File size: 6,391 Bytes
d13a83d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 | from __future__ import annotations
import hashlib
import json
from typing import Any
from actor_contract import (
LEGACY_ACTOR_SCHEMA,
REASONING_EVIDENCE_SOLUTION_RATING_ACTOR_SCHEMA,
REASONS_RATING_ACTOR_SCHEMA,
REASONING_FIELDS,
active_top_level_fields,
actor_schema,
)
ACTOR_SCHEMA = actor_schema()
ACTOR_TOP_LEVEL_FIELDS = active_top_level_fields()
ENABLE_THINKING = False
ADD_NON_THINKING_PREFIX = False
SYSTEM_PROMPT = (
"You are a helpful assistant. When the user asks a question, respond with exactly one valid JSON object and no other text."
)
if ACTOR_SCHEMA == REASONING_EVIDENCE_SOLUTION_RATING_ACTOR_SCHEMA:
PROMPT_VERSION = "vf_reasoning_evidence_solution_rating_v5_20260724"
USER_PROMPT_TEXT = (
"Assess the overall perceptual quality of this image.\n\n"
"Respond with exactly one JSON object containing these keys in this order: \"reasoning\" and \"rating\". "
"\"reasoning\" must be one JSON object containing these keys in this order: \"evidence\" and \"solution\".\n\n"
"\"evidence\" must be one concise string grounded in the visible image evidence that determines its current "
"overall perceptual quality, and must indicate where that evidence appears in the image.\n"
"\"solution\" must be one concise string containing a coherent image-edit plan that causally addresses the "
"evidence. The edited result must retain the same semantic meaning as the input image. If the image is already "
"high quality, request only a minimal preservation-first refinement without inventing a defect.\n"
"\"rating\" must be a numeric string from 1.00 to 5.00 with exactly two decimal places. \"1.00\" represents "
"the worst quality and \"5.00\" represents excellent quality."
)
elif ACTOR_SCHEMA == REASONS_RATING_ACTOR_SCHEMA:
PROMPT_VERSION = "vf_reasons_rating_qwen3vl_v1_20260716"
USER_PROMPT_TEXT = (
"Please assess the overall perceptual quality of this image and provide a quality rating written with "
"exactly two decimal places.\n\n"
"Respond with exactly one JSON object containing these keys in this order: \"reasons\" and \"rating\".\n\n"
"\"reasons\" must be one concise string that first describes the visible evidence affecting image quality "
"and then, when meaningful, gives one specific action for improving the image. If no meaningful improvement "
"is needed, state that no correction is necessary.\n"
"\"rating\" must be a finite number or numeric string from 1.00 to 5.00. \"1.00\" represents the worst "
"quality and \"5.00\" represents excellent quality."
)
else:
PROMPT_VERSION = "vf_reason_rating_suggestion_qwen3vl_v1_20260716"
USER_PROMPT_TEXT = (
"Please assess the overall perceptual quality of this image. Explain the visible reasons affecting its quality, "
"provide a quality rating written with exactly two decimal places, and give one specific suggestion for reducing "
"the negative factors affecting the image.\n\n"
"Respond with exactly one JSON object containing these keys in this order: \"reason\", \"rating\", and \"suggestion\".\n\n"
"\"reason\" must be a string describing the visible reasons that affect the image quality.\n"
"\"rating\" must be a finite number or numeric string from 1.00 to 5.00. \"1.00\" represents the worst quality "
"and \"5.00\" represents excellent quality.\n"
"\"suggestion\" must be a string containing a specific image-improvement instruction. If the image has no "
"meaningful room for improvement, use an empty string."
)
TRAINING_USER_PROMPT = f"<image>{USER_PROMPT_TEXT}"
def _sha256(value: str) -> str:
return hashlib.sha256(value.encode("utf-8")).hexdigest()
def _contract_payload() -> dict[str, Any]:
payload = {
"prompt_version": PROMPT_VERSION,
"system_prompt": SYSTEM_PROMPT,
"user_prompt_text": USER_PROMPT_TEXT,
"enable_thinking": ENABLE_THINKING,
"add_non_thinking_prefix": ADD_NON_THINKING_PREFIX,
}
if ACTOR_SCHEMA != LEGACY_ACTOR_SCHEMA:
payload["actor_schema"] = ACTOR_SCHEMA
payload["top_level_fields"] = list(ACTOR_TOP_LEVEL_FIELDS)
if ACTOR_SCHEMA == REASONING_EVIDENCE_SOLUTION_RATING_ACTOR_SCHEMA:
payload["reasoning_fields"] = list(REASONING_FIELDS)
payload["editor_contract_version"] = "same_semantics_same_size_v2_20260724"
return payload
PROMPT_HASH = _sha256(json.dumps(_contract_payload(), ensure_ascii=False, sort_keys=True, separators=(",", ":")))
SYSTEM_PROMPT_HASH = _sha256(SYSTEM_PROMPT)
USER_PROMPT_HASH = _sha256(USER_PROMPT_TEXT)
def prompt_metadata() -> dict[str, Any]:
metadata = {
"prompt_version": PROMPT_VERSION,
"prompt_hash": PROMPT_HASH,
"system_prompt_hash": SYSTEM_PROMPT_HASH,
"user_prompt_hash": USER_PROMPT_HASH,
"enable_thinking": ENABLE_THINKING,
"add_non_thinking_prefix": ADD_NON_THINKING_PREFIX,
}
if ACTOR_SCHEMA != LEGACY_ACTOR_SCHEMA:
metadata["actor_schema"] = ACTOR_SCHEMA
metadata["top_level_fields"] = list(ACTOR_TOP_LEVEL_FIELDS)
if ACTOR_SCHEMA == REASONING_EVIDENCE_SOLUTION_RATING_ACTOR_SCHEMA:
metadata["reasoning_fields"] = list(REASONING_FIELDS)
metadata["editor_contract_version"] = "same_semantics_same_size_v2_20260724"
return metadata
def validate_image_binding(user_prompt: str, images: list[Any]) -> None:
tag_count = str(user_prompt).count("<image>")
image_count = len(images)
if tag_count != image_count:
raise ValueError(f"image placeholder mismatch: tags={tag_count}, images={image_count}")
def build_training_messages(images: list[Any]) -> list[dict[str, str]]:
validate_image_binding(TRAINING_USER_PROMPT, images)
return [
{"role": "system", "content": SYSTEM_PROMPT},
{"role": "user", "content": TRAINING_USER_PROMPT},
]
def build_structured_validation_messages(image_path: str) -> list[dict[str, Any]]:
return [
{"role": "system", "content": SYSTEM_PROMPT},
{
"role": "user",
"content": [
{"type": "image", "image": image_path},
{"type": "text", "text": USER_PROMPT_TEXT},
],
},
]
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