Initial upload of ML music classifier app

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Dockerfile

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+FROM python:3.9-slim
+
+WORKDIR /app
+
+# Copy requirements and install dependencies
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy application code and models
+COPY . .
+
+# Expose port
+EXPOSE 7860
+
+# Run the application
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

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 ---
-title: Ml Music Classifier Api
-emoji: 💻
-colorFrom: green
-colorTo: indigo
+title: ML Music Classifier API
+emoji: 🎵
+colorFrom: blue
+colorTo: green
 sdk: docker
 pinned: false
 license: mit
-short_description: FastAPI service that predicts music
+app_port: 7860
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# ML Music Classifier API
+
+This API predicts whether a song will be "liked" or "not liked" based on audio features using 8 different machine learning models.
+
+## Models Available
+- ANN (Artificial Neural Network)
+- KNN (K-Nearest Neighbors)
+- Logistic Regression
+- Neural Network
+- Naive Bayes
+- Random Forest
+- SVM (Support Vector Machine)
+- XGBoost
+
+## API Endpoints
+- `POST /predict/{model_name}` - Make predictions with specific model
+- `GET /docs` - API documentation
+
+## Authentication
+All endpoints require an API token in the `X-Token` header.
+
+## Usage
+```bash
+curl -X POST "https://your-space-name.hf.space/predict/random_forest_model" \
+     -H "X-Token: your-token-here" \
+     -H "Content-Type: application/json" \
+     -d '{"danceability": 0.8, "energy": 0.7, ...}'

app.py

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+# 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)

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requirements.txt

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+fastapi==0.104.1
+uvicorn[standard]==0.24.0
+numpy==1.24.3
+scikit-learn==1.3.0
+joblib==1.3.2
+pydantic==2.4.2
+python-multipart==0.0.6
+pandas==2.1.4
+keras==2.12.0
+tensorflow==2.12.0
+xgboost==2.0.3