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a/WeiXin/Screenshots/t4_temp_history_analyzed.jpg b/WeiXin/Screenshots/t4_temp_history_analyzed.jpg new file mode 100644 index 0000000..00391aa Binary files /dev/null and b/WeiXin/Screenshots/t4_temp_history_analyzed.jpg differ diff --git a/WeiXin/Screenshots/t4_temp_history_check.jpg b/WeiXin/Screenshots/t4_temp_history_check.jpg new file mode 100644 index 0000000..c00ef99 Binary files /dev/null and b/WeiXin/Screenshots/t4_temp_history_check.jpg differ diff --git a/WeiXin/Screenshots/t4_temp_input_check.jpg b/WeiXin/Screenshots/t4_temp_input_check.jpg index 3502320..cf1a2af 100644 Binary files a/WeiXin/Screenshots/t4_temp_input_check.jpg and b/WeiXin/Screenshots/t4_temp_input_check.jpg differ diff --git a/WeiXin/T4_InputLlmText.py b/WeiXin/T4_InputLlmText.py index 8799ebf..54c85cd 100644 --- a/WeiXin/T4_InputLlmText.py +++ b/WeiXin/T4_InputLlmText.py @@ -11,7 +11,7 @@ project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) if project_root not in sys.path: sys.path.append(project_root) -from WeiXin.WxUtil import find_input_box_center, perform_input_action +from WeiXin.WxUtil import find_input_box_center, perform_input_action, analyze_chat_image from Util.LlmUtil import get_llm_response # 配置日志 @@ -37,10 +37,38 @@ async def generate_and_input(): d = u2.connect() logger.info(f"设备连接成功: {d.info.get('serial')}") - # 2. 调用 LLM 生成回复 - # 模拟一个简单的场景：针对之前的对话生成回复 - prompt = "对方说：'AI 助手我现在可以开始和你聊天了！'，请给出一个简短、友好且自然的回复，不超过 20 字。不要包含任何解释性文字。" - logger.info(f"正在请求 LLM 生成回复, Prompt: {prompt}") + # 2. 截图并识别对话历史 + screenshot_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "Screenshots") + if not os.path.exists(screenshot_dir): + os.makedirs(screenshot_dir) + + tmp_shot = os.path.join(screenshot_dir, "t4_temp_history_check.jpg") + d.screenshot(tmp_shot) + + analyzed_shot = os.path.join(screenshot_dir, "t4_temp_history_analyzed.jpg") + dialogue_log = analyze_chat_image(tmp_shot, analyzed_shot) + + history_text = "" + if dialogue_log: + history_text = "\n".join(dialogue_log) + logger.info(f"提取到对话历史:\n{history_text}") + else: + logger.warning("未提取到对话历史") + history_text = "（无对话历史）" + + # 3. 调用 LLM 生成回复 + persona = ( + "你是一名99年毕业，有27年教学经验的小学高级教师，大张老师。对作文批阅不下20000个，" + "非常熟悉小学阶段、初二以前的初中阶段作文教学。你的回复必须专业、严谨、沉稳且富有权威感。" + "严禁使用任何搞笑、幽默、有趣或轻浮的语气。要以资深教育者的身份给出严肃、务实的指导建议。" + ) + prompt = ( + f"人设：{persona}\n\n" + f"以下是微信聊天记录（'对方'和'我'的对话）：\n{history_text}\n\n" + "请作为大张老师，针对最后的消息给出一个简短、专业且严肃的回复，不超过 60 字。" + "禁止出现任何幽默或卖萌的词汇，保持特级教师的庄重感。直接输出回复内容，不要包含任何解释性文字。" + ) + logger.info(f"正在以'严谨的大张老师'身份请求 LLM 生成回复...") full_response = "" async for chunk in get_llm_response(prompt, stream=False): @@ -53,22 +81,14 @@ async def generate_and_input(): logger.error("LLM 生成内容为空，停止执行。") return - # 3. 识别输入框位置 - # 先截个图供识别使用 - screenshot_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "Screenshots") - if not os.path.exists(screenshot_dir): - os.makedirs(screenshot_dir) - - tmp_shot = os.path.join(screenshot_dir, "t4_temp_input_check.jpg") - d.screenshot(tmp_shot) - + # 4. 识别输入框位置 center_point, rect_box = find_input_box_center(tmp_shot) if center_point: cx, cy = center_point logger.info(f"识别到输入框中心: ({cx}, {cy})") - # 4. 点击输入框并输入文字 + # 5. 点击输入框并输入文字并发送 # 使用 WxUtil 中的 perform_input_action # 该函数内部会先点击坐标，然后输入内容 # 设置 auto_send=True，输入文字后点击发送