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from enum import Enum
class Mode(Enum):
OBJECT_ACTION_ALIGNMENT = 1
OBJECT_EXISTENCE = 2
OVERALL_CONSISTENCY = 3
SPATIAL_RELATIONSHIP = 4
mode_prompts = {
Mode.OBJECT_ACTION_ALIGNMENT: (
"\"object_action_alignment\":\n"
"Extract actions and their participating objects. Each proposition must describe an action and its related objects.\n"
"Example:\n"
"\"object_action_alignment\": [\"person holds hotdog\", \"person walks\"]"
),
Mode.OBJECT_EXISTENCE: (
"\"object_existence\":\n"
"Extract only the tangible objects mentioned in the prompt.\n"
"Example:\n"
"\"object_existence\": [\"person\", \"hotdog\", \"car\", \"truck\"]"
),
Mode.OVERALL_CONSISTENCY: (
"\"overall_consistency\":\n"
"Extract all meaningful event propositions that describe the combined semantics of objects, actions, and spatial relationships — "
"but avoid TL keywords such as 'and', 'or', 'not', 'until', 'eventually'.\n"
"Example:\n"
"\"overall_consistency\": [\"person holds hotdog\", \"person walks\", \"car next to truck\"]"
),
Mode.SPATIAL_RELATIONSHIP: (
"\"spatial_relationships\":\n"
"Extract only spatial relationships between tangible objects (e.g., \"object A next to object B\"). Do not infer or hallucinate spatial relationships.\n"
"Example:\n"
"\"spatial_relationships\": [\"car next to truck\"]"
)
}
mode_outputs = {
Mode.OBJECT_ACTION_ALIGNMENT: (
" \"object_action_alignment\": [...],\n"
" \"object_action_alignment_spec\": \"...\","
),
Mode.OBJECT_EXISTENCE: (
" \"object_existence\": [...],\n"
" \"object_existence_spec\": \"...\","
),
Mode.OVERALL_CONSISTENCY: (
" \"overall_consistency\": [...],\n"
" \"overall_consistency_spec\": \"...\","
),
Mode.SPATIAL_RELATIONSHIP: (
" \"spatial_relationships\": [...],\n"
" \"spatial_relationships_spec\": \"...\""
)
}
header = (
"You are an intelligent agent designed to extract structured representations from video description prompts. "
"You will operate in two stages: (1) proposition extraction and (2) TL specification generation.\n\n"
)
stage1_intro = (
"Stage 1: Proposition Extraction\n\n"
"Given an input prompt summarizing a video, extract atomic propositions in the following four modes. "
"Return all outputs in JSON format.\n\n"
)
stage2_intro = "Stage 2: TL Specification Generation\n\n"
spec_gen_intro = (
"For each of the {n} list(s) of propositions extracted in Stage 1, generate a separate Temporal Logic (TL) specification "
"describing the structure or sequence of events in that list.\n\n"
)
tl_instructions = (
"Rules for TL specification:\n"
"- The input is a single list of propositions from one of the extraction modes.\n"
"- The output is a single TL formula using **only** the propositions from that list and the allowed TL symbols: "
"['AND', 'OR', 'NOT', 'UNTIL', 'ALWAYS', 'EVENTUALLY']\n"
"- Do not introduce any new propositions.\n"
"- Each formula should reflect the temporal or logical relationships between the propositions in a way that makes semantic sense.\n\n"
)
input_template = "Input:\n{{\n \"prompt\": \"{}\"\n}}\n\n"
expected_output_header = "Expected Output:\n{\n"
expected_output_footer = "\n}"
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