main.py

# -*- coding: utf-8 -*-
"""
系統需求:
- gradio: 用於建立 Web UI
- opencv-python: 用於圖片處理
- ultralytics: YOLOv8 官方函式庫
- Pillow: 圖片處理基礎庫
- transformers: (可選，若YOLO模型需要)
huggingface上面是用main.py當主畫面

"""

import gradio as gr
import os
import cv2
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
from datetime import datetime
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。

    Args:
        files (list): 要壓縮的檔案路徑列表。
        zip_filename (str): 產生的 zip 檔案路徑。

    Returns:
        str: 產生的 zip 檔案路徑。
    """
    with zipfile.ZipFile(zip_filename, 'w', zipfile.ZIP_DEFLATED) as zipf:
        for file in files:
            if os.path.exists(file):
                # 使用 os.path.basename 確保只寫入檔案名稱，而非完整路徑
                zipf.write(file, os.path.basename(file))
            else:
                print(f"警告: 檔案 '{file}' 不存在，無法加入壓縮檔。")
    return zip_filename

def gradio_multi_model_detection(
    image_files,
    model_files,
    conf_threshold,
    enable_mllm,
    mllm_prompt,
    enable_career_prediction, # 新增職業預測參數
    progress=gr.Progress(track_tqdm=True)
):
    """
    Gradio 的主要處理函式，使用生成器 (yield) 實現流式輸出。

    Args:
        image_files (list): Gradio File 元件回傳的圖片檔案列表。
        model_files (list): Gradio File 元件回傳的模型檔案列表。
        conf_threshold (float): 置信度閾值。
        enable_mllm (bool): 是否啟用 MLLM 分析。
        mllm_prompt (str): 使用者自訂的 MLLM prompt。
        enable_career_prediction (bool): 是否啟用職業預測分析。
        progress (gr.Progress): Gradio 的進度條元件。

    Yields:
        dict: 用於更新 Gradio 介面元件的字典。
    """
    global_datetime = datetime.now()
    
    #寫主表log
    # document = {"log_style":"master",
    #             "create_datetime": str(global_datetime), 
    #             "image_files": image_files, 
    #             "model_files": model_files,
    #             "conf_threshold":conf_threshold,
    #             "enable_mllm":enable_mllm,
    #             "mllm_prompt":mllm_prompt,
    #             "enable_career_prediction":enable_career_prediction # 新增職業預測狀態到 log
    #             }
        
    # mongo.insert_mongodb_log("multi_model_detection",document)  #寫入log方便日後查驗            
    
    if not image_files:
        yield {
            output_status: gr.update(value="錯誤：請至少上傳一張圖片。"),
            output_gallery: None,
            output_text: None,
            download_button: None
        }
        return

    # --- 1. 初始化設定 ---
    # 為本次執行創建一個唯一的子目錄
    run_id = str(uuid.uuid4())
    base_output_dir = Path('gradio_detection_results')
    run_output_dir = base_output_dir / f"run_{run_id[:8]}"
    run_output_dir.mkdir(parents=True, exist_ok=True)

    image_paths = [file.name for file in image_files]
    model_paths = [file.name for file in model_files] if model_files else []

    # --- 2. 載入模型 ---
    yield {output_status: gr.update(value="正在載入模型...")}
    loaded_models = []
    if not model_paths:
        # 如果沒有上傳模型，使用預設模型
        default_model_path = 'yolov8n.pt'
        try:
            model = YOLO(default_model_path)
            loaded_models.append((default_model_path, model))
        except Exception as e:
            yield {output_status: gr.update(value=f"錯誤: 無法載入預設模型 '{default_model_path}' - {e}")}
            return
    else:
        for model_path in model_paths:
            try:
                model = YOLO(model_path)
                loaded_models.append((model_path, model))
            except Exception as e:
                print(f"警告: 無法載入模型 '{model_path}' - {e}，將跳過此模型。")
                continue

    if not loaded_models:
        yield {output_status: gr.update(value="錯誤: 沒有任何模型成功載入。")}
        return

    # --- 3. 逐一處理圖片 ---
    total_images = len(image_paths)
    annotated_image_paths = []
    all_result_files = []
    # results_map 儲存圖片路徑與其對應的文字檔路徑，用於後續點擊查詢
    results_map = {}
    # all_texts 用於收集所有圖片的辨識結果文字
    all_texts = []

    for i, image_path_str in enumerate(image_paths):
        image_path = Path(image_path_str)
        progress(i / total_images, desc=f"處理中: {image_path.name}")
        yield {
            output_status: gr.update(value=f"處理中... ({i+1}/{total_images}) - {image_path.name}"),
            output_gallery: gr.update(value=annotated_image_paths)
        }

        original_image = cv2.imread(str(image_path))
        if original_image is None:
            print(f"警告: 無法讀取圖片 '{image_path}'，跳過。")
            continue
        
        annotated_image = original_image.copy()
        image_base_name = image_path.stem

        # --- 3a. YOLO 物件偵測 ---
        yolo_output_content = [f"--- 檔案: {image_path.name} ---"]
        all_detections_for_image = []
        
        for model_path_str, model_obj in loaded_models:
            model_name = Path(model_path_str).name
            yolo_output_content.append(f"--- 模型: {model_name} ---")
            results = model_obj(str(image_path), verbose=False, device="cpu")[0]

            if results.boxes:
                for box in results.boxes:
                    conf = float(box.conf[0])
                    if conf >= conf_threshold:
                        x1, y1, x2, y2 = map(int, box.xyxy[0])
                        cls_id = int(box.cls[0])
                        cls_name = model_obj.names[cls_id]
                        
                        detection_info = {'model_name': model_name, 'class_name': cls_name, 'confidence': conf, 'bbox': (x1, y1, x2, y2)}
                        all_detections_for_image.append(detection_info)
                        yolo_output_content.append(f"  - {cls_name} (信賴度: {conf:.2f}) [座標: {x1},{y1},{x2},{y2}]")
            else:
                yolo_output_content.append("  未偵測到任何物件。")

        # 繪製偵測框
        colors = [(255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 255, 0), (255, 0, 255), (0, 255, 255)]
        color_map = {Path(p).name: colors[idx % len(colors)] for idx, (p, _) in enumerate(loaded_models)}
        for det in all_detections_for_image:
            x1, y1, x2, y2 = det['bbox']
            color = color_map.get(det['model_name'], (200, 200, 200))
            label = f"{det['class_name']} {det['confidence']:.2f}"
            cv2.rectangle(annotated_image, (x1, y1), (x2, y2), color, 2)
            cv2.putText(annotated_image, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)

        # 儲存 YOLO 標註圖
        output_image_path = run_output_dir / f"{image_base_name}_yolo_detected.jpg"
        cv2.imwrite(str(output_image_path), annotated_image)
        annotated_image_paths.append(str(output_image_path))
        all_result_files.append(str(output_image_path))

        # 儲存 YOLO 辨識資訊
        output_yolo_txt_path = run_output_dir / f"{image_base_name}_yolo_objects.txt"
        output_yolo_txt_path.write_text("\n".join(yolo_output_content), encoding='utf-8')
        all_result_files.append(str(output_yolo_txt_path))

        # --- 3b. MLLM 分析 (如果啟用) ---
        output_mllm_txt_path = None
        mllm_result_content = ""
        if enable_mllm:
            try:
                prompt_to_use = mllm_prompt if mllm_prompt and mllm_prompt.strip() else None
                mllm_str = genai.analyze_content_with_gemini(str(image_path), prompt_to_use)
                mllm_result_content = f"--- MLLM 分析結果 ---\n{mllm_str}"
            except Exception as e:
                mllm_result_content = f"--- MLLM 分析失敗 ---\n原因: {e}"
            
            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
        career_prediction_result_content = ""
        if enable_career_prediction:
            try:
                # 呼叫 genai.analyze_content_with_gemini_work 進行職業預測
                career_prediction_str = genai_work.analyze_content_with_gemini_work(str(image_path))
                
                career_prediction_result_content = f"--- 職業預測分析結果 ---\n{career_prediction_str}"
            except Exception as e:
                career_prediction_result_content = f"--- 職業預測分析失敗 ---\n原因: {e}"
            
            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",
        #             "create_datetime": str(global_datetime), 
        #             "image_path": str(image_path), 
        #             "yolo_result": yolo_output_content, 
        #             "enable_mllm": enable_mllm,
        #             "mllm_prompt": mllm_prompt,                    
        #             "mllm_result": mllm_result_content,
        #             "enable_career_prediction": enable_career_prediction, # 新增職業預測狀態到 log
        #             "career_prediction_result": career_prediction_result_content # 新增職業預測結果到 log
        #             }
        
        #mongo.insert_mongodb_log("multi_model_detection",document)  #寫入log方便日後查驗

        # 將本次圖片的結果加入到總列表中
        all_texts.append("\n".join(yolo_output_content))
        if output_mllm_txt_path:
            all_texts.append(output_mllm_txt_path.read_text(encoding='utf-8'))
        if output_career_prediction_txt_path: # 如果有職業預測結果，也加入到總列表中
            all_texts.append(output_career_prediction_txt_path.read_text(encoding='utf-8'))


    # --- 4. 完成處理，打包並更新最終結果 ---
    progress(1, desc="打包結果中...")
    zip_filename = run_output_dir / f"run_{run_id[:8]}_results.zip"
    created_zip_path = create_zip_archive(all_result_files, str(zip_filename))

    final_status = f"處理完成！共 {total_images} 張圖片。結果儲存於: {run_output_dir.absolute()}"
    combined_text_output = "\n\n".join(all_texts)
    
    


    yield {
        output_status: gr.update(value=final_status),
        download_button: gr.update(value=created_zip_path, visible=True),
        output_text: gr.update(value=combined_text_output),
        output_gallery: gr.update(value=annotated_image_paths) # 確保最終 gallery 也被更新
    }

def toggle_mllm_prompt(is_enabled):
    """
    根據 Checkbox 狀態，顯示或隱藏 MLLM prompt 輸入框。
    """
    return gr.update(visible=is_enabled)

def toggle_career_prediction_checkbox(is_enabled):
    """
    根據 Checkbox 狀態，處理職業預測相關邏輯 (目前無需顯示額外輸入框)。
    """
    return None # 職業預測目前不需要額外的輸入框，所以直接返回 None

# --- 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 + 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)

    with gr.Row():
        with gr.Column(scale=1):
            # 輸入元件
            image_input = gr.File(label="上傳圖片", file_count="multiple", file_types=["image"])
            #model_input = gr.File(label="上傳YOLO模型 (.pt)", file_count="multiple", file_types=[".pt"], info="若不提供，將使用預設的 yolov8n.pt 模型。")
            model_input = gr.File(label="上傳YOLO模型 (.pt)", file_count="multiple", file_types=[".pt"])
            
            with gr.Accordion("進階設定(開啟越多功能速度越慢呦)", open=False):
                conf_slider = gr.Slider(minimum=0.1, maximum=1, value=0.40, step=0.05, label="yolo信賴度閾值")
                mllm_enabled_checkbox = gr.Checkbox(label="開啟MLLM辨識", value=False)
                mllm_prompt_input = gr.Textbox(label="自訂 MLLM Prompt (選填)", placeholder="例如：請描述圖中人物的穿著與場景。", visible=False)
                career_prediction_checkbox = gr.Checkbox(label="開啟職業預測", value=False) # 新增職業預測 checkbox

            run_button = gr.Button("開始辨識", variant="primary")

        with gr.Column(scale=2):
            # 輸出元件
            output_gallery = gr.Gallery(label="辨識結果預覽", height=500, object_fit="contain", allow_preview=True)
            output_text = gr.Textbox(label="詳細辨識資訊", lines=15, placeholder="辨識完成後，所有結果將顯示於此。")
            output_status = gr.Textbox(label="執行狀態", interactive=False)
            download_button = gr.File(label="下載所有結果 (.zip)", file_count="single", visible=False)

    # --- 事件綁定 ---
    
    # 點擊 "開始辨識" 按鈕
    run_button.click(
        fn=gradio_multi_model_detection,
        inputs=[image_input, model_input, conf_slider, mllm_enabled_checkbox, mllm_prompt_input, career_prediction_checkbox], # 新增 career_prediction_checkbox
        outputs=[output_gallery, output_status, download_button, output_text]
    )

    # 勾選/取消 "開啟MLLM辨識"
    mllm_enabled_checkbox.change(
        fn=toggle_mllm_prompt,
        inputs=mllm_enabled_checkbox,
        outputs=mllm_prompt_input
    )

    # 勾選/取消 "開啟職業預測"
    career_prediction_checkbox.change(
        fn=toggle_career_prediction_checkbox,
        inputs=career_prediction_checkbox,
        outputs=[] # 職業預測目前不需要更新任何輸出元件
    )

# 啟動 Gradio 應用
if __name__ == "__main__":
    demo.launch(debug=True)
    #demo.launch(share=True)