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fastapi_app.py

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+"""
+Simple FastAPI REST API for Milk Spoilage Classification
+
+This provides a clean REST endpoint for Custom GPT and other integrations.
+"""
+
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel, Field
+import joblib
+import numpy as np
+from typing import Dict
+
+# Load model
+model = joblib.load("model.joblib")
+
+# Create FastAPI app
+app = FastAPI(
+    title="Milk Spoilage Classification API",
+    description="Predict milk spoilage type based on microbial count data",
+    version="1.0.0"
+)
+
+# Add CORS middleware
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Request/Response models
+class PredictionInput(BaseModel):
+    spc_d7: float = Field(..., description="Standard Plate Count at Day 7 (log CFU/mL)", ge=0.0, le=10.0)
+    spc_d14: float = Field(..., description="Standard Plate Count at Day 14 (log CFU/mL)", ge=0.0, le=10.0)
+    spc_d21: float = Field(..., description="Standard Plate Count at Day 21 (log CFU/mL)", ge=0.0, le=10.0)
+    tgn_d7: float = Field(..., description="Total Gram-Negative at Day 7 (log CFU/mL)", ge=0.0, le=10.0)
+    tgn_d14: float = Field(..., description="Total Gram-Negative at Day 14 (log CFU/mL)", ge=0.0, le=10.0)
+    tgn_d21: float = Field(..., description="Total Gram-Negative at Day 21 (log CFU/mL)", ge=0.0, le=10.0)
+    
+    class Config:
+        json_schema_extra = {
+            "example": {
+                "spc_d7": 4.0,
+                "spc_d14": 5.0,
+                "spc_d21": 6.0,
+                "tgn_d7": 3.0,
+                "tgn_d14": 4.0,
+                "tgn_d21": 5.0
+            }
+        }
+
+class PredictionOutput(BaseModel):
+    prediction: str = Field(..., description="Predicted spoilage class")
+    probabilities: Dict[str, float] = Field(..., description="Probability for each class")
+    confidence: float = Field(..., description="Confidence score (max probability)")
+
+
+@app.get("/")
+async def root():
+    """Root endpoint with API information."""
+    return {
+        "message": "Milk Spoilage Classification API",
+        "endpoints": {
+            "predict": "/predict",
+            "health": "/health",
+            "docs": "/docs"
+        }
+    }
+
+
+@app.post("/predict", response_model=PredictionOutput, tags=["Prediction"])
+async def predict(input_data: PredictionInput):
+    """
+    Predict milk spoilage type based on microbial counts.
+    
+    Returns the predicted class, probabilities for all classes, and confidence score.
+    """
+    # Prepare features
+    features = np.array([[
+        input_data.spc_d7, input_data.spc_d14, input_data.spc_d21,
+        input_data.tgn_d7, input_data.tgn_d14, input_data.tgn_d21
+    ]])
+    
+    # Make prediction
+    prediction = model.predict(features)[0]
+    probabilities = model.predict_proba(features)[0]
+    
+    # Format response
+    prob_dict = {
+        str(cls): float(prob) 
+        for cls, prob in zip(model.classes_, probabilities)
+    }
+    
+    return PredictionOutput(
+        prediction=str(prediction),
+        probabilities=prob_dict,
+        confidence=float(max(probabilities))
+    )
+
+
+@app.get("/health", tags=["Health"])
+async def health_check():
+    """Health check endpoint."""
+    return {
+        "status": "healthy",
+        "model_loaded": model is not None,
+        "classes": model.classes_.tolist()
+    }
+
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)