app.py

import time
import joblib
import numpy as np
from fastapi import FastAPI, HTTPException, Request
from pydantic import BaseModel
from fastapi.templating import Jinja2Templates
from fastapi.responses import HTMLResponse
from fastapi.staticfiles import StaticFiles

app = FastAPI()
templates = Jinja2Templates(directory="templates")
app.mount("/static", StaticFiles(directory="static"), name="static")

@app.middleware("http")
async def add_process_time_header(request: Request, call_next):
    start_time = time.perf_counter()
    response = await call_next(request)
    process_time = time.perf_counter() - start_time
    response.headers["time"] = str(process_time)
    return response

model_paths = {
    "logistic_regression": r"logistic_regression.pkl",
    "random_forest_model": r"random_forest_model.pkl",
    "decision_tree": r"DecisionTreeClassifier.pkl",
    "svm": r"SVM_model.pkl",
    "knn": r"KNeighborsClassifier_model.pkl",
    "naive_bayes": r"Naive_Bayes_model.pkl",
    "ann": r"ANN_model.pkl"
}

models = {name: joblib.load(path) for name, path in model_paths.items()}

class MusicFeatures(BaseModel):
    danceability: float
    energy: float
    key: int
    loudness: float
    mode: int
    speechiness: float
    acousticness: float
    instrumentalness: float
    liveness: float
    valence: float
    tempo: float
    duration_ms: int
    time_signature: int

def make_prediction(model, features: MusicFeatures):
    input_data = np.array([[  
        features.danceability, features.energy, features.key,
        features.loudness, features.mode, features.speechiness,
        features.acousticness, features.instrumentalness, features.liveness,
        features.valence, features.tempo, features.duration_ms, features.time_signature
    ]])
    return int(model.predict(input_data)[0])

@app.get("/", response_class=HTMLResponse)
async def read_home(request: Request):
    return templates.TemplateResponse("index.html", {"request": request})

@app.post("/predict/logistic_regression")
def predict_logistic(features: MusicFeatures):
    return {"model": "Logistic Regression", "prediction": make_prediction(models["logistic_regression"], features)}

@app.post("/predict/random_forest")
def predict_rf(features: MusicFeatures):
    return {"model": "Random Forest", "prediction": make_prediction(models["random_forest_model"], features)}

@app.post("/predict/decision_tree")
def predict_dt(features: MusicFeatures):
    return {"model": "Decision Tree", "prediction": make_prediction(models["decision_tree"], features)}

@app.post("/predict/svm")
def predict_svm(features: MusicFeatures):
    return {"model": "SVM", "prediction": make_prediction(models["svm"], features)}

@app.post("/predict/knn")
def predict_knn(features: MusicFeatures):
    return {"model": "K-Nearest Neighbors", "prediction": make_prediction(models["knn"], features)}

@app.post("/predict/naive_bayes")
def predict_nb(features: MusicFeatures):
    return {"model": "Naive Bayes", "prediction": make_prediction(models["naive_bayes"], features)}

@app.post("/predict/ann")
def predict_ann(features: MusicFeatures):
    return {"model": "Artificial Neural Network", "prediction": make_prediction(models["ann"], features)}