:\n{context}"
         
         chat_history = []
         for h in history: