"""Pydantic Schemas for API Request/Response Validation.

Matches exact hackathon requirements:
- Request: language, audioFormat, audioBase64
- Response: status, language, classification, confidenceScore, explanation
"""

from pydantic import BaseModel, Field, field_validator
from typing import List, Optional, Dict
import base64


# ============== REQUEST SCHEMAS ==============

class VoiceDetectionRequest(BaseModel):
    """Request schema matching hackathon requirements exactly."""
    
    language: str = Field(
        ...,
        description="Language of the audio: Tamil, English, Hindi, Malayalam, Telugu"
    )
    audioFormat: str = Field(
        default="mp3",
        description="Audio format (always mp3 per requirements)"
    )
    audioBase64: str = Field(
        ..., 
        description="Base64 encoded MP3 audio file",
        min_length=100
    )
    
    @field_validator('audioBase64')
    @classmethod
    def validate_base64(cls, v: str) -> str:
        """Validate that audio is valid Base64."""
        try:
            decoded = base64.b64decode(v)
            if len(decoded) < 100:
                raise ValueError("Audio data too small")
            return v
        except Exception as e:
            raise ValueError(f"Invalid Base64 encoding: {str(e)}")
    
    @field_validator('language')
    @classmethod
    def validate_language(cls, v: str) -> str:
        """Validate language is supported (case-insensitive)."""
        valid_languages = ['tamil', 'english', 'hindi', 'malayalam', 'telugu']
        v_lower = v.lower()
        if v_lower not in valid_languages:
            raise ValueError(f"Language must be one of: Tamil, English, Hindi, Malayalam, Telugu")
        return v_lower
    
    @field_validator('audioFormat')
    @classmethod
    def validate_audio_format(cls, v: str) -> str:
        """Validate audio format is mp3."""
        if v.lower() != "mp3":
            raise ValueError("audioFormat must be 'mp3'")
        return v.lower()

    class Config:
        json_schema_extra = {
            "example": {
                "language": "Tamil",
                "audioFormat": "mp3",
                "audioBase64": "SUQzBAAAAAAAI1RTU0UAAAAPAAADTGF2ZjU2LjM2LjEwMAAAAAAA..."
            }
        }


# ============== RESPONSE SCHEMAS ==============

class VoiceDetectionResponse(BaseModel):
    """Success response matching hackathon requirements exactly."""
    
    status: str = Field(
        default="success",
        description="Response status: success or error"
    )
    language: str = Field(
        ...,
        description="Language of the audio"
    )
    classification: str = Field(
        ...,
        description="AI_GENERATED or HUMAN"
    )
    confidenceScore: float = Field(
        ...,
        ge=0,
        le=1,
        description="Confidence score between 0.0 and 1.0"
    )
    explanation: str = Field(
        ...,
        description="Short reason for the decision"
    )

    class Config:
        json_schema_extra = {
            "example": {
                "status": "success",
                "language": "Tamil",
                "classification": "AI_GENERATED",
                "confidenceScore": 0.91,
                "explanation": "Unnatural pitch consistency and robotic speech patterns detected"
            }
        }


class ErrorResponse(BaseModel):
    """Error response matching hackathon requirements exactly."""
    
    status: str = Field(
        default="error",
        description="Response status: error"
    )
    message: str = Field(
        ...,
        description="Error message"
    )

    class Config:
        json_schema_extra = {
            "example": {
                "status": "error",
                "message": "Invalid API key or malformed request"
            }
        }


# ============== HEALTH CHECK SCHEMA (bonus, not required) ==============

class HealthResponse(BaseModel):
    """Health check response."""
    
    status: str = Field(default="healthy", description="Service health status")
    model_loaded: bool = Field(..., description="Whether ML model is loaded")
    version: str = Field(default="1.0.0", description="API version")