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from pydantic import BaseModel
from typing import Dict, List, Optional
# Pydantic model for the base response
class BaseResponse(BaseModel):
status: str
message: Optional[str] = None
class WelcomeResponse(BaseResponse):
endpoints: Dict[str, str]
class ModelInfoResponse(BaseResponse):
model_name: str
model_version: str
supported_formats: List[str]
max_file_size_mb: int
training_info: Optional[Dict] = None
last_updated: Optional[str] = None
# Pydantic model for the prediction response
class PredictionResponse(BaseModel):
status: str
lyrics: str
audio_file_name: str
audio_content_type: str
audio_file_size: int
results: Optional[Dict] = None
class PredictionXAIResponse(BaseModel):
status: str
lyrics: str
audio_file_name: str
audio_content_type: str
audio_file_size: int
results: Optional[Dict] = None
class AudioOnlyPredictionResponse(BaseModel):
status: str
audio_file_name: str
audio_content_type: str
audio_file_size: int
results: dict
class AudioOnlyPredictionXAIResponse(BaseModel):
status: str
audio_file_name: str
audio_content_type: str
audio_file_size: int
results: dict
class CombinedExplanationResponse(BaseModel):
status: str
lyrics: str
audio_file_name: str
audio_content_type: str
audio_file_size: int
results: dict # Contains both multimodal and audio_only results
class CombinedPredictionResponse(BaseModel):
status: str
lyrics: str
audio_file_name: str
audio_content_type: str
audio_file_size: int
results: dict # Contains both multimodal and audio_only predictions
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