Update app.py

app.py

@@ -4,30 +4,16 @@
 
 import os
 import subprocess
-
-# 強制設定 Playwright 瀏覽器存放於 user 目錄
-os.environ["PLAYWRIGHT_BROWSERS_PATH"] = "/home/user/.cache/ms-playwright"
-
-try:
-    if not os.path.exists(os.environ["PLAYWRIGHT_BROWSERS_PATH"]):
-        subprocess.run(["python3", "-m", "playwright", "install", "chromium"], check=True)
-except Exception as e:
-    print(f"Playwright 初始化提示: {e}")
-
-# --- 3. 匯入必要的模組 ---
+import time 
+import urllib.parse
+import pandas as pd
 import gradio as gr
 from playwright.sync_api import sync_playwright
-from dotenv import load_dotenv
-import pandas as pd
-import urllib.parse # 用於處理網址編碼
-import time  # ✅ 確保這行存在
 from langchain_huggingface import HuggingFaceEmbeddings
 from langchain_community.vectorstores import FAISS
 from PIL import Image
-from openai import OpenAI
-
-# ✅ 這裡就是漏掉的地方，必須加入這行：
 from dotenv import load_dotenv
+from openai import OpenAI 
 
 # --- 匯入組員的模組 ---
 import emotion 
@@ -35,17 +21,17 @@ import emotion
 
 load_dotenv()
 
-# 自動安裝 Playwright 瀏覽器 (HF 環境必要步驟)
-try:
-    import playwright
-except ImportError:
-    subprocess.run(["pip", "install", "playwright"])
-    subprocess.run(["playwright", "install", "chromium"])
+# 強制設定 Playwright 瀏覽器存放於 user 目錄
+os.environ["PLAYWRIGHT_BROWSERS_PATH"] = "/home/user/.cache/ms-playwright"
 
-from playwright.sync_api import sync_playwright
+try:
+    if not os.path.exists(os.environ["PLAYWRIGHT_BROWSERS_PATH"]):
+        subprocess.run(["python3", "-m", "playwright", "install", "chromium"], check=True)
+except Exception as e:
+    print(f"Playwright 初始化提示: {e}")
 
 # ==========================================
-# 0. 環境變數 & 系統初始化
+# 0. 全域變數初始化
 # ==========================================
 GROQ_API_KEY = os.getenv("GROQ_API_KEY")
 HF_TOKEN = os.getenv("HF_TOKEN")
@@ -57,6 +43,7 @@ rag_initialized = False
 
 def init_static_data():
     global global_mood_df
+    if global_mood_df is not None: return
     try:
         global_mood_df = pd.read_csv('mood_food_guide.csv')
     except:
@@ -67,7 +54,7 @@ def init_static_data():
         })
 
 # ==========================================
-# 1. 優化後的動態同步功能
+# 1. 【核心修正】動態同步功能：強化圖片抓取穩定性
 # ==========================================
 def sync_google_maps(url):
     global global_df, global_retriever, rag_initialized
@@ -77,50 +64,60 @@ def sync_google_maps(url):
         return
 
     try:
-        yield "🚀 [1/4] 啟動瀏覽器並準備抓取實景圖..."
+        yield "🚀 [1/4] 啟動瀏覽器中..."
         with sync_playwright() as p:
             browser = p.chromium.launch(headless=True)
             context = browser.new_context(user_agent="Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36")
             page = context.new_page()
-            yield "🌐 [2/4] 連線中..."
+            
+            yield "🌐 [2/4] 正在開啟網頁..."
             page.goto(clean_url, wait_until="domcontentloaded", timeout=60000)
-            yield "⏳ [3/4] 等待網頁內容渲染 (10秒)..."
-            time.sleep(10) 
             
-            yield "📄 [4/4] 正在解析餐廳資訊與圖片網址..."
-            # 【關鍵修改】改用名稱作為基準，再去找對應的圖片，避免因為 role="article" 抓不到
-            name_elements = page.locator('div.fontHeadlineSmall').all()
+            # 【新增】自動捲動機制：Google Maps 需要捲動才能載入隱藏的圖片
+            yield "⏳ [3/4] 正在觸發圖片載入 (自動捲動與等待)..."
+            for _ in range(3):
+                page.mouse.wheel(0, 1000) # 向下捲動
+                time.sleep(2)
+            
+            yield "📄 [4/4] 正在提取餐廳資料與真實照片..."
+            
+            # 【優化選擇器】改用更穩定的區塊定位
+            # Google Maps 清單中的每一項通常都在 role="article" 的 div 內
+            items = page.locator('div[role="article"]').all()
             
             restaurant_list = []
-            for el in name_elements:
+            for item in items:
                 try:
-                    name = el.inner_text().strip()
-                    if not name: continue
+                    # 1. 抓取店名 (通常在 fontHeadlineSmall 中)
+                    name_el = item.locator('div.fontHeadlineSmall').first
+                    if name_el.count() == 0: continue
+                    name = name_el.inner_text().strip()
                     
-                    # 尋找同一層級或父層級中的圖片與地址
-                    # Google Maps 的結構通常是名稱在一個 div，圖片在旁邊的 div
-                    container = page.locator(f"//div[contains(., '{name}')]/ancestor::div[@role='article']").first
+                    # 2. 抓取地址/細節
+                    addr_el = item.locator('div.fontBodyMedium').first
+                    addr = addr_el.inner_text().strip() if addr_el.count() > 0 else "位置見地圖"
                     
-                    # 抓取地址
-                    addr = "位置詳見地圖"
-                    addr_el = container.locator('div.fontBodyMedium').first
-                    if addr_el.count() > 0:
-                        addr = addr_el.inner_text().strip()
+                    # 3. 【關鍵修正】多重路徑抓取圖片 URL
+                    # 先找直接的 img 標籤，如果沒有，找背景圖片
+                    img_url = "https://images.unsplash.com/photo-1555396273-367ea4eb4db5?q=80&w=400" # 預設改為更中性的餐廳圖
                     
-                    # 【新增】抓取實景圖 URL
-                    img_url = "https://images.unsplash.com/photo-1517248135467-4c7edcad34c4?q=80&w=400" # 預設圖
-                    img_el = container.locator('img').first
+                    # 嘗試抓取 src
+                    img_el = item.locator('img').first
                     if img_el.count() > 0:
                         src = img_el.get_attribute('src')
+                        # 過濾掉 base64 透明佔位圖 (通常以 data: 開頭)
                         if src and not src.startswith('data:'):
-                            # 將縮圖網址改為較大尺寸
-                            img_url = src.replace('=w80-h80', '=w400-h400').split('=')[0] + '=w400-h400'
+                            # 【新增】將縮圖 URL 轉為大圖模式 (Google 的 URL 規則)
+                            if "=w" in src:
+                                img_url = src.split('=w')[0] + "=w400-h400-p-k-no"
+                            else:
+                                img_url = src
 
                     restaurant_list.append({
                         "Name": name,
                         "Address": addr,
-                        "Image_URL": img_url, # 【新增】存入圖片網址
-                        "RAG_Content": f"餐廳名稱：{name}，地址：{addr}",
+                        "Image_URL": img_url,
+                        "RAG_Content": f"餐廳：{name}，細節：{addr}",
                         "URL": f"https://www.google.com/search?q={urllib.parse.quote(name)}"
                     })
                 except: continue
@@ -128,110 +125,92 @@ def sync_google_maps(url):
             browser.close()
 
             if not restaurant_list:
-                yield "⚠️ 找不到餐廳資訊。請確認：1.連結是『分享清單』產生的 2.清單已設為公開。"
+                yield "⚠️ 抓取失敗：找不到地點資訊。請確認分享連結是否正確。"
                 return
 
-            # 更新向量資料庫
+            # 更新 RAG 系統
             global_df = pd.DataFrame(restaurant_list)
             embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
-            texts = global_df['RAG_Content'].tolist()
-            metadatas = global_df.to_dict('records')
-            
-            vectorstore = FAISS.from_texts(texts, embeddings, metadatas=metadatas)
+            vectorstore = FAISS.from_texts(global_df['RAG_Content'].tolist(), embeddings, metadatas=global_df.to_dict('records'))
             global_retriever = vectorstore.as_retriever(search_kwargs={"k": 3})
             rag_initialized = True
-            yield f"✅ 同步成功！已載入 {len(restaurant_list)} 間包含實景圖的餐廳。"
+            yield f"✅ 同步成功！已載入 {len(restaurant_list)} 間餐廳實景照片。"
+            
     except Exception as e:
         yield f"❌ 系統錯誤：{str(e)}"
 
 # ==========================================
-# 2. 核心推薦邏輯 (確保 fallback 機制)
+# 2. 【修改】推薦邏輯：支援未匹配告知
 # ==========================================
 def get_restaurant_data(mood_score_str, food_choice):
     init_static_data()
     if global_df is None or global_df.empty: 
-        return None, True, "資料庫未載入", "尚未同步地圖資料", ""
-
-    # 【新增】列出目前資料庫中所有餐廳名稱
-    all_names = "、".join(global_df['Name'].tolist())
-    
-    # 取得目前資料庫中所有餐廳的名稱清單，方便回報給使用者
-    all_restaurant_names = "、".join(global_df['Name'].tolist())
+        return None, False, "", "", ""
     
+    all_names = "、".join(global_df['Name'].tolist())
     try: score = int(str(mood_score_str).split(' ')[0])
     except: score = 3
     
-    mood_info = global_mood_df[global_mood_df['分數'] == score]
-    mood_reason = mood_info.iloc[0]['原因'] if not mood_info.empty else "隨意探索"
-    
+    mood_reason = global_mood_df[global_mood_df['分數'] == score].iloc[0]['原因']
     candidates = global_df.copy()
     food_keyword = "飯" if food_choice == "吃飯" else "麵" if food_choice == "吃麵" else ""
     
     is_match = True
     if food_keyword:
-        # 過濾包含關鍵字的餐廳
         filtered = candidates[candidates['Name'].str.contains(food_keyword) | candidates['RAG_Content'].str.contains(food_keyword)]
         if not filtered.empty:
             candidates = filtered
         else:
-            # ❗ 關鍵修改：如果找不到符合的，標記為「未命中」並使用全部資料隨機抽樣
-            is_match = False
+            is_match = False 
     
-    # 從候選（或全部）中隨機挑選一間
     result = candidates.sample(1).iloc[0]
-    return result, is_match, "個人清單", mood_reason, all_restaurant_names
-
-# (其餘 generate_content_with_groq, mood_agent_logic 與橋接邏輯保持不變...)
-def generate_content_with_groq(restaurant_name, restaurant_detail, user_diary, mood_score, mood_guide_reason, debug_mode=False):
-    if not GROQ_API_KEY: return "⚠️ 請設定 GROQ_API_KEY"
-    client = OpenAI(api_key=GROQ_API_KEY, base_url="https://api.groq.com/openai/v1")
-    system_prompt = "你是一個幽默、懂吃且善解人意的 AI 朋友..."
-    user_msg = f"【狀態】心情分數：{mood_score}\n【日記】{user_diary}\n【建議原因】{mood_guide_reason}\n【餐廳】{restaurant_name}\n資料：{restaurant_detail}"
-    try:
-        response = client.chat.completions.create(model="llama-3.3-70b-versatile", messages=[{"role": "system", "content": system_prompt}, {"role": "user", "content": user_msg}])
-        return response.choices[0].message.content
-    except Exception as e: return f"Groq Error: {str(e)}"
-
-def generate_image_huggingface(prompt):
-    if not HF_TOKEN: return None
-    try:
-        hf_client = InferenceClient(token=HF_TOKEN)
-        return hf_client.text_to_image(prompt=prompt, model="stabilityai/stable-diffusion-xl-base-1.0")
-    except: return None
+    return result, is_match, "口袋名單", mood_reason, all_names
 
+# ==========================================
+# 3. 【修改】Agent 邏輯：整合實景照片輸出
+# ==========================================
 def mood_agent_logic(score_input, food_input, diary_input, debug_mode):
     if not rag_initialized:
-        yield "⚠️ 請先同步地圖清單！", None, ""
+        yield "⚠️ 請先點擊同步按鈕！", None, ""
         return
 
-    # 取得推薦資料
     restaurant, is_match, rec_categories, mood_reason, all_names = get_restaurant_data(score_input, food_input)
     
-    # 【新增】處理「找不到符合要求」時的告知訊息
+    # 組合回報訊息
     prefix = ""
     status_hint = ""
     if not is_match and food_input != "隨便":
-        prefix = f"> 💡 **小提醒**：在您的清單中沒找到『{food_input}』相關的店，目前名單有：**{all_names}**。所以我先推薦這間給您：\n\n"
-        status_hint = f"\n(注意：使用者的清單中無 '{food_input}'，請幽默解釋為何這間也值得一試。)"
+        prefix = f"> 💡 **提醒**：名單中沒找到『{food_input}』，目前有：**{all_names}**。推薦這間試試：\n\n"
+        status_hint = f"\n(注意：清單中無 '{food_input}'，請解釋推薦原因。)"
 
     name = restaurant['Name']
     url = restaurant.get('URL', '#')
-    # 【修改】使用抓取到的真實圖片 URL
+    # 【修正】直接使用抓取到的 Image_URL
     real_img_url = restaurant.get('Image_URL') 
     rag_info = str(restaurant.get('RAG_Content', '')) + status_hint
     
-    # 產生 AI 回應
     ai_text = generate_content_with_groq(name, rag_info, diary_input, score_input, mood_reason)
     
     final_response = f"{prefix}### 🍽️ 推薦：{name}\n\n{ai_text}"
-    map_html = f'<div style="text-align:center"><a href="{url}" target="_blank" style="background:#4CAF50;color:white;padding:8px 16px;border-radius:20px;text-decoration:none">🗺️ 開啟 Google Map 導航</a></div>'
+    map_html = f'<div style="text-align:center"><a href="{url}" target="_blank" style="background:#4CAF50;color:white;padding:8px 16px;border-radius:20px;text-decoration:none">🗺️ 開啟 Google Map</a></div>'
     
-    # 【修改】直接回傳實景圖網址給 Gradio
+    # 回傳圖片
     yield final_response, real_img_url, map_html
 
 # ==========================================
-# 3. 橋接邏輯 (保留原本情緒辨識部分)
+# 4. 輔助函數與 Gradio (保持穩定版本)
 # ==========================================
+def generate_content_with_groq(restaurant_name, restaurant_detail, user_diary, mood_score, mood_guide_reason):
+    if not GROQ_API_KEY: return "⚠️ 請設定 GROQ_API_KEY"
+    client = OpenAI(api_key=GROQ_API_KEY, base_url="https://api.groq.com/openai/v1")
+    system_prompt = "你是一個幽默、懂吃且善解人意的 AI 朋友..."
+    user_msg = f"【餐廳】{restaurant_name}\n【資料】{restaurant_detail}\n【心情分數】{mood_score}\n【日記】{user_diary}"
+    try:
+        response = client.chat.completions.create(model="llama-3.3-70b-versatile", messages=[{"role": "system", "content": system_prompt}, {"role": "user", "content": user_msg}])
+        return response.choices[0].message.content
+    except Exception as e: return f"Groq Error: {str(e)}"
+
+# --- 橋接邏輯 ---
 def _score_to_radio_value(score):
     mapping = {1: "1 (心情差)", 2: "2 (不太好)", 3: "3 (普通)", 4: "4 (不錯)", 5: "5 (超棒)"}
     try: score = int(score)
@@ -267,16 +246,11 @@ def bridge_predict_upload(img, st):
         tabs_update = gr.Tabs(selected=1)
     return out_result, score_update, tabs_update, out_st
 
-# ==========================================
-# 4. Gradio 介面建構
-# ==========================================
+# --- Gradio 介面 ---
 combined_css = emotion.css 
-
 with gr.Blocks(title="AI 心情食堂", css=combined_css) as demo:
     st_state = gr.State(emotion.AppState())
-
     with gr.Tabs() as tabs:
-        # Tab 1: 情緒辨識
         with gr.TabItem("😊 情緒辨識 (Step 1)", id=0):
             with gr.Column(elem_id="app_container"):
                 gr.Markdown("### 第一步：測測你的心情能量")
@@ -284,36 +258,27 @@ with gr.Blocks(title="AI 心情食堂", css=combined_css) as demo:
                     btn_start = gr.Button("📸 開啟攝影機辨識", variant="primary")
                     btn_stop  = gr.Button("⏹️ 停止", variant="secondary", visible=False)
                 cam = gr.Image(sources=["webcam"], streaming=True, type="numpy", label="攝影機畫面", visible=False)
-                result_markdown = gr.Markdown(emotion._hint_html("請按「開啟攝影機辨識」或下方上傳照片。"))
+                result_markdown = gr.Markdown(emotion._hint_html("請按「開啟攝影機辨識」。"))
                 gr.Markdown("---")
                 upload_img = gr.Image(sources=["upload"], type="numpy", label="上傳照片")
 
-        # Tab 2: 主功能區
         with gr.TabItem("🍽️ AI 心情食堂 (Step 2)", id=1):
             with gr.Column():
-                gr.Markdown("## 🍱 同步口袋名單\n請貼上你的 Google Maps「已儲存地點」分享連結（需設為共用）。")
+                gr.Markdown("## 🍱 同步口袋名單\n請貼上您的 Google Maps 分享連結。")
                 with gr.Row():
-                    map_url = gr.Textbox(label="Google Maps 分享連結", placeholder="https://goo.gl/maps/...")
+                    map_url = gr.Textbox(label="分享連結", placeholder="https://goo.gl/maps/...")
                     sync_btn = gr.Button("🔄 同步我的清單", variant="secondary")
                 sync_msg = gr.Markdown("ℹ️ 尚未同步資料庫")
-                
                 gr.Markdown("---")
-                
                 with gr.Row():
                     with gr.Column(scale=1):
-                        score_input = gr.Radio(
-                            ["1 (心情差)", "2 (不太好)", "3 (普通)", "4 (不錯)", "5 (超棒)"], 
-                            label="1. 心情分數", 
-                            value="3 (普通)"
-                        )
+                        score_input = gr.Radio(["1 (心情差)", "2 (不太好)", "3 (普通)", "4 (不錯)", "5 (超棒)"], label="1. 心情分數", value="3 (普通)")
                         food_input = gr.Radio(["吃飯", "吃麵", "隨便"], label="2. 想吃什麼", value="隨便")
-                        diary_input = gr.Textbox(lines=4, label="3. 心情日記", placeholder="寫下今天的心情...")
-                        debug_mode_btn = gr.Checkbox(label="🔧 開啟除錯模式", value=False)
+                        diary_input = gr.Textbox(lines=4, label="3. 心情日記", placeholder="寫下心情...")
                         submit_btn = gr.Button("🍱 推薦今天的驚喜", variant="primary")
-                    
                     with gr.Column(scale=1):
                         agent_output = gr.Markdown(label="AI 回應")
-                        image_output = gr.Image(label="AI 推薦美食圖", type="pil", width=400)
+                        image_output = gr.Image(label="餐廳照片", type="filepath", width=400)
                         map_output = gr.HTML(label="地圖導航")
 
     # 事件綁定
@@ -322,7 +287,7 @@ with gr.Blocks(title="AI 心情食堂", css=combined_css) as demo:
     btn_stop.click(fn=bridge_stop_click, inputs=[st_state], outputs=[result_markdown, cam, btn_stop, btn_start, st_state])
     cam.stream(fn=bridge_predict_frame, inputs=[cam, st_state], outputs=[cam, result_markdown, st_state, btn_stop, btn_start, score_input, tabs])
     upload_img.change(fn=bridge_predict_upload, inputs=[upload_img, st_state], outputs=[result_markdown, score_input, tabs, st_state])
-    submit_btn.click(fn=mood_agent_logic, inputs=[score_input, food_input, diary_input, debug_mode_btn], outputs=[agent_output, image_output, map_output])
+    submit_btn.click(fn=mood_agent_logic, inputs=[score_input, food_input, diary_input], outputs=[agent_output, image_output, map_output])
 
 if __name__ == "__main__":
     demo.launch(ssr_mode=False)