Sync App files

routes/model.py

@@ -0,0 +1,50 @@
+from fastapi import APIRouter, Depends, HTTPException, Request, Response, UploadFile
+from pydantic import BaseModel, Field
+from typing import Literal
+from routes.task import predict_drug
+import skops.io as sio
+
+# Create an instance of the FastAPI class
+router = APIRouter(
+    prefix="/api",
+    tags=["api"],
+    responses={404: {"description": "Not found"}}
+)
+
+class PredictDrugInput(BaseModel):
+    age: int = Field(..., ge=15, le=74, description="Age of the patient (15 to 74)")
+    sex: Literal["M", "F"] = Field(..., description="Sex of the patient (M or F)")
+    blood_pressure: Literal["HIGH", "LOW", "NORMAL"] = Field(..., description="Blood pressure level")
+    cholesterol: Literal["HIGH", "NORMAL"] = Field(..., description="Cholesterol level")
+    na_to_k_ratio: float = Field(..., ge=6.2, le=38.2, description="Sodium-to-potassium ratio in blood (6.2 to 38.2)")
+    
+    model_config = {
+        "json_schema_extra": {
+            "examples": [
+                {
+                    "age": 30,
+                    "sex": "M",
+                    "blood_pressure": "HIGH",
+                    "cholesterol": "HIGH",
+                    "na_to_k_ratio": 10
+                }
+            ]
+        }
+    }
+
+# Define a GET endpoint
+@router.get("/")
+def read_root():
+    return {"message": "Hello, welcome to the demo"}
+
+@router.get("/get_perams")
+def get_perams():
+    pipe = sio.load("./Model/drug_pipeline.skops", trusted=True)
+    model = pipe.named_steps["model"]
+    model_params = model.get_params()
+    return model_params
+
+@router.post("/predict")
+def predict_ml(input_data: PredictDrugInput):
+    label = predict_drug(input_data.age, input_data.sex, input_data.blood_pressure, input_data.cholesterol, input_data.na_to_k_ratio)
+    return label

routes/task.py

@@ -0,0 +1,22 @@
+import skops.io as sio
+
+pipe = sio.load("./Model/drug_pipeline.skops", trusted=True)
+
+def predict_drug(age, sex, blood_pressure, cholesterol, na_to_k_ratio):
+    """Predict drugs based on patient features.
+
+    Args:
+        age (int): Age of patient
+        sex (str): Sex of patient 
+        blood_pressure (str): Blood pressure level
+        cholesterol (str): Cholesterol level
+        na_to_k_ratio (float): Ratio of sodium to potassium in blood
+
+    Returns:
+        str: Predicted drug label
+    """
+    features = [age, sex, blood_pressure, cholesterol, na_to_k_ratio]
+    predicted_drug = pipe.predict([features])[0]
+
+    label = f"Predicted Drug: {predicted_drug}"
+    return label