support text + image hybrid scenariors
This commit is contained in:
parent
79d1775197
commit
ce10e4fbf2
19
config.py
19
config.py
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@ -150,7 +150,7 @@ A_STOCK_MONITOR_CONFIG = {
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"000333.SZ",
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"002230.SZ",
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"300308.SZ",
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"002475.SZ"
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"002475.SZ",
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],
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"bars": ["1D", "1W", "1M"],
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"initial_date": "2015-01-01 00:00:00",
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@ -236,7 +236,7 @@ ITICK_API_KEY = "dfd4bc0caed148d6bc03b960224754ffb5356349e389431f828702b3a27e8a2
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TWITTER_CONFIG = {
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"keys": {
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"api_key": "FRfhlDHnmYc1PCCrVHZdWtqDENr2",
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},
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},
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"base_url": "https://api.scrapecreators.com/v1/twitter/user-tweets?handle={0}&trim=false",
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"monitor_accounts": [
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{"name": "realDonaldTrump"},
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@ -247,7 +247,18 @@ TWITTER_CONFIG = {
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],
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}
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TRUTH_SOCIAL_API = {"api_key": "FRfhlDHnmYc1PCCrVHZdWtqDENr2",
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"user_id": {"realDonaldTrump": "107780257626128497"}}
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TRUTH_SOCIAL_API = {
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"api_key": "FRfhlDHnmYc1PCCrVHZdWtqDENr2",
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"media_config": [
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{
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"media_name": "Truth Social",
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"base_url": "https://api.scrapecreators.com/v1/truthsocial/user/posts",
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"user_info": {
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"WhiteHouse": {"id": "", "full_name": "白宫"},
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"realDonaldTrump": {"id": "107780257626128497", "full_name": "川普"},
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},
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}
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],
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}
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ALI_API_KEY = "sk-216039fdd9ee4bc48667418b23e648d0"
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Binary file not shown.
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@ -19,7 +19,8 @@ logger = logging.logger
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class TruthSocialRetriever:
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def __init__(self) -> None:
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self.api_key = TRUTH_SOCIAL_API.get("api_key", "")
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self.user_info = TRUTH_SOCIAL_API.get("user_id", {})
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self.media_config_list = TRUTH_SOCIAL_API.get("media_config", [])
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# self.user_info = TRUTH_SOCIAL_API.get("user_id", {})
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mysql_user = COIN_MYSQL_CONFIG.get("user", "xch")
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mysql_password = COIN_MYSQL_CONFIG.get("password", "")
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if not mysql_password:
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@ -53,6 +54,10 @@ class TruthSocialRetriever:
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with open(image_post_instruction_file, "r", encoding="utf-8") as f:
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self.image_post_instruction = json.load(f)
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text_image_post_instruction_file = r"./instructions/media_article_image_post_instructions.json"
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with open(text_image_post_instruction_file, "r", encoding="utf-8") as f:
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self.text_image_post_instruction = json.load(f)
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def get_user_id_from_page(self, handle="realDonaldTrump"):
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url = f"https://truthsocial.com/@{handle}"
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headers = {
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@ -90,89 +95,96 @@ class TruthSocialRetriever:
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"""
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headers = {"x-api-key": self.api_key, "Content-Type": "application/json"}
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for user_name, user_id in self.user_info.items():
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params = {
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"handle": user_name, # 用户名
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"user_id": user_id, # 可选，用户 ID
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"next_max_id": None, # 分页时设置为上一次响应的 max_id
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"trim": "false", # 保留完整内容
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}
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for media_config in self.media_config_list:
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media_name = media_config.get("media_name", "")
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logger.info(f"开始获取{media_name}的帖子")
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base_url = media_config.get("base_url", "")
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user_info = media_config.get("user_info", {})
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for user_name, user_details in user_info.items():
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user_id = user_details.get("id", "")
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user_full_name = user_details.get("full_name", "")
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url = "https://api.scrapecreators.com/v1/truthsocial/user/posts"
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logger.info(f"Searching contents for user: {user_name}")
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try:
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response = requests.get(url, headers=headers, params=params)
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response.raise_for_status() # 检查 HTTP 错误
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data = response.json()
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params = {
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"handle": user_name, # 用户名
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"user_id": user_id, # 可选，用户 ID
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"next_max_id": None, # 分页时设置为上一次响应的 max_id
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"trim": "false", # 保留完整内容
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}
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# 提取帖子列表（假设响应中 'posts' 是键，根据实际文档调整）
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if limit is not None and isinstance(limit, int):
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posts = data.get("posts", [])[:limit]
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else:
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posts = data.get("posts", [])
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logger.info(f"Searching contents for user: {user_name}")
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try:
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response = requests.get(base_url, headers=headers, params=params)
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response.raise_for_status() # 检查 HTTP 错误
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data = response.json()
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results = []
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if posts:
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logger.info(f"获取{user_name}帖子: {len(posts)}条")
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for post in posts:
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result = {}
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result["article_id"] = post.get("id")
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result["user_id"] = user_id
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result["user_name"] = user_name
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datetime_text = post.get("created_at")
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datetime_dict = self.transform_datetime(datetime_text)
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timestamp_ms = datetime_dict["timestamp_ms"]
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result["timestamp"] = timestamp_ms
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beijing_time_str = datetime_dict["beijing_time_str"]
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result["date_time"] = beijing_time_str
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result["text"] = post.get("text", "无内容")
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media_attachments = post.get("media_attachments", [])
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result["media_url"] = ""
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result["media_type"] = ""
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result["media_thumbnail"] = ""
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if media_attachments:
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for media_attachment in media_attachments:
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result["media_url"] = media_attachment.get("url")
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result["media_type"] = media_attachment.get("type")
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result["media_thumbnail"] = media_attachment.get(
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"preview_url"
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)
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break
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results.append(result)
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else:
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print("获取帖子失败，请检查 API 密钥或网络。")
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if len(results) > 0:
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result_df = pd.DataFrame(results)
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result_df = self.remove_duplicate_posts(result_df)
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if len(result_df) > 0:
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result_df["analysis_result"] = ""
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result_df["analysis_token"] = 0
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result_df = self.send_wechat_message(result_df)
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result_df = result_df[
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[
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"article_id",
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"user_id",
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"user_name",
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"timestamp",
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"date_time",
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"text",
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"analysis_result",
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"analysis_token",
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"media_url",
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"media_type",
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"media_thumbnail",
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]
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]
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self.db_truth_social_content.insert_data_to_mysql(result_df)
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logger.info(f"已将{len(result_df)}条数据插入到数据库")
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# 提取帖子列表（假设响应中 'posts' 是键，根据实际文档调整）
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if limit is not None and isinstance(limit, int):
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posts = data.get("posts", [])[:limit]
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else:
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logger.info(f"没有数据需要插入到数据库和发送企业微信消息")
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except requests.exceptions.RequestException as e:
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print(f"请求错误: {e}")
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except json.JSONDecodeError as e:
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print(f"JSON 解析错误: {e}")
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posts = data.get("posts", [])
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results = []
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if posts:
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logger.info(f"获取{user_name}帖子: {len(posts)}条")
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for post in posts:
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result = {}
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result["article_id"] = post.get("id")
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result["user_id"] = user_id
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result["user_name"] = user_name
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datetime_text = post.get("created_at")
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datetime_dict = self.transform_datetime(datetime_text)
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timestamp_ms = datetime_dict["timestamp_ms"]
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result["timestamp"] = timestamp_ms
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beijing_time_str = datetime_dict["beijing_time_str"]
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result["date_time"] = beijing_time_str
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result["text"] = post.get("text", "")
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media_attachments = post.get("media_attachments", [])
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result["media_url"] = ""
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result["media_type"] = ""
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result["media_thumbnail"] = ""
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if media_attachments:
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for media_attachment in media_attachments:
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result["media_url"] = media_attachment.get("url")
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result["media_type"] = media_attachment.get("type")
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result["media_thumbnail"] = media_attachment.get(
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"preview_url"
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)
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break
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results.append(result)
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else:
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print("获取帖子失败，请检查 API 密钥或网络。")
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if len(results) > 0:
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result_df = pd.DataFrame(results)
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result_df = self.remove_duplicate_posts(result_df)
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if len(result_df) > 0:
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result_df["analysis_result"] = ""
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result_df["analysis_token"] = 0
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result_df = self.send_wechat_message(result_df, user_full_name)
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result_df = result_df[
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[
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"article_id",
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"user_id",
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"user_name",
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"timestamp",
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"date_time",
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"text",
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"analysis_result",
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"analysis_token",
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"media_url",
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"media_type",
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"media_thumbnail",
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]
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]
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self.db_truth_social_content.insert_data_to_mysql(result_df)
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logger.info(f"已将{len(result_df)}条数据插入到数据库")
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else:
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logger.info(f"没有数据需要插入到数据库和发送企业微信消息")
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except requests.exceptions.RequestException as e:
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print(f"请求错误: {e}")
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except json.JSONDecodeError as e:
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print(f"JSON 解析错误: {e}")
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def send_message_by_json_file(self, json_file_name: str):
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with open(json_file_name, "r", encoding="utf-8") as f:
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@ -204,7 +216,7 @@ class TruthSocialRetriever:
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logger.error(f"删除重复的行失败: {e}")
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return result_df
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def send_wechat_message(self, result_df: pd.DataFrame):
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def send_wechat_message(self, result_df: pd.DataFrame, user_full_name: str):
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if self.wechat is None:
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logger.error("企业微信未初始化")
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return
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@ -213,7 +225,72 @@ class TruthSocialRetriever:
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date_time = row["date_time"]
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text = row["text"]
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media_thumbnail = row["media_thumbnail"]
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if media_thumbnail and len(media_thumbnail) > 0:
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if len(text) > 0:
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if media_thumbnail and len(media_thumbnail) > 0:
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contents = []
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contents.append(f"## {user_full_name}推文")
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contents.append(text)
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contents.append(f"## 推文时间")
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contents.append(date_time)
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mark_down_text = "\n\n".join(contents)
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self.wechat.send_markdown(mark_down_text)
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response, image_path, base64_str, md5_str = self.wechat.send_image(media_thumbnail)
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image_format = "jpg"
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if image_path is not None and len(image_path) > 0:
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image_format = image_path.split(".")[-1]
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if image_format == "jpeg":
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image_format = "jpg"
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analysis_result, analysis_token = self.analyze_truth_social_content(
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text=mark_down_text,
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image_stream=base64_str,
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image_format=image_format,
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media_type="hybrid",
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user_full_name=user_full_name
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)
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if analysis_result is not None and len(analysis_result) > 0:
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result_df.at[index, "analysis_result"] = analysis_result
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result_df.at[index, "analysis_token"] = analysis_token
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else:
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result_df.at[index, "analysis_result"] = ""
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result_df.at[index, "analysis_token"] = 0
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analysis_text = f"\n\n## 上述图文分析结果\n\n{analysis_result}"
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analysis_text += f"\n\n## 上述图文分析token\n\n{analysis_token}"
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self.wechat.send_markdown(analysis_text)
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else:
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contents = []
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contents.append(f"## {user_full_name}推文")
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contents.append(text)
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contents.append(f"## 推文时间")
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contents.append(date_time)
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mark_down_text = "\n\n".join(contents)
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analysis_result, analysis_token = self.analyze_truth_social_content(
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text=text,
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image_stream=None,
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image_format=None,
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media_type="text",
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user_full_name=user_full_name
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)
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result_df.at[index, "analysis_result"] = analysis_result
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result_df.at[index, "analysis_token"] = analysis_token
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analysis_text = f"\n\n## 分析结果\n\n{analysis_result}"
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analysis_text += f"\n\n## 分析token\n\n{analysis_token}"
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if self.calculate_bytes(mark_down_text + analysis_text) > 4096:
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self.wechat.send_markdown(mark_down_text)
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if self.calculate_bytes(analysis_text) > 4096:
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half_analysis_text_length = len(analysis_text) // 2
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analysis_1st = analysis_text[:half_analysis_text_length].strip()
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analysis_2nd = analysis_text[half_analysis_text_length:].strip()
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self.wechat.send_markdown(
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f"## 分析结果第一部分\n\n{analysis_1st}"
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)
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self.wechat.send_markdown(
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f"## 分析结果第二部分\n\n{analysis_2nd}"
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)
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else:
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self.wechat.send_markdown(f"## 分析结果\n\n{analysis_text}")
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else:
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self.wechat.send_markdown(mark_down_text + analysis_text)
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elif media_thumbnail and len(media_thumbnail) > 0:
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response, image_path, base64_str, md5_str = self.wechat.send_image(media_thumbnail)
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image_format = "jpg"
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if image_path is not None and len(image_path) > 0:
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@ -221,10 +298,11 @@ class TruthSocialRetriever:
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if image_format == "jpeg":
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image_format = "jpg"
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analysis_result, analysis_token = self.analyze_truth_social_content(
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text=None,
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text="",
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image_stream=base64_str,
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image_format=image_format,
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media_type="image"
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media_type="image",
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user_full_name=user_full_name
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)
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if analysis_result is not None and len(analysis_result) > 0:
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result_df.at[index, "analysis_result"] = analysis_result
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@ -236,38 +314,7 @@ class TruthSocialRetriever:
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analysis_text += f"\n\n## 上述图片分析token\n\n{analysis_token}"
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self.wechat.send_markdown(analysis_text)
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else:
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contents = []
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contents.append(f"## 川普推文")
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contents.append(text)
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contents.append(f"## 推文时间")
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contents.append(date_time)
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mark_down_text = "\n\n".join(contents)
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analysis_result, analysis_token = self.analyze_truth_social_content(
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text=text,
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image_stream=None,
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image_format=None,
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media_type="text"
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)
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result_df.at[index, "analysis_result"] = analysis_result
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result_df.at[index, "analysis_token"] = analysis_token
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analysis_text = f"\n\n## 分析结果\n\n{analysis_result}"
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analysis_text += f"\n\n## 分析token\n\n{analysis_token}"
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if self.calculate_bytes(mark_down_text + analysis_text) > 4096:
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self.wechat.send_markdown(mark_down_text)
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if self.calculate_bytes(analysis_text) > 4096:
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half_analysis_text_length = len(analysis_text) // 2
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analysis_1st = analysis_text[:half_analysis_text_length].strip()
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analysis_2nd = analysis_text[half_analysis_text_length:].strip()
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self.wechat.send_markdown(
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f"## 分析结果第一部分\n\n{analysis_1st}"
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)
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self.wechat.send_markdown(
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f"## 分析结果第二部分\n\n{analysis_2nd}"
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)
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else:
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self.wechat.send_markdown(f"## 分析结果\n\n{analysis_text}")
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else:
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self.wechat.send_markdown(mark_down_text + analysis_text)
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continue
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except Exception as e:
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logger.error(f"发送企业微信消息失败: {e}")
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continue
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@ -276,10 +323,13 @@ class TruthSocialRetriever:
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def calculate_bytes(self, text: str):
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return len(text.encode("utf-8"))
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def analyze_truth_social_content(self, text: str, image_stream: str, image_format: str, media_type: str):
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def analyze_truth_social_content(self, text: str, image_stream: str, image_format: str, media_type: str, user_full_name: str):
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try:
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token = 0
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if media_type == "image":
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if text is None:
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text = ""
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image_text = ""
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if media_type in ["image", "hybrid"]:
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if image_stream is None or len(image_stream) == 0:
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return "", 0
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instructions = self.image_instruction.get("Instructions", "")
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|
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@ -300,28 +350,42 @@ class TruthSocialRetriever:
|
|||
messages=messages_local,
|
||||
)
|
||||
if response.status_code == 200:
|
||||
text = (
|
||||
image_text = (
|
||||
response.get("output", {})
|
||||
.get("choices", [])[0]
|
||||
.get("message", {})
|
||||
.get("content", "")
|
||||
)
|
||||
temp_image_text = ""
|
||||
if isinstance(image_text, list):
|
||||
for item in image_text:
|
||||
if isinstance(item, dict):
|
||||
temp_image_text += item.get("text", "") + "\n\n"
|
||||
elif isinstance(item, str):
|
||||
temp_image_text += item + "\n\n"
|
||||
else:
|
||||
pass
|
||||
image_text = temp_image_text.strip()
|
||||
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
|
||||
text += image_text
|
||||
|
||||
context = text
|
||||
if media_type == "text":
|
||||
instructions = self.text_instruction.get("Instructions", "")
|
||||
instructions = self.text_instruction.get("Instructions", "").format(user_full_name)
|
||||
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", "")
|
||||
elif media_type == "image":
|
||||
instructions = self.image_post_instruction.get("Instructions", "").format(user_full_name)
|
||||
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}"
|
||||
elif media_type == "hybrid":
|
||||
instructions = self.text_image_post_instruction.get("Instructions", "").format(user_full_name)
|
||||
output = self.text_image_post_instruction.get("Output", "").format(user_full_name)
|
||||
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",
|
||||
|
|
|
|||
|
|
@ -67,6 +67,7 @@ class Wechat:
|
|||
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(), image_path, base64_str, md5_str
|
||||
|
|
|
|||
|
|
@ -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的内容格式是从社媒图文并茂的推文中获取的信息，包括: ### {0}推文原文\n\n### 推文时间\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{0}在社交媒体发布的图文推文，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. 将推文原文翻译成中文，要求语义通顺，\n2. 结合推文原文，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### {0}推文翻译\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"
|
||||
}
|
||||
|
|
@ -1,5 +1,5 @@
|
|||
{
|
||||
"Context": "{0}\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",
|
||||
"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{0}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"
|
||||
}
|
||||
|
|
@ -1,4 +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",
|
||||
"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{0}在社交媒体发布的图文推文，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"
|
||||
}
|
||||
Loading…
Reference in New Issue