from typing import List, Literal, Optional
from pydantic import BaseModel, Field

from openai import OpenAI
from dotenv import load_dotenv
import os

load_dotenv()

client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))

class VisualHierarchy(BaseModel):
    flow_summary: str = Field(..., description="Brief of top-to-bottom reading flow and funneling.")
    key_elements_order: List[str] = Field(..., description="Ordered list of elements as the eye flows.")

class TypographySpec(BaseModel):
    casing: Literal["all-caps","title-case","sentence-case","mixed"]
    weight: Literal["light","regular","medium","bold","extra-bold"]
    color: str
    notes: str

class CTAInfo(BaseModel):
    text: str
    shape_or_style: str
    bg_color: str
    text_color: str
    contrast_comment: str

class VisualStructureLayout(BaseModel):
    background_color: str
    psychological_association: str
    fit_to_theme_comment: str
    typography: TypographySpec
    minimalism_level: Literal["none","low","medium","high"]
    cta: CTAInfo
    hierarchy: VisualHierarchy

class CopywritingBreakdown(BaseModel):
    headline: str
    device_used: List[Literal["curiosity_gap","authority","fear_avoidance","education","urgency","social_proof","other"]]
    supporting_text: str
    compliance_comment: str
    cta_text: str
    funnel_feel: Literal["content_recommendation","direct_response","hybrid"]

class PsychologicalBehavioralTriggers(BaseModel):
    curiosity: str
    fear_or_pain_point: str
    authority_appeal: str
    ease_of_action: str

class StoryCreativeTells(BaseModel):
    problem: str
    hope: str
    next_step: str
    narrative_summary: str

class StrengthWeaknessItem(BaseModel):
    point: str
    why_it_matters: str

class RisksArbitrageContext(BaseModel):
    bounce_risk: str
    fatigue_risk: str
    compliance_risk: str

class CreativeVariant(BaseModel):
    hypothesis: str
    change: str
    expected_effect: str
    metric_to_watch: Literal["CTR","CPC","CPI","CVR","RPM","BounceRate","TimeOnPage","QualityRanking","Other"]

class FunnelSynergy(BaseModel):
    landing_page_angle: str
    affiliate_or_offer_fit: Optional[str] = None

class TestingPlan(BaseModel):
    primary_metrics: List[Literal["CTR","CPC","CVR","RPM","BounceRate","TimeOnPage","QualityRanking","Other"]]
    sample_size_or_duration_hint: str
    stop_loss_rules: str

class OptimizationNextSteps(BaseModel):
    creative_variants: List[CreativeVariant]
    funnel_synergy: FunnelSynergy
    testing_plan: TestingPlan

class MetaDetect(BaseModel):
    language: str
    detected_topic: str
    confidence_0to1: float = Field(ge=0, le=1)
    red_flags: List[str] = Field(..., description="Potential policy or brand-safety flags, if any.")

class AdAnalysis(BaseModel):
    visual_structure_layout: VisualStructureLayout
    copywriting_breakdown: CopywritingBreakdown
    psychological_behavioral_triggers: PsychologicalBehavioralTriggers
    story_creative_tells: StoryCreativeTells
    strengths: List[StrengthWeaknessItem]
    weaknesses: List[StrengthWeaknessItem]
    risks_in_arbitrage_context: RisksArbitrageContext
    optimization_next_steps: OptimizationNextSteps
    meta: MetaDetect


SYSTEM_PROMPT = """
You are a senior performance-creative analyst who analyze a SINGLE image ad. Do not include explanations, markdown, or extra keys.

Rules:
- Be precise and concise. Avoid generic fluff.
- When unsure, infer cautiously and note uncertainty.
- Explain “why it matters” in business terms (CTR, CPC, CVR, RPM, bounce, fatigue, policy).
- Do not invent medical claims or promises; flag policy risks instead.
- Keep color names human-readable (e.g., “pure orange”, “purple”, “yellow”).
"""

USER_PROMPT_TEMPLATE = """
Task: Analyze the attached image ad and produce a structured breakdown:
1) Visual structure & layout
2) Copywriting breakdown
3) Psychological & behavioral triggers
4) Story the creative tells
5) Strengths
6) Weaknesses
7) Risks in arbitrage context
8) Optimization / next steps (variants, funnel synergy, testing plan)

Constraints:
- Use crisp, decision-ready language.
- If text is in Spanish or another language, keep your summaries in English but preserve original CTA text.
"""

def get_analysis(img_url):
    messages=[
        {
            "role": "system",
            "content": [
                {
                    "type": "text",
                    "text": SYSTEM_PROMPT
                }
            ]
        },
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": USER_PROMPT_TEMPLATE
                },
                {
                    "type": "image_url",
                    "image_url": {
                        "url": img_url
                    }
                }
            ]
        }
    ]

    completion = client.chat.completions.parse(
        model="gpt-4o",
        messages=messages,
        response_format=AdAnalysis,
    )

    ad_analysis = completion.choices[0].message.parsed

    return ad_analysis