backend/models.py

from typing import List, Optional, Dict, Any
from pydantic import BaseModel, Field
from datetime import datetime, timezone


def current_utc_time():
    return datetime.now(timezone.utc)


class AlternativeDiagnosis(BaseModel):
    disease_id: str
    disease_name: str
    crop: str
    confidence: float = Field(..., ge=0.0, le=1.0)


class TreatmentPlan(BaseModel):
    immediate_action: List[str]
    organic_control: List[str]
    chemical_control: List[str]


class DiseaseDetail(BaseModel):
    id: str
    name: str
    scientific_name: Optional[str] = None
    crop: str
    severity: str = Field(..., description="Healthy | Low | Moderate | Severe | Critical | Invalid")
    is_healthy: bool = False
    symptoms: List[str]
    treatment: TreatmentPlan
    prevention: List[str]
    expert_consultation_recommended: bool = False


class DiagnosisResponse(BaseModel):
    success: bool = True
    is_valid_leaf: bool = Field(default=True, description="False if image is a car, object, or non-plant")
    timestamp: datetime = Field(default_factory=current_utc_time)
    image_id: str
    crop: str
    disease: str
    disease_id: str
    scientific_name: Optional[str] = None
    is_healthy: bool
    severity: str
    confidence: float = Field(..., ge=0.0, le=1.0)
    confidence_percentage: str
    symptoms: List[str]
    management: TreatmentPlan
    prevention: List[str]
    alternatives: List[AlternativeDiagnosis] = []
    similar_crops: List[str] = []
    expert_escalation: dict = Field(
        default_factory=lambda: {
            "recommended": False,
            "helpline_number": "+918817814859",
            "whatsapp_expert": "+918817814859",
            "reason": None
        }
    )


class CropInfo(BaseModel):
    crop_name: str
    supported_diseases_count: int
    diseases: List[str]


class SupportedCropsResponse(BaseModel):
    total_crops: int
    total_classes: int
    crops: List[CropInfo]


class ExpertTicketRequest(BaseModel):
    farmer_name: str
    phone_number: str
    location: Optional[str] = None
    crop: str
    disease_suspected: Optional[str] = None
    confidence_score: Optional[float] = None
    notes: Optional[str] = None
    image_url_or_id: Optional[str] = None


class ExpertTicketResponse(BaseModel):
    ticket_id: str
    status: str = "PENDING_REVIEW"
    created_at: datetime = Field(default_factory=current_utc_time)
    message: str
    assigned_expert: str = "Dr. S. Sharma (Senior Agronomist, Plant Pathology)"
    estimated_callback_minutes: int = 30


class DosageCalculationRequest(BaseModel):
    crop: str
    disease_id: str
    farm_area_acres: float = Field(..., gt=0.0, description="Farm area in acres")
    knapsack_tank_liters: float = Field(default=16.0, gt=0.0, description="Knapsack sprayer tank capacity in liters")
    treatment_type: str = Field(default="chemical", description="chemical | organic")


class DosageCalculationResponse(BaseModel):
    crop: str
    disease: str
    farm_area_acres: float
    recommended_chemical_or_bio: str
    total_water_liters: float
    total_dosage_amount: str
    dosage_per_tank: str
    total_knapsack_tanks: int
    spray_instructions: List[str]
    safety_precautions: List[str]


class SporeCastPoint(BaseModel):
    district: str
    latitude: float
    longitude: float
    disease: str
    risk_level: str  # Low, Moderate, High, Severe Epidemic
    wind_vector: str
    forecast_48h: str
    affected_farms_reported: int


class SporeCastAlertResponse(BaseModel):
    active_epidemics: int
    highest_risk_region: str
    spore_dispersion_speed_kmh: float
    regional_heatmap: List[SporeCastPoint]
    kvk_advisory: str


class GreenCreditRequest(BaseModel):
    farmer_id: Optional[str] = "FARMER-001"
    crop: str
    farm_area_acres: float
    adopted_practice: str = Field(..., description="bio_fungicide | neem_extract | trichoderma | balanced_ipm")


class GreenCreditResponse(BaseModel):
    green_credits_earned: int
    chemical_pesticide_saved_kg: float
    carbon_offset_kg_co2e: float
    soil_microbiome_health_score: str
    reward_redemption_eligible: bool
    certificate_id: str


class SpectroscopyAnalysisResponse(BaseModel):
    is_leaf: bool = True
    is_valid_leaf: bool = True
    rejection_code: Optional[str] = None
    error_message: Optional[str] = None
    chlorophyll_retention_percentage: float = 0.0
    green_leaf_index_gli: float = 0.0
    vari_index: float = 0.0
    ngrdi_index: float = 0.0
    health_status: str
    asymptomatic_stress_detected: bool = False
    risk_classification: str
    spectral_diagnosis: str
    early_action_protocol: List[str] = []
    foliage_coverage_percentage: Optional[float] = None
    diagnostics: Optional[dict] = None


class FarmMemorySaveRequest(BaseModel):
    crop: str
    disease: str
    disease_id: str
    severity: str
    confidence: float
    temperature_c: float
    humidity_pct: float
    weather_condition: str
    location_name: Optional[str] = "Ambikapur, Surguja"
    gps_lat: Optional[float] = 23.1188
    gps_lng: Optional[float] = 83.1979
    farmer_notes: Optional[str] = ""
    diagnosis_date: Optional[str] = None


class SeasonalAlertItem(BaseModel):
    alert_id: str
    crop: str
    disease: str
    disease_id: str
    historical_outbreak_date: str
    threat_level: str
    recurrence_risk_score: float
    window_description: str
    alert_title: str
    alert_message: str
    prophylactic_plan: dict


class SeasonalAlertsResponse(BaseModel):
    status: str = "success"
    active_alerts_count: int
    alerts: List[SeasonalAlertItem]


class BioAcousticAnalysisResponse(BaseModel):
    pest_detected: bool
    pest_identified: str
    threat_level: str
    confidence_score: float
    dominant_frequency_hz: float
    pest_acoustic_energy_ratio: float
    diagnosis_summary: str
    recommended_actions: List[str]
    engine_type: Optional[str] = "Mel_CNN_BioAcoustic"
    class_probabilities: Optional[Dict[str, float]] = None
    mastication_rhythm_index: Optional[float] = None


class BioTwinForecastResponse(BaseModel):
    crop: str
    target_pathogen: str
    spore_germination_index_pct: float
    risk_classification: str
    calculated_dew_point_c: float
    effective_leaf_wetness_hours: float
    critical_infection_window_hours: int
    physics_forecast_summary: str
    preventive_canopy_actions: List[str]


class DroneVRAMissionRequest(BaseModel):
    field_id: str = "FIELD-01"
    center_lat: float = 23.1188
    center_lng: float = 83.1979
    field_area_acres: float = Field(..., gt=0.0)
    infected_clusters: List[dict] = Field(default_factory=list)
    spray_altitude_meters: float = Field(default=2.5, gt=0.5, le=10.0)


class DroneVRAMissionResponse(BaseModel):
    mission_id: str
    field_area_acres: float
    flight_altitude_meters: float
    swath_width_meters: float
    total_waypoints: int
    estimated_flight_duration_minutes: float
    vra_spot_spray_liters: float
    traditional_blanket_spray_liters: float
    chemical_savings_percentage: str
    waypoints: List[dict]
    geojson_flight_plan: dict
    qgroundcontrol_format_compatible: bool


class ProofOfFarmingRequest(BaseModel):
    farmer_id: str = "FARMER-101"
    farmer_name: str
    khasra_survey_number: str
    district: str = "Surguja"
    state: str = "Chhattisgarh"
    crop: str = "Paddy (Rice)"
    disease_identified: str
    severity_percentage: float = Field(..., ge=0.0, le=100.0)
    affected_area_acres: float = Field(..., gt=0.0)
    gps_latitude: float
    gps_longitude: float
    image_hash: Optional[str] = None


class ProofOfFarmingResponse(BaseModel):
    certificate_id: str
    digital_signature_sha256: str
    created_at_utc: str
    claim_status: str
    fast_track_claim_payload: dict
    blockchain_smart_contract_ready: bool
    verification_qr_payload: str


class GovernmentSchemeItem(BaseModel):
    id: str
    title_hi: str
    title_en: str
    category: str
    category_hi: str
    benefit_amount: str
    summary_hi: str
    summary_en: str
    eligibility_hi: str
    required_documents_hi: List[str]
    official_portal: str
    direct_apply_url: Optional[str] = None
    status_check_url: Optional[str] = None
    guidelines_pdf_url: Optional[str] = None
    source_portal_authority: Optional[str] = "Government of India"
    helpline: str
    icon: str
    badge_color: str
    last_synced_utc: Optional[str] = None


class GovernmentSchemesResponse(BaseModel):
    total_schemes: int
    myscheme_portal_url: str = "https://www.myscheme.gov.in/search/category/Agriculture,Rural%20&%20Environment"
    live_sync_status: str = "SYNCED_WITH_GOVT_PORTALS"
    categories: List[str]
    schemes: List[GovernmentSchemeItem]


class KisanPollOptionItem(BaseModel):
    id: str
    title_hi: str
    title_en: str
    description_hi: str
    icon: str
    votes_count: int
    vote_percentage: float
    recommended_govt_action_hi: str


class KisanPollVoteRequest(BaseModel):
    problem_id: str
    district: Optional[str] = "Surguja"
    state: Optional[str] = "Chhattisgarh"
    crop_affected: Optional[str] = "Paddy (Rice)"
    farmer_comment: Optional[str] = ""


class KisanPollResultsResponse(BaseModel):
    target_district: str
    total_farmer_responses: int
    highest_priority_issue: str
    highest_priority_vote_share: str
    recommended_immediate_intervention: str
    issues_breakdown: List[KisanPollOptionItem]
    last_updated: str


# =========================================================================
# Next-Gen Innovation Schemas (Voice, Traps, Satellite, Soil, GNN)
# =========================================================================
class VoiceAdvisoryRequest(BaseModel):
    user_query: str
    dialect: Optional[str] = "ch-IN"
    crop: Optional[str] = None


class VoiceAdvisoryResponse(BaseModel):
    status: str = "success"
    dialect: str
    dialect_name: str
    user_query: str
    normalized_context: dict
    spoken_title: str
    advisory_text: str
    tank_prescription: dict
    tts_playback_metadata: dict



class SoilHealthCardRequest(BaseModel):
    crop: Optional[str] = "Rice"
    area_acres: Optional[float] = 1.0
    ph: Optional[float] = 6.8
    organic_carbon_pct: Optional[float] = 0.52
    available_n_kg_ha: Optional[float] = 220.0
    available_p_kg_ha: Optional[float] = 14.5
    available_k_kg_ha: Optional[float] = 180.0
    zinc_ppm: Optional[float] = 0.55
    has_active_blast: Optional[bool] = False


class SoilHealthCardResponse(BaseModel):
    status: str = "success"
    soil_test_readings: dict
    target_crop: str
    area_acres: float
    total_commercial_fertilizer: dict
    split_schedule: List[dict]
    safety_alert: Optional[str] = None



# =========================================================================
# Extreme Weather Shock & IoT Telemetry Schemas
# =========================================================================
class WeatherShockForecastRequest(BaseModel):
    crop: Optional[str] = "Potato"
    ambient_temp_c: Optional[float] = 4.0
    min_predicted_temp_c: Optional[float] = 1.5
    max_predicted_temp_c: Optional[float] = 24.0
    relative_humidity: Optional[float] = 55.0
    wind_speed_kmh: Optional[float] = 3.0
    cloud_cover_pct: Optional[float] = 5.0
    growth_stage: Optional[str] = None


class WeatherShockForecastResponse(BaseModel):
    status: str = "success"
    crop: str
    vulnerable_stage: str
    primary_threat: str
    microclimate_snapshot: dict
    frost_assessment: dict
    heatwave_assessment: dict


class IoTTelemetryPacket(BaseModel):
    device_id: str = "NODE-SURGUJA-01"
    battery_voltage_v: float = 3.92
    solar_charging: bool = True
    rssi_dbm: int = -88
    snr_db: float = 8.5
    soil_moisture_vwc_15cm: float = 24.5
    soil_moisture_vwc_30cm: float = 28.2
    canopy_temp_c: float = 26.4
    canopy_rh_pct: float = 82.0
    leaf_wetness_pct: float = 45.0


class IoTTelemetryResponse(BaseModel):
    status: str = "success"
    device_id: str
    packet_timestamp_utc: str
    soil_moisture_assessment: dict
    microclimate: dict
    hardware_telemetry: dict
    active_alarms: List[dict]


# =========================================================================
# Kisan Jan-Mat Community Poll & Government Scheme Schemas
# =========================================================================
class KisanPollVoteRequest(BaseModel):
    problem_id: str
    district: Optional[str] = "Surguja"
    state: Optional[str] = "Chhattisgarh"
    crop_affected: Optional[str] = "Paddy (Rice)"
    farmer_comment: Optional[str] = ""
    device_id: Optional[str] = ""


class KisanPollVoteResponse(BaseModel):
    status: str
    message_hi: str
    message_en: str
    ballot_id: str
    total_community_votes: int
    updated_results: List[dict]


class KisanPollQuestionsResponse(BaseModel):
    status: str = "success"
    total_options: int
    options: List[dict]


class KisanPollResultsResponse(BaseModel):
    status: str = "success"
    target_district: str
    total_farmer_responses: int
    highest_priority_issue: str
    highest_priority_vote_share: str
    recommended_immediate_intervention: str
    issues_breakdown: List[dict]
    recent_grassroot_ballots: Optional[List[dict]] = []
    last_updated: str