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from fastapi import APIRouter, Depends
from sqlalchemy.orm import Session
from schemas.appointment import AppointmentInput
from core.database import SessionLocal
from core.models.appointment import Appointment
router = APIRouter()
# Dependency
def get_db():
db = SessionLocal()
try:
yield db
finally:
db.close()
@router.post("/appointments/create")
def create_appointment(data: AppointmentInput, db: Session = Depends(get_db)):
new_appointment = Appointment(
patient_name=data.patient_name,
age=data.age,
symptoms=data.symptoms,
specialist=data.specialist
)
db.add(new_appointment)
db.commit()
db.refresh(new_appointment)
return {
"message": "Appointment saved to database",
"data": {
"id": new_appointment.id,
"patient_name": new_appointment.patient_name,
"age": new_appointment.age,
"symptoms": new_appointment.symptoms,
"specialist": new_appointment.specialist
}
}
from fastapi import APIRouter, UploadFile, File
from utils.stt_processor import simulate_stt # make sure path is correct
import shutil
import os
from utils.specialist_predictor import predict_specialist
@router.post("/appointments/voice")
async def create_appointment_from_voice(audio: UploadFile = File(...), db: Session = Depends(get_db)):
# 1. Save temp audio
temp_path = f"temp_{audio.filename}"
with open(temp_path, "wb") as buffer:
shutil.copyfileobj(audio.file, buffer)
try:
# 2. Extract symptoms from audio
data = simulate_stt(temp_path)
symptoms = data.get("symptoms", "")
# 3. Predict specialist using model
specialist, score = predict_specialist(symptoms)
# 4. Create appointment object
new_appointment = Appointment(
patient_name=data.get("patient_name", "Unknown"),
age=data.get("age", 0),
symptoms=symptoms,
specialist=specialist
)
db.add(new_appointment)
db.commit()
db.refresh(new_appointment)
# 5. Return response
return {
"message": "Appointment created successfully from voice",
"predicted_specialist": specialist,
"similarity_score": round(score, 4),
"appointment": {
"id": new_appointment.id,
"patient_name": new_appointment.patient_name,
"age": new_appointment.age,
"symptoms": symptoms,
"specialist": specialist
}
}
finally:
# Clean up file
os.remove(temp_path) |