app.py

# app.py
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from typing import List, Tuple, Dict
import helper
import dialogue
import models.linear as linear_model
import models.logistic as logistic_model
import judge

app = FastAPI(
    title="KindCare Chat API with ML",
    description="Chat + prediction + judge endpoints"
)

class ChatRequest(BaseModel):
    message: str
    history: List[Tuple[str, str]] = []

class ChatResponse(BaseModel):
    reply: str
    history: List[Tuple[str, str]]

class PredictRequest(BaseModel):
    features: Dict[str, float]

class PredictLinearResponse(BaseModel):
    prediction: float
    metrics: Dict[str, float]

class PredictLogisticRequest(PredictRequest):
    bins: List[str]

class PredictLogisticResponse(BaseModel):
    prediction: str
    metrics: Dict[str, float]

class JudgeRequest(BaseModel):
    model_type: str
    metrics: Dict[str, float]

class JudgeResponse(BaseModel):
    verdict: str
    comments: List[str]

@app.post("/chat", response_model=ChatResponse)
async def chat(req: ChatRequest):
    try:
        reply, hist = dialogue.handle_message(req.message, req.history)
        return ChatResponse(reply=reply, history=hist)
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

@app.post("/predict/linear", response_model=PredictLinearResponse)
async def predict_linear(req: PredictRequest):
    pred, metrics = linear_model.predict(req.features)
    return PredictLinearResponse(prediction=pred, metrics=metrics)

@app.post("/predict/logistic", response_model=PredictLogisticResponse)
async def predict_logistic(req: PredictLogisticRequest):
    pred, metrics = logistic_model.predict(req.features, req.bins)
    return PredictLogisticResponse(prediction=pred, metrics=metrics)

@app.post("/judge/model", response_model=JudgeResponse)
async def judge_endpoint(req: JudgeRequest):
    result = judge.judge_model(req.model_type, req.metrics)
    return JudgeResponse(**result)

if __name__ == "__main__":
    import uvicorn
    uvicorn.run("app:app", host="0.0.0.0", port=8000)