From 8c5bb34e5a627b74fb5ab6df159fb6fff1a03df3 Mon Sep 17 00:00:00 2001 From: blade <8019068@qq.com> Date: Mon, 27 Oct 2025 15:07:12 +0800 Subject: [PATCH] support analyze image --- .../truth_social_retriever.cpython-312.pyc | Bin 15205 -> 18034 bytes core/media/truth_social_retriever.py | 99 ++++++++++++++---- core/wechat.py | 20 ++-- instructions/media_article_instructions.json | 2 +- instructions/media_image_instructions.json | 4 + .../media_image_post_instructions.json | 4 + 6 files changed, 103 insertions(+), 26 deletions(-) create mode 100644 instructions/media_image_instructions.json create mode 100644 instructions/media_image_post_instructions.json diff --git a/core/media/__pycache__/truth_social_retriever.cpython-312.pyc b/core/media/__pycache__/truth_social_retriever.cpython-312.pyc index a48cd429a9c3341f9249e2665eb17ed7ab9a5cee..150311b5027bc8f1856c76540fbd493a3417f737 100644 GIT binary patch delta 5601 zcmb7IeNbChc7OMYr!Vw@(C0$}34{>hLxgY4H)Aly;5a6+9f)lr^PWK%B>W_Tp--?* 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uA&AJS!~7g`^bp}aLXobl$Nr6J11_lEE@>0|5@j_uI*uBE_c29KSNabh7)dAq diff --git a/core/media/truth_social_retriever.py b/core/media/truth_social_retriever.py index f4e7b67..5b4a6d3 100644 --- a/core/media/truth_social_retriever.py +++ b/core/media/truth_social_retriever.py @@ -41,9 +41,17 @@ class TruthSocialRetriever: os.makedirs(self.save_path, exist_ok=True) self.ali_api_key = ALI_API_KEY - instruction_file = r"./instructions/media_article_instructions.json" - with open(instruction_file, "r", encoding="utf-8") as f: - self.instruction = json.load(f) + text_instruction_file = r"./instructions/media_article_instructions.json" + with open(text_instruction_file, "r", encoding="utf-8") as f: + self.text_instruction = json.load(f) + + image_instruction_file = r"./instructions/media_image_instructions.json" + with open(image_instruction_file, "r", encoding="utf-8") as f: + self.image_instruction = json.load(f) + + image_post_instruction_file = r"./instructions/media_image_post_instructions.json" + with open(image_post_instruction_file, "r", encoding="utf-8") as f: + self.image_post_instruction = json.load(f) def get_user_id_from_page(self, handle="realDonaldTrump"): url = f"https://truthsocial.com/@{handle}" @@ -135,14 +143,6 @@ class TruthSocialRetriever: print("获取帖子失败，请检查 API 密钥或网络。") if len(results) > 0: - # user_path = os.path.join(self.save_path, user_name) - # os.makedirs(user_path, exist_ok=True) - # now_date_time = datetime.now().strftime("%Y%m%d%H%M%S") - # json_file_name = os.path.join(user_path, f"{user_name}_{now_date_time}.json") - # # 将results内容写入json_file_name文件中 - # with open(json_file_name, 'w', encoding='utf-8') as f: - # json.dump(results, f, ensure_ascii=False, indent=2) - # logger.info(f"已将{len(results)}条数据保存到: {json_file_name}") result_df = pd.DataFrame(results) result_df = self.remove_duplicate_posts(result_df) @@ -214,7 +214,27 @@ class TruthSocialRetriever: text = row["text"] media_thumbnail = row["media_thumbnail"] if media_thumbnail and len(media_thumbnail) > 0: - self.wechat.send_image(media_thumbnail) + response, image_path, base64_str, md5_str = self.wechat.send_image(media_thumbnail) + image_format = "jpg" + if image_path is not None and len(image_path) > 0: + image_format = image_path.split(".")[-1] + if image_format == "jpeg": + image_format = "jpg" + analysis_result, analysis_token = self.analyze_truth_social_content( + text=None, + image_stream=base64_str, + image_format=image_format, + media_type="image" + ) + if analysis_result is not None and len(analysis_result) > 0: + result_df.at[index, "analysis_result"] = analysis_result + result_df.at[index, "analysis_token"] = analysis_token + else: + result_df.at[index, "analysis_result"] = "" + result_df.at[index, "analysis_token"] = 0 + analysis_text = f"\n\n## 上述图片分析结果\n\n{analysis_result}" + analysis_text += f"\n\n## 上述图片分析token\n\n{analysis_token}" + self.wechat.send_markdown(analysis_text) else: contents = [] contents.append(f"## 川普推文") @@ -223,7 +243,10 @@ class TruthSocialRetriever: contents.append(date_time) mark_down_text = "\n\n".join(contents) analysis_result, analysis_token = self.analyze_truth_social_content( - text + text=text, + image_stream=None, + image_format=None, + media_type="text" ) result_df.at[index, "analysis_result"] = analysis_result result_df.at[index, "analysis_token"] = analysis_token @@ -253,12 +276,52 @@ class TruthSocialRetriever: def calculate_bytes(self, text: str): return len(text.encode("utf-8")) - def analyze_truth_social_content(self, text: str): + def analyze_truth_social_content(self, text: str, image_stream: str, image_format: str, media_type: str): try: + token = 0 + if media_type == "image": + if image_stream is None or len(image_stream) == 0: + return "", 0 + instructions = self.image_instruction.get("Instructions", "") + output = self.image_instruction.get("Output", "") + prompt = f"# Instructions\n\n{instructions}\n\n# Output\n\n{output}" + messages_local = [ + { + "role": "user", + "content": [ + {"image": f"data:image/{image_format};base64,{image_stream}"}, # base64 字符串 + {"text": prompt} # 你的 prompt + ] + } + ] + response = dashscope.MultiModalConversation.call( + api_key=self.ali_api_key, + model='qwen-vl-plus', + messages=messages_local, + ) + if response.status_code == 200: + text = ( + response.get("output", {}) + .get("choices", [])[0] + .get("message", {}) + .get("content", "") + ) + token = response.get("usage", {}).get("total_tokens", 0) + else: + text = f"{response.code} {response.message} 无法分析图片" + token = 0 + + if text is None or len(text) == 0: + return "", 0 context = text - instructions = self.instruction.get("Instructions", "") - output = self.instruction.get("Output", "") - prompt = f"# Context\n\n{context}\n\n# Instructions\n\n{instructions}\n\n# Output\n\n{output}" + if media_type == "text": + instructions = self.text_instruction.get("Instructions", "") + output = self.text_instruction.get("Output", "") + prompt = f"# Context\n\n{context}\n\n# Instructions\n\n{instructions}\n\n# Output\n\n{output}" + else: + instructions = self.image_post_instruction.get("Instructions", "") + output = self.image_post_instruction.get("Output", "") + prompt = f"# Context\n\n{context}\n\n# Instructions\n\n{instructions}\n\n# Output\n\n{output}" response = dashscope.Generation.call( api_key=self.ali_api_key, model="qwen-plus", @@ -276,7 +339,7 @@ class TruthSocialRetriever: .get("message", {}) .get("content", "") ) - token = response.get("usage", {}).get("total_tokens", 0) + token += response.get("usage", {}).get("total_tokens", 0) else: response_contents = f"{response.code} {response.message}" token = 0 diff --git a/core/wechat.py b/core/wechat.py index 14ce006..1b4403d 100644 --- a/core/wechat.py +++ b/core/wechat.py @@ -9,6 +9,8 @@ import requests import base64 import hashlib import json +import os +import time logger = logging.logger class Wechat: @@ -17,6 +19,8 @@ class Wechat: # 只要启动代码文件在根目录，config就能找到 self.key = key self.url = f"https://qyapi.weixin.qq.com/cgi-bin/webhook/send?key={self.key}" + self.image_path = r"./output/wechat/image" + os.makedirs(self.image_path, exist_ok=True) def send_text(self, text: str): """ @@ -46,12 +50,6 @@ class Wechat: """ 发送图片消息 """ - # data = { - # "msgtype": "image", - # "image": {"url": image_url} - # } - # response = requests.post(self.url, json=data) - # return response.json() image_bytes = self.download_image(image_url) base64_str, md5_str = self.get_base64_and_md5(image_bytes) data = { @@ -61,9 +59,17 @@ class Wechat: "md5": md5_str, } } + # 获取url中图片的名称 + image_name = image_url.split("/")[-1].split(".")[0] + # 获取当前时间，格式为YYYYMMDDHHMMSS + now_time = time.strftime("%Y%m%d%H%M%S", time.localtime()) + image_name = f"{image_name}_{now_time}.jpg" + image_path = os.path.join(self.image_path, image_name) + with open(image_path, "wb") as f: + f.write(image_bytes) response = requests.post(self.url, json=data) response.raise_for_status() - return response.json() + return response.json(), image_path, base64_str, md5_str def download_image(self, image_url): """下载图片并返回 bytes""" diff --git a/instructions/media_article_instructions.json b/instructions/media_article_instructions.json index c21925e..f2f62cd 100644 --- a/instructions/media_article_instructions.json +++ b/instructions/media_article_instructions.json @@ -1,5 +1,5 @@ { "Context": "{0}\n\n", - "Instructions": "你是一个专业的时政与金融分析师，你的任务是分析推文，结合推文时间（北京时间），联网搜索，并给出分析结果。\n\nContext中的文章，就是特朗普在社交媒体发布的文章，不要怀疑这一点。\n并基于此文章内容进行分析。\n\n要求：\n1. 翻译推文为中文，要求符合中文表达习惯；\n2. 分析推文内容，给出推文的核心观点；\n3. 人物分析：分析推文涉及人物以及人物简介；\n4. 区域分析：包括国家与地区；\n5. 行业以及影响分析；\n6. 经济与金融分析：分析涉及经济与金融影响，包括美股、虚拟货币以及中国A股，并列出最有可能被影响的股票品种或虚拟货币的名称与代码；\n\n", + "Instructions": "您是一位资深的国际时事与军事政治评论员与经济、金融分析师，你的任务是分析推文，结合推文时间（北京时间），联网搜索，并给出分析结果。\n\nContext中的文章，就是特朗普在社交媒体发布的文章，不要怀疑这一点。\n并基于此文章内容进行分析。\n\n要求：\n1. 翻译推文为中文，要求符合中文表达习惯；\n2. 分析推文内容，给出推文的核心观点；\n3. 人物分析：分析推文涉及人物以及人物简介；\n4. 区域分析：包括国家与地区；\n5. 行业以及影响分析；\n6. 经济与金融分析：分析涉及经济与金融影响，包括美股、虚拟货币以及中国A股，并列出最有可能被影响的股票品种或虚拟货币的名称与代码；\n\n", "Output": "## 输出要求\n\n除了翻译之外，核心观点+人物分析+区域分析+行业及影响分析+经济与金融分析，不超过1000汉字。\n要求对人名、区域、行业、金融产品、股票代码等专属名词，进行粗体处理。\n\n## 输出格式\n\n### 翻译\n\n### 人物分析\n\n### 区域分析\n\n### 行业及影响分析\n\n### 经济与金融分析\n\n" } \ No newline at end of file diff --git a/instructions/media_image_instructions.json b/instructions/media_image_instructions.json new file mode 100644 index 0000000..e38c8a4 --- /dev/null +++ b/instructions/media_image_instructions.json @@ -0,0 +1,4 @@ +{ + "Instructions": "您是一位资深的国际时事与军事政治评论员与经济、金融分析师，请阅读这张图片，并给出分析结果，分析内容包括：图片文字翻译为中文，将非文字部分的图像，做出图像场景描述。\n\n要求：\n1. 如果图片只有图像信息，请根据图像做出场景描述。\n2. 如果图片既有文字又有图像内容，请提取其中的文字，并将文字翻译为中文，并对非文字部分的图像，做出图像场景描述。\n3. 如果图片中只有文字，请提取其中的文字，并将文字翻译为中文。", + "Output": "## 输出要求\n\n图片中的文字原文、文字中文翻译、图片场景描述。\n\n## 输出格式如下：\n\n### 图中文字原文\n\n### 图中文字中文翻译\n\n### 图片场景描述" +} \ No newline at end of file diff --git a/instructions/media_image_post_instructions.json b/instructions/media_image_post_instructions.json new file mode 100644 index 0000000..9511a77 --- /dev/null +++ b/instructions/media_image_post_instructions.json @@ -0,0 +1,4 @@ +{ + "Instructions": "您是一位资深的国际时事与军事政治评论员与经济、金融分析师，Context的内容是通过图片分析到的信息，你的任务是分析其中的信息，进行联网搜索，并给出分析结果。\n\n该信息，就是特朗普在社交媒体发布的，不要怀疑这一点。\n并基于此文章内容进行分析。\n\n要求：\n1. 分析图片中的文字与图像场景描述，给出推文的核心观点；\n2. 人物分析：分析推文涉及人物以及人物简介；\n3. 区域分析：包括国家与地区；\n4. 行业以及影响分析；\n5. 经济与金融分析：分析涉及经济与金融影响，包括美股、虚拟货币以及中国A股，并列出最有可能被影响的股票品种或虚拟货币的名称与代码；\n\n", + "Output": "## 输出要求\n\n要求将Context中的文字原文，中文翻译与图片场景描述，进行原文输出，之外的核心观点+人物分析+区域分析+行业及影响分析+经济与金融分析，不超过1000汉字。\n要求对人名、区域、行业、金融产品、股票代码等专属名词，进行粗体处理。\n\n## 输出格式\n\n### 图中文字原文\n\n### 图中文字中文翻译\n\n### 图片场景描述\n\n### 人物分析\n\n### 区域分析\n\n### 行业及影响分析\n\n### 经济与金融分析\n\n" +} \ No newline at end of file