src/llm_generation/generator.py

import os
import re
import copy
import logging
import traceback
from tqdm import tqdm
from tenacity import (
    retry, 
    stop_after_attempt, 
    wait_random_exponential, 
    retry_if_exception_type,
    before_sleep_log
)
from src.utils.image_utils import parse_multimodal_text, parse_multimodal_text_for_vllm

# 导入 Prompts
from src.llm_generation.prompts import (
    TASK1_SYSTEM_PROMPT, TASK1_STEP_GENERATION_PROMPT, TASK1_FINAL_VERIFICATION_PROMPT,
    TASK3_SYSTEM_PROMPT, TASK3_STEP_GENERATION_PROMPT, TASK3_FINAL_VERIFICATION_PROMPT
)

# 设置 logging
logger = logging.getLogger(__name__)

class CoTGenerator:
    def __init__(self, client, image_root, model_name="gpt-4o"):
        self.client = client
        self.image_root = image_root
        self.model_name = model_name
        # 判断是否为 vllm，其和API采用两种图片加载方式
        self.is_vllm = "VLLMClient" in client.__class__.__name__
        
        self.prompt_map = {
            "camera_view_prediction": {
                "system": TASK1_SYSTEM_PROMPT,
                "step": TASK1_STEP_GENERATION_PROMPT,
                "final": TASK1_FINAL_VERIFICATION_PROMPT
            },
            "camera_view_ordering": {
                "system": TASK3_SYSTEM_PROMPT,
                "step": TASK3_STEP_GENERATION_PROMPT,
                "final": TASK3_FINAL_VERIFICATION_PROMPT
            }
        }

    def _get_content(self, text, img_map):
        """将 prompt 中的相对路径占位符替换为绝对路径格式"""
        def repl(match):
            content = match.group(1)
            rel_path = img_map.get(content, content)
            abs_path = os.path.join(self.image_root, rel_path)
            return f"<image_start>[{abs_path}]<image_end>"
        
        text_abs = re.sub(r"<image_start>\[(.*?)\]<image_end>", repl, text)
        
        if self.is_vllm:
            return parse_multimodal_text_for_vllm(text_abs)
        else:
            return parse_multimodal_text(text_abs)

    @retry(
        stop=stop_after_attempt(5), 
        wait=wait_random_exponential(min=1, max=60),
        before_sleep=before_sleep_log(logger, logging.WARNING),
        reraise=True
    )
    def _call_llm_with_retry(self, messages):
        return self.client.call_chat(messages, model=self.model_name)

    def process_single_entry(self, entry):
        # 确定 task 种类
        # task1: camera view prediction
        # task3: camera view ordering
        task_type = entry.get('task', 'camera_view_prediction')
        # 根据种类选择对应的 prompt
        prompts = self.prompt_map.get(task_type)
        if not prompts:
            raise ValueError(f"Unknown task type: {task_type}")

        meta = entry['oracle_meta']
        img_map = entry['images']
        chain = meta['chain']
      
        # 1. 初始化
        # system prompt: system prompt template + "The question is: {question}"
        history_messages = [{"role": "system", "content": prompts["system"]}]
        # 把 question 加入历史对话中
        question = self._get_content("**Question:\n**" + entry["question"], img_map)
        history_messages.append({"role": "user", "content": question})
      
        full_trace_parts = [] 
        cot_list = []
      
        current_angle_so_far = 0.0
      
        # 2. 循环生成 Chain (Think 1...N)
        for i, step in enumerate(chain):
            # 这一步的目标图 I_k
            next_key = step['result_image_key']
            step_action = step['action']
          
            # --- A. 准备 Context ---
            # 这里的 context 是：历史消息 + 这一轮的新指令
            current_turn_messages = copy.deepcopy(history_messages)
            # 如果是第二步以后，需要给模型上一步的结果；将上一步的结果作为 User 的新输入放进去
            if i > 0:
                # 拿到上一步的 image 占位符，如 reasoning_image_1
                prev_key = chain[i-1]['result_image_key']
                # 构造 text instruction
                # instruction: 这是上一步的结果，请继续
                step_instruction_text = f"Here is the result of the previous step: <image_start>[{prev_key}]<image_end>. Proceed to the next step."
                # 使用 _get_content 方法直接将图片嵌入，并得到 content
                step_content = self._get_content(step_instruction_text, img_map)
                
                current_turn_messages.append({"role": "user", "content": step_content})

            # --- B. 准备 Task 3 特有的匹配提示 ---
            # 默认值为空字符串 (适配 Task 1)
            match_status_hint_text = ""
            
            if task_type == "camera_view_ordering":
                is_current_view_match = False
                matched_label = None
                
                # Look Back 逻辑：检查“当前看到的图”（即上一步的结果）是否是 Key Frame
                if i == 0:
                    is_current_view_match = False
                else:
                    prev_step = chain[i-1]
                    if prev_step.get('is_key_frame'):
                        is_current_view_match = True
                        # 当前看到的图，对应的是要 order 的哪个 view
                        matched_label = prev_step['matched_display_label']
                
                # 提示模型，注意当前 view 与 matched_label 的图是 match 的
                if is_current_view_match:
                    match_status_hint_text = f"The view you are looking at RIGHT NOW corresponds to **Candidate Image {matched_label}**. Explicitly identify this match."
                else:
                    match_status_hint_text = "The view you are looking at RIGHT NOW is an intermediate transition. It does NOT match any candidate image."
                    
            current_step_direction = step_action.get('direction', '')
            if current_step_direction == 'anticlockwise':
                # 逆时针 = 摄像机向右移 = 画面向左移
                physics_rule_text = "Camera moves RIGHT -> View shifts LEFT. (Features on the Right move to Center; Center moves to Left)."
            elif current_step_direction == 'clockwise':
                # 顺时针 = 摄像机向左移 = 画面向右移
                physics_rule_text = "Camera moves LEFT -> View shifts RIGHT. (Features on the Left move to Center; Center moves to Right)."
            else:
                physics_rule_text = "Maintain current view."

            # --- C. 构造参数字典 ---
            step_format_kwargs = {
                # Task 1 & 3 通用
                "total_degrees": meta.get('angle_degrees', 0),
                "direction": meta.get('direction', step_action.get('direction', '')),
                "current_angle_so_far": current_angle_so_far,
                "step_degrees": step_action.get('degrees', 0),
                "step_direction": step_action.get('direction', ''),
                "physics_rule": physics_rule_text,
                "next_image_content": self._get_content(f"<image_start>[{next_key}]<image_end>", img_map),
              
                # Task 3 特有 (Task 1 会自动忽略这个多余参数)
                "match_status_hint": match_status_hint_text,
              
                # 辅助
                "next_image_key": img_map.get(next_key, next_key)
            }
          
            # --- D. 渲染 Prompt ---
            step_prompt_template = prompts["step"]
            cheat_prompt = step_prompt_template.format(**step_format_kwargs)
          
            current_turn_messages.append({"role": "user", "content": self._get_content(cheat_prompt, img_map)})
          
            # --- E. 调用模型 ---
            reasoning_text = self._call_llm_with_retry(current_turn_messages)
            cot_list.append(reasoning_text)
          
            think_block = f"<think>{reasoning_text}</think>"
            image_block = f"<image_start>[{next_key}]<image_end>"
          
            full_trace_parts.append(think_block)
            full_trace_parts.append(image_block)
          
            # --- F. 更新 History ---
            # TODO: assistant 历史中不放 image ，其已经保留在 user query 中
            assistant_content = f"{think_block}"
            history_messages.append({"role": "assistant", "content": assistant_content})
          
            current_angle_so_far = step_action.get('total_angle_so_far', 0.0)

        # 3. 最终验证
        current_turn_messages = copy.deepcopy(history_messages)
        final_key = chain[-1]['result_image_key']
      
        final_format_kwargs = {
            # common args
            "correct_label": meta.get('correct_label', ""), 
            # task1 args
            "total_degrees": meta.get('angle_degrees', 0),
            "final_img_abs_path": img_map.get(final_key, ""),
            "direction": meta.get("direction"),
            # task3 args
            "correct_sequence_str": meta.get('correct_sequence_str', "")
        }
      
        final_prompt = prompts["final"].format(**final_format_kwargs)
        current_turn_messages.append({"role": "user", "content": self._get_content(final_prompt, img_map)})
      
        final_response_text = self._call_llm_with_retry(current_turn_messages)
        cot_list.append(final_response_text)
      
        full_trace_parts.append(f"<think>{final_response_text}</think>")

        entry['reasoning'] = "".join(full_trace_parts)
        entry['oracle_meta']['cot'] = cot_list
      
        return entry



    def process_batch(self, entries):
        results = []
        logging.basicConfig(level=logging.INFO)
      
        for entry in tqdm(entries, desc="Generating CoT"):
            try:
                res = self.process_single_entry(entry)
                results.append(res)
            except Exception as e:
                logger.error(f"Failed to process entry {entry.get('id', 'unknown')} (Error: {e})")
                print("="*30 + " ERROR TRACEBACK " + "="*30)
                traceback.print_exc()
                print("="*77)
        return results