Update app.py

app.py

@@ -1,87 +1,101 @@
 import gradio as gr
 import os
-import google.generativeai as genai
 import json
 import re
-import random # 匯入隨機數函式庫
+import requests # 使用 requests 函式庫
 
-# --- 步驟一：設定與載入 Gemini 模型 ---
+# --- 步驟一：設定 ---
 GOOGLE_API_KEY = os.getenv('GOOGLE_API_KEY')
-gemini_model = None
-if GOOGLE_API_KEY:
-    try:
-        genai.configure(api_key=GOOGLE_API_KEY)
-        gemini_model = genai.GenerativeModel('gemini-pro')
-        print("INFO: Gemini model loaded successfully.")
-    except Exception as e:
-        print(f"ERROR: Failed to configure Gemini: {e}")
-else:
-    print("WARNING: GOOGLE_API_KEY not found. Service will not function.")
+# 我們將直接在函數中使用 API Key，不再需要載入 Gemini 模型
 
-# --- 步驟二：定義最終的處理函數 ---
-def process_card_with_gemini(text):
+# --- 步驟二：定義最終的處理函數 (使用 requests) ---
+def process_card_with_direct_api(text):
     info = {
       'First Name': '', 'Last Name': '', 'E-mail': '', 'Phone': '', 'Mobile': '',
       'Address': '', 'Organization Name': '', 'Organization Title': '', 'Organization Department': '',
-      'Source Engine': 'Error - Gemini Disabled'
+      'Source Engine': 'Error - API Key Missing'
     }
     
-    if not gemini_model or not text:
-        if not gemini_model: info['First Name'] = 'Gemini Error'
+    if not GOOGLE_API_KEY or not text:
+        if not GOOGLE_API_KEY: info['First Name'] = 'API Key Error'
         return info
 
-    try:
-        # *** 關鍵修正：加入快取破解符 (Cache Buster) ***
-        cache_buster = random.randint(10000, 99999)
-        
-        print(f"INFO: Processing text with Gemini... (buster: {cache_buster})")
-        prompt = f"""
-        Analyze the following business card text and extract the information into a pure JSON object.
-        - Split Chinese names into "First Name" and "Last Name". For "曾祐信", Last Name is "曾", First Name is "祐信".
-        - Combine multi-line addresses into a single "Address" field.
-        - Prioritize mobile numbers (starting with 09) for the "Mobile" field.
-        - Other numbers go into the "Phone" field, including extensions.
-        - If a field is not found, its value should be an empty string "".
-        - CRITICAL: Respond ONLY with the JSON object, without any surrounding text, explanations, or markdown like ```json ... ```.
+    # --- 使用 requests 直接呼叫 Google AI v1 穩定版 API ---
+    api_url = f"https://generativelanguage.googleapis.com/v1/models/gemini-pro:generateContent?key={GOOGLE_API_KEY}"
+    
+    headers = {
+        "Content-Type": "application/json"
+    }
+    
+    prompt = f"""
+    Analyze the following business card text and extract the information into a pure JSON object.
+    - Split Chinese names into "First Name" and "Last Name". For "曾祐信", Last Name is "曾", First Name is "祐信".
+    - Combine multi-line addresses into a single "Address" field.
+    - Prioritize mobile numbers (starting with 09) for the "Mobile" field.
+    - Other numbers go into the "Phone" field, including extensions.
+    - If a field is not found, its value should be an empty string "".
+    - CRITICAL: Respond ONLY with the JSON object, without any surrounding text, explanations, or markdown like ```json ... ```.
 
-        TARGET FIELDS:
-        "First Name", "Last Name", "E-mail", "Phone", "Mobile", "Address", "Organization Name", "Organization Title", "Organization Department"
+    TARGET FIELDS:
+    "First Name", "Last Name", "E-mail", "Phone", "Mobile", "Address", "Organization Name", "Organization Title", "Organization Department"
 
-        BUSINESS CARD TEXT:
-        ---
-        {text}
-        ---
-        (Technical instruction: Ignore this random number, it's for cache busting: {cache_buster})
-        """
-        response = gemini_model.generate_content(prompt)
-        gemini_json_text = response.text.strip().replace("```json", "").replace("```", "").strip()
+    BUSINESS CARD TEXT:
+    ---
+    {text}
+    ---
+    """
+    
+    body = {
+        "contents": [
+            {
+                "parts": [
+                    {
+                        "text": prompt
+                    }
+                ]
+            }
+        ]
+    }
+
+    try:
+        print("INFO: Calling Google AI v1 API directly...")
+        response = requests.post(api_url, headers=headers, json=body, timeout=60)
+        response.raise_for_status() # 如果 API 回傳錯誤 (如 4xx, 5xx), 會在此拋出異常
+
+        response_json = response.json()
+        
+        # 提取 Gemini 回傳的 JSON 文字
+        gemini_output_text = response_json['candidates'][0]['content']['parts'][0]['text']
+        gemini_output_text = gemini_output_text.strip().replace("```json", "").replace("```", "").strip()
         
-        gemini_info = json.loads(gemini_json_text)
+        # 解析 JSON
+        gemini_info = json.loads(gemini_output_text)
         
         for key in info.keys():
             if key not in gemini_info:
                 gemini_info[key] = ''
 
-        gemini_info['Source Engine'] = 'Gemini'
-        print("INFO: Gemini processing successful.")
+        gemini_info['Source Engine'] = 'Gemini (v1 API)'
+        print("INFO: Direct API call successful.")
         return gemini_info
 
     except Exception as e:
-        print(f"ERROR: An error occurred during Gemini processing: {e}")
-        info['Source Engine'] = 'Gemini Error - Fallback'
+        print(f"ERROR: An error occurred during direct API call: {e}")
+        # 如果 API 呼叫失敗，至少回傳 RegEx 抓到的基本聯絡資訊
+        info['Source Engine'] = 'Direct API Error - Fallback'
         email_match = re.search(r'([a-zA-Z0-9._-]+@[a-zA-Z0-9._-]+\.[a-zA-Z]{2,})', text)
         mobile_match = re.search(r'(09\d{2}[- ]?\d{3}[- ]?\d{3})', text)
         if email_match: info['E-mail'] = email_match.group(1).strip()
         if mobile_match: info['Mobile'] = mobile_match.group(1).strip()
         return info
 
-# --- 步驟三：建立並啟動 Gradio 服務 (回歸最簡潔的 Interface 模式) ---
+# --- 步驟三：建立並啟動 Gradio 服務 ---
 interface = gr.Interface(
-    fn=process_card_with_gemini,
+    fn=process_card_with_direct_api,
     inputs=gr.Textbox(lines=15, label="請輸入名片內容"),
     outputs=gr.JSON(label="辨識結果"),
-    title="中文名片智慧辨識 API (Gemini-First, v2)",
-    description="使用 Google Gemini 進行高精度名片辨識。",
+    title="中文名片智慧辨識 API (Direct API v3)",
+    description="直接呼叫 Google v1 API，以獲取最高穩定性。",
     api_name="predict"
 )