add GAS

main.py

@@ -17,6 +17,8 @@ from ultralytics import YOLO
 import shutil
 import zipfile
 import uuid  # 匯入 uuid 以生成唯一的執行 ID
+import requests
+import json
 from pathlib import Path # 匯入 Path 以更方便地操作路徑
 import gemini_ai as genai
 import gemini_ai_work as genai_work
@@ -26,6 +28,23 @@ import mongo_lib as mongo
 
 
 
+def send_to_gas(action, run_id, file_name, result_content):
+    """
+    將分析結果傳送到指定的 Google Apps Script (GAS) URL。
+    """
+    gas_url = "https://script.google.com/macros/s/AKfycbyW89mbMozl7KB1RFDLTMmyAbe7Dx87TNE1X5VXRcqidjXQKvYrHgLyOhMRJ2j6ndVLbw/exec"
+    payload = {
+        "action": action,
+        "uuid": f"{{{run_id}}}",
+        "檔案名稱": file_name,
+        "分析結果": result_content
+    }
+    try:
+        response = requests.post(gas_url, json=payload, timeout=10)
+        print(f"GAS 傳送結果 ({action}): {response.status_code}, {response.text}")
+    except Exception as e:
+        print(f"GAS 傳送失敗: {e}")
+
 def create_zip_archive(files, zip_filename):
     """
     將一系列檔案壓縮成一個 zip 檔案。
@@ -212,6 +231,9 @@ def gradio_multi_model_detection(
             output_mllm_txt_path = run_output_dir / f"{image_base_name}_mllm_result.txt"
             output_mllm_txt_path.write_text(mllm_result_content, encoding='utf-8')
             all_result_files.append(str(output_mllm_txt_path))
+            
+            # 傳送 MLLM 結果至 GAS
+            send_to_gas("mllm", run_id, f"{image_base_name}.txt", mllm_result_content)
 
         # --- 3c. 職業預測分析 (如果啟用) ---
         output_career_prediction_txt_path = None
@@ -228,6 +250,9 @@ def gradio_multi_model_detection(
             output_career_prediction_txt_path = run_output_dir / f"{image_base_name}_career_prediction.txt"
             output_career_prediction_txt_path.write_text(career_prediction_result_content, encoding='utf-8')
             all_result_files.append(str(output_career_prediction_txt_path))
+
+            # 傳送職業預測結果至 GAS
+            send_to_gas("career_prediction", run_id, f"{image_base_name}_work.txt", career_prediction_result_content)
         
         #寫明細表log
         # document = {"log_style":"detail",
@@ -283,8 +308,8 @@ def toggle_career_prediction_checkbox(is_enabled):
 
 # --- Gradio Interface ---
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown("# ㊙️智慧影像與職業潛能分析 (YOLO + MLLM)")
-    gr.Markdown("上傳圖片與YOLO模型進行物件偵測，並可選用MLLM進行進階圖像理解。   ver.250901.1")
+    gr.Markdown("# ㊙️智慧影像與職業潛能分析 (YOLO + MLLM + GAS)㊙️")
+    gr.Markdown("上傳圖片與YOLO模型(default yolov8n)進行物件偵測，並可選用MLLM進行進階圖像理解。   ver.260228.1")
     # mongo_uri = os.getenv('mongo_uri')
     # gr.Markdown(mongo_uri)