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9062d0b 813f9c0 9062d0b 813f9c0 9062d0b 813f9c0 9062d0b 813f9c0 9062d0b 813f9c0 9062d0b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 | # import time
# import pickle
# import numpy as np
# from fastapi import FastAPI, Request, status, HTTPException, Depends
# from fastapi.security.api_key import APIKeyHeader
# from pydantic import BaseModel
# from typing import List
# import os
# import joblib
# app = FastAPI(
# title="ML Music Classifier API",
# description="Predict 'liked' or not from audio features using 8 ML models.",
# version="1.0.0"
# )
# DEFAULT_TOKEN = "a9b7c7e8b0e44157a99c9a8c5f6a172e10b77e2b44693506a32e5a6a0cd749d0"
# api_key_header = APIKeyHeader(name="X-Token", auto_error=False)
# @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["X-Process-Time"] = str(process_time)
# return response
# def verify_token(x_token: str = Depends(api_key_header)):
# if x_token != DEFAULT_TOKEN:
# raise HTTPException(
# status_code=status.HTTP_401_UNAUTHORIZED,
# detail="Invalid or missing token. Use correct 'X-Token' in headers."
# )
# class SongFeatures(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
# MODEL_DIR = "models"
# model_names = [
# "ANN_model", "knn_model", "logistic_regression_model",
# "neural_model", "Naive_Bayes_model",
# "random_forest_model", "svm_model", "XGBoost_model"
# ]
# models = {}
# for name in model_names:
# path = os.path.join(MODEL_DIR, f"{name}.pkl")
# try:
# models[name] = joblib.load(path)
# except Exception as e:
# print(f"Error loading {name}: {e}")
# @app.post("/predict/{model_name}", dependencies=[Depends(verify_token)])
# def predict(model_name: str, features: SongFeatures):
# if model_name not in models:
# raise HTTPException(status_code=404, detail="Model not found")
# model = models[model_name]
# input_array = np.array([[getattr(features, field) for field in features.model_fields]])
# try:
# prediction = model.predict(input_array)
# return {
# "model": model_name,
# "input": features,
# "prediction": int(prediction[0]),
# "prediction_label": "liked" if int(prediction[0]) == 1 else "not_liked"
# }
# except Exception as e:
# raise HTTPException(status_code=500, detail=f"Prediction error: {str(e)}")
# import time
# import pickle
# import numpy as np
# from fastapi import FastAPI, Request, status, HTTPException, Depends
# from fastapi.security.api_key import APIKeyHeader
# from pydantic import BaseModel
# from typing import List
# import os
# import joblib
# app = FastAPI(
# title="ML Music Classifier API",
# description="Predict 'liked' or not from audio features using 8 ML models.",
# version="1.0.0"
# )
# DEFAULT_TOKEN = "a9b7c7e8b0e44157a99c9a8c5f6a172e10b77e2b44693506a32e5a6a0cd749d0"
# api_key_header = APIKeyHeader(name="X-Token", auto_error=False)
# @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["X-Process-Time"] = str(process_time)
# return response
# def verify_token(x_token: str = Depends(api_key_header)):
# if x_token != DEFAULT_TOKEN:
# raise HTTPException(
# status_code=status.HTTP_401_UNAUTHORIZED,
# detail="Invalid or missing token. Use correct 'X-Token' in headers."
# )
# class SongFeatures(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
# MODEL_DIR = "models"
# model_names = [
# "ANN_model", "knn_model", "logistic_regression_model",
# "neural_model", "Naive_Bayes_model",
# "random_forest_model", "svm_model", "XGBoost_model"
# ]
# models = {}
# for name in model_names:
# path = os.path.join(MODEL_DIR, f"{name}.pkl")
# try:
# models[name] = joblib.load(path)
# print(f"✅ Successfully loaded {name}")
# except Exception as e:
# print(f"❌ Error loading {name}: {e}")
# @app.get("/")
# def root():
# return {
# "message": "ML Music Classifier API is running!",
# "loaded_models": len(models),
# "available_models": list(models.keys()),
# "endpoints": {
# "predict": "/predict/{model_name}",
# "docs": "/docs",
# "health": "/health"
# }
# }
# @app.get("/health")
# def health_check():
# return {
# "status": "healthy",
# "loaded_models": len(models),
# "available_models": list(models.keys())
# }
# @app.post("/predict/{model_name}", dependencies=[Depends(verify_token)])
# def predict(model_name: str, features: SongFeatures):
# if model_name not in models:
# raise HTTPException(status_code=404, detail="Model not found")
# model = models[model_name]
# input_array = np.array([[getattr(features, field) for field in features.model_fields]])
# try:
# prediction = model.predict(input_array)
# return {
# "model": model_name,
# "input": features.dict(),
# "prediction": int(prediction[0]),
# "prediction_label": "liked" if int(prediction[0]) == 1 else "not_liked"
# }
# except Exception as e:
# raise HTTPException(status_code=500, detail=f"Prediction error: {str(e)}")
# if __name__ == "__main__":
# import uvicorn
# uvicorn.run(app, host="0.0.0.0", port=7860)
import time
import pickle
import numpy as np
from fastapi import FastAPI, Request, status, HTTPException, Depends
from fastapi.security.api_key import APIKeyHeader
from fastapi.templating import Jinja2Templates
from fastapi.responses import HTMLResponse
from pydantic import BaseModel
from typing import List
import os
import joblib
app = FastAPI(
title="ML Music Classifier API",
description="Predict 'liked' or not from audio features using 8 ML models.",
version="1.0.0"
)
templates = Jinja2Templates(directory="templates")
DEFAULT_TOKEN = "a9b7c7e8b0e44157a99c9a8c5f6a172e10b77e2b44693506a32e5a6a0cd749d0"
api_key_header = APIKeyHeader(name="X-Token", auto_error=False)
@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["X-Process-Time"] = str(process_time)
return response
def verify_token(x_token: str = Depends(api_key_header)):
if x_token != DEFAULT_TOKEN:
raise HTTPException(
status_code=status.HTTP_401_UNAUTHORIZED,
detail="Invalid or missing token. Use correct 'X-Token' in headers."
)
class SongFeatures(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
MODEL_DIR = "models"
model_names = [
"ANN_model", "knn_model", "logistic_regression_model",
"neural_model", "Naive_Bayes_model",
"random_forest_model", "svm_model", "XGBoost_model"
]
models = {}
for name in model_names:
path = os.path.join(MODEL_DIR, f"{name}.pkl")
try:
models[name] = joblib.load(path)
print(f"✅ Successfully loaded {name}")
except Exception as e:
print(f"❌ Error loading {name}: {e}")
@app.get("/", response_class=HTMLResponse)
def home(request: Request):
return templates.TemplateResponse("index.html", {
"request": request,
"models": list(models.keys())
})
@app.get("/health")
def health_check():
return {
"status": "healthy",
"loaded_models": len(models),
"available_models": list(models.keys())
}
@app.post("/predict/{model_name}", dependencies=[Depends(verify_token)])
def predict(model_name: str, features: SongFeatures):
if model_name not in models:
raise HTTPException(status_code=404, detail="Model not found")
model = models[model_name]
input_array = np.array([[getattr(features, field) for field in features.model_fields]])
try:
prediction = model.predict(input_array)
return {
"model": model_name,
"input": features.dict(),
"prediction": int(prediction[0]),
"prediction_label": "liked" if int(prediction[0]) == 1 else "Not liked"
}
except Exception as e:
raise HTTPException(status_code=500, detail=f"Prediction error: {str(e)}")
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=7860) |