ml-service.py

#!/usr/bin/env python3
"""
Simplified FastAPI ML Microservice for testing
"""

import os
import sys
from typing import List
from datetime import datetime

# FastAPI imports
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import uvicorn

# Add ML services to Python path
ml_services_path = os.path.join(os.path.dirname(__file__), 'ml services')
sys.path.insert(0, ml_services_path)

# Import ML model
from student_risk_ml_python import StudentRiskMLModel

# Initialize FastAPI app
app = FastAPI(title="Student Risk ML Service", version="1.0.0")

# Global ML model
ml_model = None

# Load model at startup
try:
    print("🤖 Loading ML model...")
    ml_model = StudentRiskMLModel()
    
    model_path = os.path.join('ml services', 'models', 'student_risk_model.pkl')
    if os.path.exists(model_path):
        ml_model.load_model(model_path)
        print("✅ ML model loaded successfully")
    else:
        print("⚠️ Training new model...")
        ml_model.train_model()
        os.makedirs(os.path.join('ml services', 'models'), exist_ok=True)
        ml_model.save_model(model_path)
        print("✅ New model trained and saved")
        
except Exception as e:
    print(f"❌ Model loading error: {e}")
    ml_model = None

# Request models
class StudentData(BaseModel):
    studentId: str
    attendance: float
    gpa: float
    obtainedMarks: int
    totalMarks: int
    totalDue: float
    paid: float
    paidOnTime: bool

class PredictionRequest(BaseModel):
    students: List[StudentData]

# Endpoints
@app.get("/")
async def root():
    return {
        "service": "Student Risk ML Service",
        "status": "running",
        "model_loaded": ml_model is not None,
        "timestamp": datetime.now().isoformat()
    }

@app.get("/health")
async def health():
    return {
        "status": "healthy" if ml_model else "unhealthy",
        "model_loaded": ml_model is not None,
        "timestamp": datetime.now().isoformat()
    }

@app.post("/predict")
async def predict_batch(request: PredictionRequest):
    if not ml_model:
        raise HTTPException(status_code=503, detail="ML model not available")
    
    predictions = []
    for student in request.students:
        try:
            student_dict = student.dict()
            prediction = ml_model.predict_student_risk(student_dict)
            predictions.append({
                "studentId": student.studentId,
                "riskScore": float(prediction["riskScore"]),
                "riskLevel": prediction["riskLevel"],
                "riskFactors": prediction.get("riskFactors", [])
            })
        except Exception as e:
            print(f"Prediction error for {student.studentId}: {e}")
            predictions.append({
                "studentId": student.studentId,
                "riskScore": 0.5,
                "riskLevel": "Medium",
                "riskFactors": [f"Prediction error: {str(e)}"]
            })
    
    return {
        "predictions": predictions,
        "processed_count": len(predictions),
        "timestamp": datetime.now().isoformat()
    }

@app.post("/predict-single")
async def predict_single(student: StudentData):
    if not ml_model:
        raise HTTPException(status_code=503, detail="ML model not available")
    
    try:
        student_dict = student.dict()
        prediction = ml_model.predict_student_risk(student_dict)
        return {
            "studentId": student.studentId,
            "riskScore": float(prediction["riskScore"]),
            "riskLevel": prediction["riskLevel"],
            "riskFactors": prediction.get("riskFactors", []),
            "timestamp": datetime.now().isoformat()
        }
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Prediction failed: {str(e)}")

if __name__ == "__main__":
    print("🚀 Starting ML Microservice on http://localhost:8000")
    uvicorn.run(app, host="127.0.0.1", port=8000, log_level="info")