src/labeling_logic.py

# labeling_logic.py
import os
import json

PROMPT_VARIANTS = {
    "standard": {
        "description": "Standard Persona",
        "instruction": "You are a Factuality Agent."
    },
    "skeptic": {
        "description": "Skeptical Persona",
        "instruction": "You are a highly skeptical Factuality Agent. Assume claims might be misleading until verified."
    },
    "lenient": {
        "description": "Lenient Persona",
        "instruction": "You are a lenient Factuality Agent. Assume claims are true unless overtly false."
    }
}

TEXT_ONLY_INSTRUCTIONS = """
**NOTE: No video provided.** Analyze the text only (caption and transcript). Do not make assumptions about visual elements unless described in the text. Visual and Audio integrity scores should be estimated based on textual claims if applicable, or defaulted if completely unknowable.
"""

def get_formatted_tag_list():
    path = "data/tags.json"
    if os.path.exists(path):
        try:
            with open(path, 'r') as f:
                tags = json.load(f)
                if tags:
                    return "Suggested tags: " + ", ".join(tags.keys())
        except Exception:
            pass
    return "Provide relevant comma-separated tags."

LABELING_PROMPT_TEMPLATE = """
{system_persona}

You are an AI Factuality Assessment Agent operating under the "Ali Arsanjani Factuality Factors" framework. 
Your goal is to mass-label video content, quantifying "Veracity Vectors" and "Modality Alignment".

**INPUT DATA:**
- **User Caption:** "{caption}"
- **Audio Transcript:** "{transcript}"
- **Visuals:** (Provided in video context)

**INSTRUCTIONS:**
1.  **Grounding:** Cross-reference claims in the transcript with your internal knowledge base (and tools if active).
2.  **Chain of Thought (<thinking>):** You MUST think step-by-step inside a `<thinking>` block before generating output.
    *   Analyze *Visual Integrity* (Artifacts, edits).
    *   Analyze *Audio Integrity* (Voice cloning, sync).
    *   Analyze *Modality Alignment* (Does video match audio? Does caption match content? Does audio match caption?).
    *   Analyze *Logic* (Fallacies, gaps).
    *   Determine *Disinformation* classification.
3.  **Output Format:** Output strictly in **TOON** format (Token-Oriented Object Notation) as defined below.

**CRITICAL CONSTRAINTS:** 
- Do NOT repeat the input data.
- START your response IMMEDIATELY with the `<thinking>` tag.
- **DO NOT use Markdown code blocks.** (Output plain text only).
- Use strict `Key : Type [ Count ] {{ Headers }} :` format followed by data lines.
- Strings containing commas MUST be quoted.
- ALL scores must be filled (use 0 if unsure, do not leave blank).
- **MODALITY SCORING:** You must provide 3 distinct alignment scores: Video-Audio, Video-Caption, and Audio-Caption.
- **TAGS:** {tag_list_text}

**TOON SCHEMA:**
{toon_schema}

{score_instructions}

**RESPONSE:**
<thinking>
"""

LABELING_PROMPT_TEMPLATE_NO_COT = """
{system_persona}

You are an AI Factuality Assessment Agent operating under the "Ali Arsanjani Factuality Factors" framework. 
Your goal is to mass-label video content, quantifying "Veracity Vectors" and "Modality Alignment".

**INPUT DATA:**
- **User Caption:** "{caption}"
- **Audio Transcript:** "{transcript}"
- **Visuals:** (Provided in video context)

**INSTRUCTIONS:**
1.  **Grounding:** Cross-reference claims in the transcript with your internal knowledge base (and tools if active).
2.  **Output Format:** Output strictly in **TOON** format (Token-Oriented Object Notation) as defined below.

**CRITICAL CONSTRAINTS:** 
- Do NOT repeat the input data.
- **DO NOT use Markdown code blocks.** (Output plain text only).
- Use strict `Key : Type [ Count ] {{ Headers }} :` format followed by data lines.
- Strings containing commas MUST be quoted.
- ALL scores must be filled (use 0 if unsure, do not leave blank).
- **MODALITY SCORING:** You must provide 3 distinct alignment scores: Video-Audio, Video-Caption, and Audio-Caption.
- **TAGS:** {tag_list_text}

**TOON SCHEMA:**
{toon_schema}

{score_instructions}

**RESPONSE:**
"""

SCORE_INSTRUCTIONS_REASONING = """
**Constraints:** 
1. Provide specific reasoning for EACH score in the `vectors` and `modalities` tables.
2. Ensure strings are properly quoted.
"""

SCORE_INSTRUCTIONS_SIMPLE = """
**Constraint:** Focus on objective measurements. Keep text concise.
"""

SCHEMA_SIMPLE = """summary: text[1]{text}:
"Brief neutral summary of the video events"

vectors: scores[1]{visual,audio,source,logic,emotion}:
(Int 1-10),(Int 1-10),(Int 1-10),(Int 1-10),(Int 1-10)
*Scale: 1=Fake/Malicious, 10=Authentic/Neutral*

modalities: scores[1]{video_audio_score,video_caption_score,audio_caption_score}:
(Int 1-10),(Int 1-10),(Int 1-10)
*Scale: 1=Mismatch, 10=Perfect Match*

factuality: factors[1]{accuracy,gap,grounding}:
(Verified/Misleading/False),"Missing evidence description","Grounding check results"

disinfo: analysis[1]{class,intent,threat}:
(None/Misinfo/Disinfo/Satire),(Political/Commercial/None),(Deepfake/Recontextualization/None)

final: assessment[1]{score,reasoning}:
(Int 1-100),"Final synthesis of why this score was given"

tags: keywords[1]{tags}:
"Comma-separated list of tags"
"""

SCHEMA_REASONING = """
summary: text[1]{text}:
"Brief neutral summary of the video events"

vectors: details[5]{category,score,reasoning}:
Visual,(Int 1-10),"Reasoning for visual score"
Audio,(Int 1-10),"Reasoning for audio score"
Source,(Int 1-10),"Reasoning for source credibility"
Logic,(Int 1-10),"Reasoning for logical consistency"
Emotion,(Int 1-10),"Reasoning for emotional manipulation"

modalities: details[3]{category,score,reasoning}:
VideoAudio,(Int 1-10),"Reasoning for video-to-audio alignment"
VideoCaption,(Int 1-10),"Reasoning for video-to-caption alignment"
AudioCaption,(Int 1-10),"Reasoning for audio-to-caption alignment"

factuality: factors[1]{accuracy,gap,grounding}:
(Verified/Misleading/False),"Missing evidence description","Grounding check results"

disinfo: analysis[1]{class,intent,threat}:
(None/Misinfo/Disinfo/Satire),(Political/Commercial/None),(Deepfake/Recontextualization/None)

final: assessment[1]{score,reasoning}:
(Int 1-100),"Final synthesis of why this score was given"

tags: keywords[1]{tags}:
"Comma-separated list of tags"
"""

FCOT_MACRO_PROMPT = """
{system_persona}

**Fractal Chain of Thought - Stage 1: Macro-Scale Hypothesis (Wide Aperture)**

You are analyzing a video for factuality.
**Context:** Caption: "{caption}" | Transcript: "{transcript}"

1. **Global Scan**: Observe the video, audio, and caption as a whole entity.
2. **Context Aperture**: Wide. Assess the overall intent (Humor, Information, Political, Social) and the setting.
3. **Macro Hypothesis**: Formulate a high-level hypothesis about the veracity. (e.g., "The video is likely authentic but the caption misrepresents the location" or "The audio quality suggests synthetic generation").

**Objective**: Maximize **Coverage** (broadly explore potential angles of manipulation).

**Output**: A concise paragraph summarizing the "Macro Hypothesis".
"""

FCOT_MESO_PROMPT = """
**Fractal Chain of Thought - Stage 2: Meso-Scale Expansion (Recursive Verification)**

**Current Macro Hypothesis**: "{macro_hypothesis}"

**Action**: Zoom In. Decompose the hypothesis into specific verification branches.
Perform the following checks recursively:

1. **Visual Branch**: Look for specific artifacts, lighting inconsistencies, cuts, or deepfake signs.
2. **Audio Branch**: Analyze lip-sync, background noise consistency, and voice tonality.
3. **Logical Branch**: Does the visual evidence strictly support the caption's claim? Are there logical fallacies?

**Dual-Objective Self-Correction**:
- **Faithfulness**: Do not hallucinate details not present in the video.
- **Coverage**: Did you miss any subtle cues?

**Output**: Detailed "Micro-Observations" for each branch. If you find contradictions to the Macro Hypothesis, note them explicitly as **"Self-Correction"**.
"""

FCOT_SYNTHESIS_PROMPT = """
**Fractal Chain of Thought - Stage 3: Inter-Scale Consensus & Synthesis**

**Action**: Integrate your Macro Hypothesis and Micro-Observations.
- **Consensus Check**: If Micro-Observations contradict the Macro Hypothesis, prioritize the Micro evidence (Self-Correction).
- **Compression**: Synthesize the findings into the final structured format.

**Output Format**:
Strictly fill out the following TOON schema based on the consensus. Do not include markdown code blocks.

**TAGS GUIDANCE**: {tag_list_text}

**TOON SCHEMA**:
{toon_schema}

{score_instructions}
"""