1002 lines
48 KiB
Python
1002 lines
48 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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import os
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import time
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import random
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import argparse
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import json
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from datetime import datetime
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import sys
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import traceback
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import logging # Add logging
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import re
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# sys.path.append('/root/autodl-tmp') # No longer needed if running as a module or if path is set correctly
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# 从本地模块导入
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# from TravelContentCreator.core.ai_agent import AI_Agent # Remove project name prefix
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# from TravelContentCreator.core.topic_parser import TopicParser # Remove project name prefix
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# ResourceLoader is now used implicitly via PromptManager
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# from TravelContentCreator.utils.resource_loader import ResourceLoader
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# from TravelContentCreator.utils.prompt_manager import PromptManager # Remove project name prefix
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# from ..core import contentGen as core_contentGen # Change to absolute import
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# from ..core import posterGen as core_posterGen # Change to absolute import
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# from ..core import simple_collage as core_simple_collage # Change to absolute import
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from core.ai_agent import AI_Agent
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from core.topic_parser import TopicParser
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from utils.prompt_manager import PromptManager # Keep this as it's importing from the same level package 'utils'
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from utils import content_generator as core_contentGen
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from core import poster_gen as core_posterGen
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from core import simple_collage as core_simple_collage
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from .output_handler import OutputHandler # <-- 添加导入
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from utils.content_judger import ContentJudger # <-- 添加ContentJudger导入
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class tweetTopic:
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def __init__(self, index, date, logic, object, product, product_logic, style, style_logic, target_audience, target_audience_logic):
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self.index = index
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self.date = date
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self.logic = logic
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self.object = object
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self.product = product
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self.product_logic = product_logic
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self.style = style
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self.style_logic = style_logic
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self.target_audience = target_audience
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self.target_audience_logic = target_audience_logic
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class tweetTopicRecord:
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def __init__(self, topics_list, system_prompt, user_prompt, run_id):
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self.topics_list = topics_list
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self.system_prompt = system_prompt
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self.user_prompt = user_prompt
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self.run_id = run_id
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class tweetContent:
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def __init__(self, result, prompt, run_id, article_index, variant_index):
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self.result = result
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self.prompt = prompt
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self.run_id = run_id
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self.article_index = article_index
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self.variant_index = variant_index
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try:
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self.json_data = self.split_content(result)
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except Exception as e:
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logging.error(f"Failed to parse AI result for {article_index}_{variant_index}: {e}")
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logging.debug(f"Raw result: {result[:500]}...") # Log partial raw result
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self.json_data = {"title": "", "content": "", "tag": "", "error": True, "raw_result": e} # 不再包含raw_result
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def split_content(self, result):
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try:
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processed_result = result
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if "</think>" in result:
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processed_result = result.split("</think>")[1] # Take part after </think>
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# 以json 格式输出
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json_data = json.loads(processed_result)
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json_data["error"] = False
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json_data["raw_result"] = None
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json_data["judge_success"] = None
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return json_data
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except Exception as e:
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logging.warning(f"解析内容时出错: {e}, 使用默认空内容")
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# 创建一个新的json_data而不是使用未定义的变量
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return {
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"title": "",
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"content": "",
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"error": True,
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"raw_result": str(e),
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"judge_success": False
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}
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def get_json_data(self):
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"""Returns the generated JSON data dictionary."""
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return self.json_data
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def get_prompt(self):
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"""Returns the user prompt used to generate this content."""
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return self.prompt
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def get_content(self):
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return self.content
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def get_title(self):
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return self.title
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def generate_topics(ai_agent, system_prompt, user_prompt, run_id, temperature=0.2, top_p=0.5, presence_penalty=1.5):
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"""生成选题列表 (run_id is now passed in, output_dir removed as argument)"""
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logging.info("Starting topic generation...")
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time_start = time.time()
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# Call AI agent work method (updated return values)
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result, tokens, time_cost = ai_agent.work(
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system_prompt, user_prompt, "", temperature, top_p, presence_penalty
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)
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logging.info(f"Topic generation API call completed in {time_cost:.2f}s. Estimated tokens: {tokens}")
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# Parse topics
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result_list = TopicParser.parse_topics(result, run_id)
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if not result_list:
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logging.warning("Topic parsing resulted in an empty list.")
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# Optionally handle raw result logging here if needed, but saving is responsibility of OutputHandler
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# error_log_path = os.path.join(output_dir, run_id, f"topic_parsing_error_{run_id}.txt") # output_dir is not available here
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# try:
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# # ... (save raw output logic) ...
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# except Exception as log_err:
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# logging.error(f"Failed to save raw AI output on parsing failure: {log_err}")
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# 直接返回解析后的列表
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return result_list
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def generate_single_content(ai_agent, system_prompt, user_prompt, item, run_id,
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article_index, variant_index, temperature=0.3, top_p=0.4, presence_penalty=1.5,
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max_retries=3):
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"""Generates single content variant data. Returns (content_json, user_prompt) or (None, None)."""
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logging.info(f"Generating content for topic {article_index}, variant {variant_index}")
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if not system_prompt or not user_prompt:
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logging.error("System or User prompt is empty. Cannot generate content.")
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return None, None
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logging.debug(f"Using pre-constructed prompts. User prompt length: {len(user_prompt)}")
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# 实现重试逻辑
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retry_count = 0
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last_result = None
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last_tokens = None
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last_time_cost = None
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while retry_count <= max_retries:
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try:
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# 只有重试时增加延迟和调整参数
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if retry_count > 0:
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# 添加随机延迟避免频繁请求
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delay = 1 + random.random() * 2 # 1-3秒随机延迟
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logging.info(f"内容生成重试 ({retry_count}/{max_retries})，等待{delay:.1f}秒后尝试...")
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time.sleep(delay)
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# 调整温度参数，增加多样性
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adjusted_temperature = min(temperature + (retry_count * 0.1), 0.9)
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logging.info(f"调整温度参数为: {adjusted_temperature}")
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else:
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adjusted_temperature = temperature
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# Generate content (non-streaming work returns result, tokens, time_cost)
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result, tokens, time_cost = ai_agent.work(
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system_prompt, user_prompt, "", adjusted_temperature, top_p, presence_penalty
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)
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last_result = result
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last_tokens = tokens
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last_time_cost = time_cost
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if result is None: # Check if AI call failed completely
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logging.error(f"AI agent work failed for {article_index}_{variant_index}. No result returned.")
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retry_count += 1
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continue
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logging.info(f"Content generation for {article_index}_{variant_index} completed in {time_cost:.2f}s. Estimated tokens: {tokens}")
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# --- Create tweetContent object (handles parsing) ---
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tweet_content = tweetContent(result, user_prompt, run_id, article_index, variant_index)
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content_json = tweet_content.get_json_data()
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# 检查是否成功解析到有效内容
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if not content_json.get("error", False) and content_json.get("title") and content_json.get("content"):
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# 成功获取有效内容
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if retry_count > 0:
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logging.info(f"在第{retry_count}次重试后成功获取有效内容")
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# 返回成功结果
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return content_json, user_prompt
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else:
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logging.warning(f"内容解析失败或内容不完整，结果: {content_json.get('error')}, 标题长度: {len(content_json.get('title', ''))}, 内容长度: {len(content_json.get('content', ''))}")
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# 如果到这里，说明内容生成或解析有问题，需要重试
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retry_count += 1
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except Exception as e:
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logging.exception(f"Error during content generation attempt {retry_count+1} for {article_index}_{variant_index}:")
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retry_count += 1
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if retry_count <= max_retries:
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logging.info(f"将尝试第{retry_count}次重试...")
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else:
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logging.error(f"达到最大重试次数({max_retries})，无法生成有效内容")
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# 所有重试都失败，返回最后一次的结果（即使不完整）
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logging.warning(f"在{max_retries}次尝试后仍未生成有效内容，返回最后一次结果")
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# 如果有最后一次结果，尝试使用它
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if last_result:
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try:
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tweet_content = tweetContent(last_result, user_prompt, run_id, article_index, variant_index)
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content_json = tweet_content.get_json_data()
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return content_json, user_prompt
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except Exception as e:
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logging.exception(f"Error processing last result: {e}")
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# 完全失败的情况，返回空内容
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return {"title": "", "content": "", "error": True, "judge_success": False}, user_prompt
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def generate_content(ai_agent, system_prompt, topics, output_dir, run_id, prompts_dir, resource_dir,
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variants=2, temperature=0.3, start_index=0, end_index=None):
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"""根据选题生成内容"""
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if not topics:
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logging.warning("没有选题，无法生成内容")
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return
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# 确定处理范围
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if end_index is None or end_index > len(topics):
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end_index = len(topics)
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topics_to_process = topics[start_index:end_index]
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logging.info(f"准备处理{len(topics_to_process)}个选题...")
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# 创建汇总文件
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# summary_file = ResourceLoader.create_summary_file(output_dir, run_id, len(topics_to_process))
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# 处理每个选题
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processed_results = []
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for i, item in enumerate(topics_to_process):
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logging.info(f"处理第 {i+1}/{len(topics_to_process)} 篇文章")
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# 为每个选题生成多个变体
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for j in range(variants):
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logging.info(f"正在生成变体 {j+1}/{variants}")
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# 调用单篇文章生成函数
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tweet_content, result = generate_single_content(
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ai_agent, system_prompt, item, run_id, i+1, j+1, temperature
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)
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if tweet_content:
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processed_results.append(tweet_content)
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# # 更新汇总文件 (仅保存第一个变体到汇总文件)
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# if j == 0:
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# ResourceLoader.update_summary(summary_file, i+1, user_prompt, result)
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logging.info(f"完成{len(processed_results)}篇文章生成")
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return processed_results
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def prepare_topic_generation(prompt_manager: PromptManager,
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api_url: str,
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model_name: str,
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api_key: str,
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timeout: int,
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max_retries: int):
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"""准备选题生成的环境和参数. Returns agent, system_prompt, user_prompt.
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Args:
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prompt_manager: An initialized PromptManager instance.
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api_url, model_name, api_key, timeout, max_retries: Parameters for AI_Agent.
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"""
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logging.info("Preparing for topic generation (using provided PromptManager)...")
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# 从传入的 PromptManager 获取 prompts
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system_prompt, user_prompt = prompt_manager.get_topic_prompts()
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if not system_prompt or not user_prompt:
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logging.error("Failed to get topic generation prompts from PromptManager.")
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return None, None, None # Return three Nones
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# 使用传入的参数初始化 AI Agent
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try:
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logging.info("Initializing AI Agent for topic generation...")
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ai_agent = AI_Agent(
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api_url, # Use passed arg
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model_name, # Use passed arg
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api_key, # Use passed arg
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timeout=timeout, # Use passed arg
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max_retries=max_retries # Use passed arg
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)
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except Exception as e:
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logging.exception("Error initializing AI Agent for topic generation:")
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return None, None, None # Return three Nones
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# 返回 agent 和 prompts
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return ai_agent, system_prompt, user_prompt
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def run_topic_generation_pipeline(config, run_id=None):
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"""
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Runs the complete topic generation pipeline based on the configuration.
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Returns: (run_id, topics_list, system_prompt, user_prompt) or (None, None, None, None) on failure.
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"""
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logging.info("Starting Step 1: Topic Generation Pipeline...")
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if run_id is None:
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logging.info("No run_id provided, generating one based on timestamp.")
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run_id = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
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else:
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logging.info(f"Using provided run_id: {run_id}")
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ai_agent, system_prompt, user_prompt = None, None, None # Initialize
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topics_list = None
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prompt_manager = None # Initialize prompt_manager
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try:
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# --- 读取 PromptManager 所需参数 ---
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topic_sys_prompt_path = config.get("topic_system_prompt")
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topic_user_prompt_path = config.get("topic_user_prompt")
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content_sys_prompt_path = config.get("content_system_prompt") # 虽然这里不用，但 PromptManager 可能需要
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prompts_dir_path = config.get("prompts_dir")
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prompts_config = config.get("prompts_config") # 新增：获取prompts_config配置
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resource_config = config.get("resource_dir", [])
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topic_num = config.get("num", 1)
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topic_date = config.get("date", "")
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# --- 创建 PromptManager 实例 ---
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prompt_manager = PromptManager(
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topic_system_prompt_path=topic_sys_prompt_path,
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topic_user_prompt_path=topic_user_prompt_path,
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content_system_prompt_path=content_sys_prompt_path,
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prompts_dir=prompts_dir_path,
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prompts_config=prompts_config, # 新增：传入prompts_config配置
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resource_dir_config=resource_config,
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topic_gen_num=topic_num,
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topic_gen_date=topic_date
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)
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logging.info("PromptManager instance created.")
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# --- 读取 AI Agent 所需参数 ---
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ai_api_url = config.get("api_url")
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ai_model = config.get("model")
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ai_api_key = config.get("api_key")
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ai_timeout = config.get("request_timeout", 30)
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ai_max_retries = config.get("max_retries", 3)
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# 检查必需的 AI 参数是否存在
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if not all([ai_api_url, ai_model, ai_api_key]):
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raise ValueError("Missing required AI configuration (api_url, model, api_key) in config.")
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# --- 调用修改后的 prepare_topic_generation ---
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ai_agent, system_prompt, user_prompt = prepare_topic_generation(
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prompt_manager, # Pass instance
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ai_api_url,
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ai_model,
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ai_api_key,
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ai_timeout,
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ai_max_retries
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)
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if not ai_agent or not system_prompt or not user_prompt:
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raise ValueError("Failed to prepare topic generation (agent or prompts missing).")
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# --- Generate topics (保持不变) ---
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topics_list = generate_topics(
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ai_agent, system_prompt, user_prompt,
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run_id, # Pass run_id
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config.get("topic_temperature", 0.2),
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config.get("topic_top_p", 0.5),
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config.get("topic_presence_penalty", 1.5)
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)
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except Exception as e:
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logging.exception("Error during topic generation pipeline execution:")
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# Ensure agent is closed even if generation fails mid-way
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if ai_agent: ai_agent.close()
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return None, None, None, None # Signal failure
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finally:
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# Ensure the AI agent is closed after generation attempt (if initialized)
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if ai_agent:
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logging.info("Closing topic generation AI Agent...")
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ai_agent.close()
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if topics_list is None: # Check if generate_topics returned None (though it currently returns list)
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logging.error("Topic generation failed (generate_topics returned None or an error occurred).")
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return None, None, None, None
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elif not topics_list: # Check if list is empty
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logging.warning(f"Topic generation completed for run {run_id}, but the resulting topic list is empty.")
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# Return empty list and prompts anyway, let caller decide
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# --- Saving logic removed previously ---
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logging.info(f"Topic generation pipeline completed successfully. Run ID: {run_id}")
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# Return the raw data needed by the OutputHandler
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return run_id, topics_list, system_prompt, user_prompt
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# --- Decoupled Functional Units (Moved from main.py) ---
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def generate_content_for_topic(ai_agent: AI_Agent,
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prompt_manager: PromptManager,
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topic_item: dict,
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run_id: str,
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topic_index: int,
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output_handler: OutputHandler, # Changed name to match convention
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# 添加具体参数，移除 config 和 output_dir
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variants: int,
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temperature: float,
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top_p: float,
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presence_penalty: float,
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enable_content_judge: bool):
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"""Generates all content variants for a single topic item and uses OutputHandler.
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Args:
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ai_agent: Initialized AI_Agent instance.
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prompt_manager: Initialized PromptManager instance.
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topic_item: Dictionary representing the topic.
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run_id: Current run ID.
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topic_index: 1-based index of the topic.
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output_handler: An instance of OutputHandler to process results.
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variants: Number of variants to generate.
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temperature, top_p, presence_penalty: AI generation parameters.
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enable_content_judge: Whether to enable content judge.
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Returns:
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bool: True if at least one variant was successfully generated and handled, False otherwise.
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"""
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logging.info(f"Generating content for Topic {topic_index} (Object: {topic_item.get('object', 'N/A')})...")
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success_flag = False # Track if any variant succeeded
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# 使用传入的 variants 参数
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# variants = config.get("variants", 1)
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# 如果启用了内容审核，获取产品资料
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product_info = None
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content_judger = None
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if enable_content_judge:
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logging.info(f"内容审核功能已启用，准备获取产品资料...")
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# 从topic_item中获取产品名称和对象名称
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product_name = topic_item.get("product", "")
|
||
object_name = topic_item.get("object", "")
|
||
|
||
# 组合获取产品资料
|
||
product_info = ""
|
||
|
||
# 获取对象信息
|
||
if object_name:
|
||
# 通过PromptManager获取对象和产品资料
|
||
# 这一部分逻辑来自PromptManager.get_content_prompts中对object_info的构建
|
||
found_object_info = False
|
||
all_description_files = []
|
||
|
||
# 从resource_dir_config搜集所有可能的资源文件
|
||
for dir_info in prompt_manager.resource_dir_config:
|
||
if dir_info.get("type") in ["Object", "Description"]:
|
||
all_description_files.extend(dir_info.get("file_path", []))
|
||
|
||
# 尝试精确匹配对象资料
|
||
for file_path in all_description_files:
|
||
if object_name in os.path.basename(file_path):
|
||
from utils.resource_loader import ResourceLoader
|
||
info = ResourceLoader.load_file_content(file_path)
|
||
if info:
|
||
product_info += f"Object: {object_name}\n{info}\n\n"
|
||
logging.info(f"为内容审核找到对象'{object_name}'的资源文件: {file_path}")
|
||
found_object_info = True
|
||
break
|
||
|
||
# 如果未找到对象资料，记录警告但继续处理
|
||
if not found_object_info:
|
||
logging.warning(f"未能为内容审核找到对象'{object_name}'的资源文件")
|
||
|
||
# 获取产品信息
|
||
if product_name:
|
||
found_product_info = False
|
||
all_product_files = []
|
||
|
||
# 搜集所有可能的产品资源文件
|
||
for dir_info in prompt_manager.resource_dir_config:
|
||
if dir_info.get("type") == "Product":
|
||
all_product_files.extend(dir_info.get("file_path", []))
|
||
|
||
# 尝试精确匹配产品资料
|
||
for file_path in all_product_files:
|
||
if product_name in os.path.basename(file_path):
|
||
from utils.resource_loader import ResourceLoader
|
||
info = ResourceLoader.load_file_content(file_path)
|
||
if info:
|
||
product_info += f"Product: {product_name}\n{info}\n\n"
|
||
logging.info(f"为内容审核找到产品'{product_name}'的资源文件: {file_path}")
|
||
found_product_info = True
|
||
break
|
||
|
||
# 如果未找到产品资料，记录警告但继续处理
|
||
if not found_product_info:
|
||
logging.warning(f"未能为内容审核找到产品'{product_name}'的资源文件")
|
||
|
||
# 如果成功获取产品资料，初始化ContentJudger
|
||
if product_info:
|
||
logging.info("成功获取产品资料，初始化ContentJudger...")
|
||
# 从配置中读取系统提示词路径（脚本级别无法直接获取，需要传递）
|
||
# 使用ai_agent的model_name或api_url判断是否使用主AI模型，避免额外资源占用
|
||
content_judger_system_prompt = prompt_manager._system_prompt_cache.get("judger_system_prompt")
|
||
content_judger = ContentJudger(ai_agent, system_prompt=content_judger_system_prompt)
|
||
else:
|
||
logging.warning("未能获取产品资料，内容审核功能将被跳过")
|
||
enable_content_judge = False
|
||
|
||
for j in range(variants):
|
||
variant_index = j + 1
|
||
logging.info(f" Generating Variant {variant_index}/{variants}...")
|
||
|
||
# PromptManager 实例已传入，直接调用
|
||
content_system_prompt, content_user_prompt = prompt_manager.get_content_prompts(topic_item)
|
||
|
||
if not content_system_prompt or not content_user_prompt:
|
||
logging.warning(f" Skipping Variant {variant_index} due to missing content prompts.")
|
||
continue
|
||
|
||
time.sleep(random.random() * 0.5)
|
||
try:
|
||
# Call generate_single_content with passed-in parameters
|
||
content_json, prompt_data = generate_single_content(
|
||
ai_agent, content_system_prompt, content_user_prompt, topic_item,
|
||
run_id, topic_index, variant_index,
|
||
temperature, # 使用传入的参数
|
||
top_p, # 使用传入的参数
|
||
presence_penalty # 使用传入的参数
|
||
)
|
||
|
||
# 简化检查，只要content_json不是None就处理它
|
||
# 即使是空标题和内容也是有效的结果
|
||
if content_json is not None:
|
||
# 进行内容审核（如果启用且ContentJudger已初始化）
|
||
if enable_content_judge and content_judger and product_info:
|
||
logging.info(f" 对Topic {topic_index}, Variant {variant_index}进行内容审核...")
|
||
|
||
# 准备审核内容
|
||
content_to_judge = f"""title: {content_json.get('title', '')}
|
||
|
||
content: {content_json.get('content', '')}
|
||
"""
|
||
|
||
# 调用ContentJudger进行审核
|
||
try:
|
||
judged_result = content_judger.judge_content(product_info, content_to_judge)
|
||
if judged_result and isinstance(judged_result, dict):
|
||
if "title" in judged_result and "content" in judged_result:
|
||
# 保存原始标题和内容
|
||
content_json["original_title"] = content_json.get("title", "")
|
||
content_json["original_content"] = content_json.get("content", "")
|
||
# 保存原始标签（优先使用tags，如果没有则使用tag）
|
||
original_tags = content_json.get("tags", content_json.get("tag", ""))
|
||
content_json["original_tags"] = original_tags
|
||
# 更新为审核后的内容
|
||
content_json["title"] = judged_result["title"]
|
||
content_json["content"] = judged_result["content"]
|
||
# 保留原始标签，避免重复
|
||
content_json["tags"] = original_tags
|
||
# 删除可能存在的重复tag字段
|
||
if "tag" in content_json:
|
||
del content_json["tag"]
|
||
# 添加审核标记
|
||
content_json["judged"] = True
|
||
# 添加judge_success状态
|
||
content_json["judge_success"] = judged_result.get("judge_success", False)
|
||
# 处理分析结果，优先使用"analysis"字段，兼容"不良内容分析"字段
|
||
if "analysis" in judged_result:
|
||
content_json["judge_analysis"] = judged_result["analysis"]
|
||
else:
|
||
logging.warning(f" 审核结果缺少title或content字段，保留原内容")
|
||
content_json["judge_success"] = False
|
||
else:
|
||
logging.warning(f" 内容审核返回无效结果，保留原内容")
|
||
content_json["judge_success"] = False
|
||
except Exception as judge_err:
|
||
logging.exception(f" 内容审核过程出错: {judge_err}，保留原内容")
|
||
content_json["judge_success"] = False
|
||
else:
|
||
# 未启用内容审核时，添加相应标记
|
||
content_json["judged"] = False
|
||
content_json["judge_success"] = None
|
||
|
||
# Use the output handler to process/save the result
|
||
output_handler.handle_content_variant(
|
||
run_id, topic_index, variant_index, content_json, prompt_data or ""
|
||
)
|
||
success_flag = True # Mark success for this topic
|
||
|
||
# Check specifically if the AI result itself indicated a parsing error internally
|
||
if content_json.get("error"):
|
||
logging.warning(f" Content generation for Topic {topic_index}, Variant {variant_index} succeeded but response parsing had issues. Using empty content values.")
|
||
else:
|
||
logging.info(f" Successfully generated and handled content for Topic {topic_index}, Variant {variant_index}.")
|
||
else:
|
||
logging.error(f" Content generation failed for Topic {topic_index}, Variant {variant_index}. Skipping handling.")
|
||
|
||
except Exception as e:
|
||
logging.exception(f" Error during content generation call or handling for Topic {topic_index}, Variant {variant_index}:")
|
||
|
||
# Return the success flag for this topic
|
||
return success_flag
|
||
|
||
def generate_posters_for_topic(topic_item: dict,
|
||
output_dir: str,
|
||
run_id: str,
|
||
topic_index: int,
|
||
output_handler: OutputHandler, # 添加 handler
|
||
variants: int,
|
||
model_name: str,
|
||
base_url: str,
|
||
api_key: str,
|
||
poster_assets_base_dir: str,
|
||
image_base_dir: str,
|
||
img_frame_possibility: float,
|
||
text_bg_possibility: float,
|
||
title_possibility: float,
|
||
text_possibility: float,
|
||
resource_dir_config: list,
|
||
poster_target_size: tuple,
|
||
output_collage_subdir: str,
|
||
output_poster_subdir: str,
|
||
output_poster_filename: str,
|
||
system_prompt: str,
|
||
collage_style: str,
|
||
timeout: int
|
||
):
|
||
"""Generates all posters for a single topic item, handling image data via OutputHandler.
|
||
|
||
Args:
|
||
topic_item: The dictionary representing a single topic.
|
||
output_dir: The base output directory for the entire run (e.g., ./result).
|
||
run_id: The ID for the current run.
|
||
topic_index: The 1-based index of the current topic.
|
||
variants: Number of variants.
|
||
poster_assets_base_dir: Path to poster assets (fonts, frames etc.).
|
||
image_base_dir: Base path for source images.
|
||
img_frame_possibility: Probability of adding image frame.
|
||
text_bg_possibility: Probability of adding text background.
|
||
resource_dir_config: Configuration for resource directories (used for Description).
|
||
poster_target_size: Target size tuple (width, height) for the poster.
|
||
text_possibility: Probability of adding secondary text.
|
||
output_collage_subdir: Subdirectory name for saving collages.
|
||
output_poster_subdir: Subdirectory name for saving posters.
|
||
output_poster_filename: Filename for the final poster.
|
||
system_prompt: System prompt for content generation.
|
||
output_handler: An instance of OutputHandler to process results.
|
||
timeout: Timeout for content generation.
|
||
Returns:
|
||
True if poster generation was attempted (regardless of individual variant success),
|
||
False if setup failed before attempting variants.
|
||
"""
|
||
logging.info(f"Generating posters for Topic {topic_index} (Object: {topic_item.get('object', 'N/A')})...")
|
||
|
||
# --- Load content data from files ---
|
||
loaded_content_list = []
|
||
logging.info(f"Attempting to load content data for {variants} variants for topic {topic_index}...")
|
||
for j in range(variants):
|
||
variant_index = j + 1
|
||
variant_dir = os.path.join(output_dir, run_id, f"{topic_index}_{variant_index}")
|
||
content_path = os.path.join(variant_dir, "article.json")
|
||
try:
|
||
if os.path.exists(content_path):
|
||
with open(content_path, 'r', encoding='utf-8') as f_content:
|
||
content_data = json.load(f_content)
|
||
|
||
# 支持Base64编码格式的文件
|
||
if 'title_base64' in content_data and 'content_base64' in content_data:
|
||
import base64
|
||
logging.info(f"检测到Base64编码的内容文件: {content_path}")
|
||
|
||
# 解码Base64内容
|
||
try:
|
||
title = base64.b64decode(content_data.get('title_base64', '')).decode('utf-8')
|
||
content = base64.b64decode(content_data.get('content_base64', '')).decode('utf-8')
|
||
|
||
# 创建包含解码内容的新数据对象
|
||
decoded_data = {
|
||
'title': title,
|
||
'content': content,
|
||
'judge_success': content_data.get('judge_success', True),
|
||
'judged': content_data.get('judged', True)
|
||
}
|
||
|
||
# 如果有标签，也解码
|
||
if 'tags_base64' in content_data:
|
||
tags = base64.b64decode(content_data.get('tags_base64', '')).decode('utf-8')
|
||
decoded_data['tags'] = tags
|
||
|
||
loaded_content_list.append(decoded_data)
|
||
logging.debug(f" 已成功解码并加载Base64内容: {content_path}")
|
||
continue
|
||
except Exception as decode_error:
|
||
logging.error(f" 解码Base64内容时出错: {decode_error}，跳过此文件")
|
||
continue
|
||
|
||
# 常规JSON格式检查
|
||
if isinstance(content_data, dict) and 'title' in content_data and 'content' in content_data:
|
||
loaded_content_list.append(content_data)
|
||
logging.debug(f" Successfully loaded content from: {content_path}")
|
||
else:
|
||
logging.warning(f" Content file {content_path} has invalid format. Skipping.")
|
||
else:
|
||
logging.warning(f" Content file not found for variant {variant_index}: {content_path}. Skipping.")
|
||
except json.JSONDecodeError:
|
||
logging.error(f" Error decoding JSON from content file: {content_path}. Skipping.")
|
||
except Exception as e:
|
||
logging.exception(f" Error loading content file {content_path}: {e}")
|
||
|
||
if not loaded_content_list:
|
||
logging.error(f"No valid content data loaded for topic {topic_index}. Cannot generate posters.")
|
||
return False
|
||
logging.info(f"Successfully loaded content data for {len(loaded_content_list)} variants.")
|
||
# --- End Load content data ---
|
||
|
||
# Initialize generators using parameters
|
||
try:
|
||
content_gen_instance = core_contentGen.ContentGenerator(model_name=model_name, base_url=base_url, api_key=api_key)
|
||
if not poster_assets_base_dir:
|
||
logging.error("Error: 'poster_assets_base_dir' not provided. Cannot generate posters.")
|
||
return False
|
||
poster_gen_instance = core_posterGen.PosterGenerator(base_dir=poster_assets_base_dir)
|
||
poster_gen_instance.set_img_frame_possibility(img_frame_possibility)
|
||
poster_gen_instance.set_text_bg_possibility(text_bg_possibility)
|
||
except Exception as e:
|
||
logging.exception("Error initializing generators for poster creation:")
|
||
return False
|
||
# --- Setup: Paths and Object Name ---
|
||
object_name = topic_item.get("object", "")
|
||
if not object_name:
|
||
logging.warning("Warning: Topic object name is missing. Cannot generate posters.")
|
||
return False
|
||
|
||
# Clean object name
|
||
try:
|
||
object_name_cleaned = object_name.split(".")[0].replace("景点信息-", "").strip()
|
||
if not object_name_cleaned:
|
||
logging.warning(f"Warning: Object name '{object_name}' resulted in empty string after cleaning. Skipping posters.")
|
||
return False
|
||
object_name = object_name_cleaned
|
||
except Exception as e:
|
||
logging.warning(f"Warning: Could not fully clean object name '{object_name}': {e}. Skipping posters.")
|
||
return False
|
||
|
||
# Construct and check INPUT image paths
|
||
input_img_dir_path = os.path.join(image_base_dir, object_name)
|
||
if not os.path.exists(input_img_dir_path) or not os.path.isdir(input_img_dir_path):
|
||
# 模糊匹配：如果找不到完全匹配的目录，尝试查找包含关键词的目录
|
||
logging.info(f"尝试对图片目录进行模糊匹配: {object_name}")
|
||
found_dir = None
|
||
|
||
# 1. 尝试获取image_base_dir下的所有目录
|
||
try:
|
||
all_dirs = [d for d in os.listdir(image_base_dir)
|
||
if os.path.isdir(os.path.join(image_base_dir, d))]
|
||
logging.info(f"找到 {len(all_dirs)} 个图片目录可用于模糊匹配")
|
||
|
||
# 2. 提取对象名称中的关键词
|
||
# 例如："美的鹭湖鹭栖台酒店+盈香心动乐园" -> ["美的", "鹭湖", "酒店", "乐园"]
|
||
# 首先通过常见分隔符分割（+、空格、_、-等）
|
||
parts = re.split(r'[+\s_\-]', object_name)
|
||
keywords = []
|
||
for part in parts:
|
||
# 只保留长度大于1的有意义关键词
|
||
if len(part) > 1:
|
||
keywords.append(part)
|
||
|
||
# 尝试匹配更短的语义单元（例如中文的2-3个字的词语）
|
||
# 对于中文名称，可以尝试提取2-3个字的短语
|
||
for i in range(len(object_name) - 1):
|
||
keyword = object_name[i:i+2] # 提取2个字符
|
||
if len(keyword) == 2 and all('\u4e00' <= c <= '\u9fff' for c in keyword):
|
||
keywords.append(keyword)
|
||
|
||
# 3. 对每个目录进行评分
|
||
dir_scores = {}
|
||
for directory in all_dirs:
|
||
score = 0
|
||
dir_lower = directory.lower()
|
||
# 为每个匹配的关键词增加分数
|
||
for keyword in keywords:
|
||
if keyword.lower() in dir_lower:
|
||
score += 1
|
||
|
||
# 如果得分大于0（至少匹配一个关键词），记录该目录
|
||
if score > 0:
|
||
dir_scores[directory] = score
|
||
|
||
# 4. 选择得分最高的目录
|
||
if dir_scores:
|
||
best_match = max(dir_scores.items(), key=lambda x: x[1])
|
||
found_dir = best_match[0]
|
||
logging.info(f"模糊匹配成功！匹配目录: {found_dir}，匹配分数: {best_match[1]}")
|
||
|
||
# 更新图片目录路径
|
||
input_img_dir_path = os.path.join(image_base_dir, found_dir)
|
||
logging.info(f"使用模糊匹配的图片目录: {input_img_dir_path}")
|
||
else:
|
||
logging.warning(f"模糊匹配未找到任何包含关键词的目录")
|
||
except Exception as e:
|
||
logging.warning(f"模糊匹配过程中出错: {e}")
|
||
|
||
# 如果仍然无法找到有效目录，则返回错误
|
||
if not found_dir or not os.path.exists(input_img_dir_path) or not os.path.isdir(input_img_dir_path):
|
||
logging.warning(f"Warning: 即使通过模糊匹配也无法找到图片目录: '{input_img_dir_path}'. Skipping posters for this topic.")
|
||
return False
|
||
|
||
# Locate Description File using resource_dir_config parameter
|
||
info_directory = []
|
||
description_file_path = None
|
||
found_description = False
|
||
|
||
# 准备关键词列表用于模糊匹配
|
||
# 与上面图片目录匹配类似，提取对象名称的关键词
|
||
parts = re.split(r'[+\s_\-]', object_name)
|
||
keywords = []
|
||
for part in parts:
|
||
if len(part) > 1:
|
||
keywords.append(part)
|
||
|
||
# 尝试提取中文短语作为关键词
|
||
for i in range(len(object_name) - 1):
|
||
keyword = object_name[i:i+2]
|
||
if len(keyword) == 2 and all('\u4e00' <= c <= '\u9fff' for c in keyword):
|
||
keywords.append(keyword)
|
||
|
||
logging.info(f"用于描述文件模糊匹配的关键词: {keywords}")
|
||
|
||
# 尝试精确匹配
|
||
for dir_info in resource_dir_config:
|
||
if dir_info.get("type") == "Description":
|
||
for file_path in dir_info.get("file_path", []):
|
||
if object_name in os.path.basename(file_path):
|
||
description_file_path = file_path
|
||
if os.path.exists(description_file_path):
|
||
info_directory = [description_file_path]
|
||
logging.info(f"找到并使用精确匹配的描述文件: {description_file_path}")
|
||
found_description = True
|
||
else:
|
||
logging.warning(f"Warning: 配置中指定的描述文件未找到: {description_file_path}")
|
||
break
|
||
if found_description:
|
||
break
|
||
|
||
# 如果精确匹配失败，尝试模糊匹配
|
||
if not found_description:
|
||
logging.info(f"未找到'{object_name}'的精确匹配描述文件，尝试模糊匹配...")
|
||
best_score = 0
|
||
best_file = None
|
||
|
||
for dir_info in resource_dir_config:
|
||
if dir_info.get("type") == "Description":
|
||
for file_path in dir_info.get("file_path", []):
|
||
file_name = os.path.basename(file_path)
|
||
score = 0
|
||
|
||
# 计算关键词匹配分数
|
||
for keyword in keywords:
|
||
if keyword.lower() in file_name.lower():
|
||
score += 1
|
||
|
||
# 如果当前文件得分更高，更新最佳匹配
|
||
if score > best_score and os.path.exists(file_path):
|
||
best_score = score
|
||
best_file = file_path
|
||
|
||
# 如果找到了最佳匹配文件
|
||
if best_file:
|
||
description_file_path = best_file
|
||
info_directory = [description_file_path]
|
||
logging.info(f"模糊匹配找到描述文件: {description_file_path}，匹配分数: {best_score}")
|
||
found_description = True
|
||
|
||
if not found_description:
|
||
logging.warning(f"未找到对象'{object_name}'的匹配描述文件。")
|
||
|
||
# Generate Text Configurations for All Variants
|
||
try:
|
||
poster_text_configs_raw = content_gen_instance.run(info_directory, variants, loaded_content_list, system_prompt, timeout=timeout)
|
||
if not poster_text_configs_raw:
|
||
logging.warning("Warning: ContentGenerator returned empty configuration data. Skipping posters.")
|
||
return False
|
||
|
||
# --- 使用 OutputHandler 保存 Poster Config ---
|
||
output_handler.handle_poster_configs(run_id, topic_index, poster_text_configs_raw)
|
||
# --- 结束使用 Handler 保存 ---
|
||
|
||
# 打印原始配置数据以进行调试
|
||
logging.info(f"生成的海报配置数据: {poster_text_configs_raw}")
|
||
|
||
# 直接使用配置数据，避免通过文件读取
|
||
if isinstance(poster_text_configs_raw, list):
|
||
poster_configs = poster_text_configs_raw
|
||
logging.info(f"直接使用生成的配置列表，包含 {len(poster_configs)} 个配置项")
|
||
else:
|
||
# 如果不是列表，尝试转换或使用PosterConfig类解析
|
||
logging.info("生成的配置数据不是列表，使用PosterConfig类进行处理")
|
||
poster_config_summary = core_posterGen.PosterConfig(poster_text_configs_raw)
|
||
poster_configs = poster_config_summary.get_config()
|
||
except Exception as e:
|
||
logging.exception("Error running ContentGenerator or parsing poster configs:")
|
||
traceback.print_exc()
|
||
return False
|
||
|
||
# Poster Generation Loop for each variant
|
||
poster_num = min(variants, len(poster_configs)) if isinstance(poster_configs, list) else variants
|
||
logging.info(f"计划生成 {poster_num} 个海报变体")
|
||
any_poster_attempted = False
|
||
|
||
for j_index in range(poster_num):
|
||
variant_index = j_index + 1
|
||
logging.info(f"Generating Poster {variant_index}/{poster_num}...")
|
||
any_poster_attempted = True
|
||
collage_img = None # To store the generated collage PIL Image
|
||
poster_img = None # To store the final poster PIL Image
|
||
try:
|
||
# 获取当前变体的配置
|
||
if isinstance(poster_configs, list) and j_index < len(poster_configs):
|
||
poster_config = poster_configs[j_index]
|
||
logging.info(f"使用配置数据项 {j_index+1}: {poster_config}")
|
||
else:
|
||
# 回退方案：使用PosterConfig类
|
||
poster_config = poster_config_summary.get_config_by_index(j_index)
|
||
logging.info(f"使用PosterConfig类获取配置项 {j_index+1}")
|
||
|
||
if not poster_config:
|
||
logging.warning(f"Warning: Could not get poster config for index {j_index}. Skipping.")
|
||
continue
|
||
|
||
# --- Image Collage ---
|
||
logging.info(f"Generating collage from: {input_img_dir_path}")
|
||
collage_images, used_image_filenames = core_simple_collage.process_directory(
|
||
input_img_dir_path,
|
||
style=collage_style,
|
||
target_size=poster_target_size,
|
||
output_count=1
|
||
)
|
||
|
||
if not collage_images: # 检查列表是否为空
|
||
logging.warning(f"Warning: Failed to generate collage image for Variant {variant_index}. Skipping poster.")
|
||
continue
|
||
collage_img = collage_images[0] # 获取第一个 PIL Image
|
||
used_image_files = used_image_filenames[0] if used_image_filenames else [] # 获取使用的图片文件名
|
||
logging.info(f"Collage image generated successfully (in memory). Used images: {used_image_files}")
|
||
logging.info(f"拼贴图使用的图片文件: {used_image_files}")
|
||
|
||
# --- 使用 Handler 保存 Collage 图片和使用的图片文件信息 ---
|
||
output_handler.handle_generated_image(
|
||
run_id, topic_index, variant_index,
|
||
image_type='collage',
|
||
image_data=collage_img,
|
||
output_filename='collage.png', # 或者其他期望的文件名
|
||
metadata={'used_images': used_image_files} # 添加图片文件信息到元数据
|
||
)
|
||
# --- 结束保存 Collage ---
|
||
|
||
# --- Create Poster ---
|
||
if random.random() > title_possibility:
|
||
text_data = {
|
||
"title": poster_config.get('main_title', ''),
|
||
"subtitle": "",
|
||
"additional_texts": []
|
||
}
|
||
texts = poster_config.get('texts', [])
|
||
if texts:
|
||
# 确保文本不为空
|
||
if random.random() > text_possibility:
|
||
text_data["additional_texts"].append({"text": texts[0], "position": "middle", "size_factor": 0.8})
|
||
# for text in texts:
|
||
# if random.random() < text_possibility:
|
||
# text_data["additional_texts"].append({"text": text, "position": "middle", "size_factor": 0.8})
|
||
else:
|
||
text_data = {
|
||
"title": "",
|
||
"subtitle": "",
|
||
"additional_texts": []
|
||
}
|
||
# 打印要发送的文本数据
|
||
logging.info(f"文本数据: {text_data}")
|
||
|
||
# 调用修改后的 create_poster, 接收 PIL Image
|
||
poster_img = poster_gen_instance.create_poster(collage_img, text_data)
|
||
|
||
if poster_img:
|
||
logging.info(f"Poster image generated successfully (in memory).")
|
||
# --- 使用 Handler 保存 Poster 图片 ---
|
||
output_handler.handle_generated_image(
|
||
run_id, topic_index, variant_index,
|
||
image_type='poster',
|
||
image_data=poster_img,
|
||
output_filename=output_poster_filename, # 使用参数中的文件名
|
||
metadata={'used_collage': True, 'collage_images': used_image_files}
|
||
)
|
||
# --- 结束保存 Poster ---
|
||
else:
|
||
logging.warning(f"Warning: Poster generation function returned None for variant {variant_index}.")
|
||
|
||
except Exception as e:
|
||
logging.exception(f"Error during poster generation for Variant {variant_index}:")
|
||
traceback.print_exc()
|
||
continue
|
||
|
||
return any_poster_attempted |