Create services.py

services.py

@@ -0,0 +1,138 @@
+import os
+import json
+from dotenv import load_dotenv
+from google import genai
+from google.genai import types
+from typing import List, Dict, Any, Optional
+
+# 載入環境變數
+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
+        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")
+
+    def search_professors(self, query: str, exclude_names: List[str] = []) -> List[Dict]:
+        self._check_client()
+        exclusion_prompt = ""
+        if exclude_names:
+            exclusion_prompt = f"IMPORTANT: Do not include: {', '.join(exclude_names)}."
+
+        # Phase 1: Search (Pure Text)
+        search_prompt = f"""
+        Using Google Search, find 10 prominent professors in universities across Taiwan who are experts in the field of "{query}".
+        
+        CRITICAL:
+        1. FACT CHECK: Verify they are currently faculty.
+        2. RELEVANCE: Their PRIMARY research focus must be "{query}".
+        {exclusion_prompt}
+        
+        List them (Name - University - Department) in Traditional Chinese.
+        """
+
+        search_response = self.client.models.generate_content(
+            model=self.model_id,
+            contents=search_prompt,
+            config=types.GenerateContentConfig(
+                tools=[types.Tool(google_search=types.GoogleSearch())]
+            )
+        )
+        raw_text = search_response.text
+
+        # Phase 2: Extract JSON
+        extract_prompt = f"""
+        From the text below, extract professor names, universities, and departments.
+        Calculate a Relevance Score (0-100) based on query: "{query}".
+        
+        Return ONLY a JSON array: [{{"name": "...", "university": "...", "department": "...", "relevanceScore": 85}}]
+        
+        Text:
+        ---
+        {raw_text}
+        ---
+        """
+
+        extract_response = self.client.models.generate_content(
+            model=self.model_id,
+            contents=extract_prompt,
+            config=types.GenerateContentConfig(
+                response_mime_type='application/json'
+            )
+        )
+
+        try:
+            return json.loads(extract_response.text)
+        except Exception as e:
+            print(f"JSON Parse Error: {e}")
+            return []
+
+    def get_professor_details(self, professor: Dict) -> Dict:
+        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}.
+        
+        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).
+        """
+
+        response = self.client.models.generate_content(
+            model=self.model_id,
+            contents=prompt,
+            config=types.GenerateContentConfig(
+                tools=[types.Tool(google_search=types.GoogleSearch())]
+            )
+        )
+        
+        # Extract Sources
+        sources = []
+        if response.candidates[0].grounding_metadata and response.candidates[0].grounding_metadata.grounding_chunks:
+            for chunk in response.candidates[0].grounding_metadata.grounding_chunks:
+                if chunk.web and chunk.web.uri and chunk.web.title:
+                    sources.append({"title": chunk.web.title, "uri": chunk.web.uri})
+        
+        # Deduplicate
+        unique_sources = {v['uri']: v for v in sources}.values()
+
+        return {
+            "text": response.text,
+            "sources": list(unique_sources)
+        }
+
+    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}"
+        
+        chat_history = []
+        for h in history:
+            role = "user" if h["role"] == "user" else "model"
+            chat_history.append(types.Content(role=role, parts=[types.Part(text=h["content"])]))
+
+        chat = self.client.chats.create(
+            model=self.model_id,
+            history=chat_history,
+            config=types.GenerateContentConfig(
+                system_instruction=system_instruction
+            )
+        )
+        
+        response = chat.send_message(new_message)
+        return response.text