Update app.py

app.py

@@ -1,63 +1,85 @@
-from fastapi import FastAPI, Request
-import pandas as pd
-import joblib
-import numpy as np
-
-app = FastAPI()
-
-# Load the model
-print("Loading model...")
-stroke_model = joblib.load("model.joblib")
-encoded_cols = stroke_model["encoded_cols"]
-numeric_cols = stroke_model["numeric_cols"]
-preprocessor = stroke_model["preprocessor"]
-
-@app.post("/")
-async def predict(request: Request):
-    """
-    Make a stroke risk prediction based on input features
-    Example input:
-    {
-        "gender": "Male",
-        "age": 67,
-        "hypertension": 1,
-        "heart_disease": 0,
-        "ever_married": "Yes",
-        "work_type": "Private",
-        "Residence_type": "Urban",
-        "avg_glucose_level": 228.69,
-        "bmi": 36.6,
-        "smoking_status": "formerly smoked"
-    }
-    """
-    data = await request.json()
-    
-    # Convert to DataFrame
-    input_df = pd.DataFrame([data])
-    
-    # Preprocess the input
-    processed_df = pd.DataFrame()
-    for col in numeric_cols:
-        processed_df[col] = input_df[col]
-    processed_df[encoded_cols] = preprocessor.transform(input_df)
-    X = processed_df[numeric_cols + encoded_cols]
-    
-    # Get prediction probability
-    prediction_proba = stroke_model['model'].predict_proba(X)[0, 1]
-    
-    # Classify risk level
-    risk_level = "Very Low Risk"
-    if prediction_proba > 0.8:
-        risk_level = "Very High Risk"
-    elif prediction_proba > 0.6:
-        risk_level = "High Risk"
-    elif prediction_proba > 0.4:
-        risk_level = "Moderate Risk"
-    elif prediction_proba > 0.2:
-        risk_level = "Low Risk"
-    
-    return {
-        "probability": float(prediction_proba),
-        "prediction": risk_level,
-        "stroke_prediction": int(prediction_proba > 0.5)
+from fastapi import FastAPI, Request
+import pandas as pd
+import joblib
+import numpy as np
+
+app = FastAPI()
+
+# Load the model
+print("Loading model...")
+stroke_model = joblib.load("model.joblib")
+encoded_cols = stroke_model["encoded_cols"]
+numeric_cols = stroke_model["numeric_cols"]
+preprocessor = stroke_model["preprocessor"]
+
+@app.get("/")
+async def root():
+    """
+    Root endpoint for health check and documentation
+    """
+    return {
+        "message": "Stroke Prediction API is running",
+        "usage": "Send a POST request to / with patient data",
+        "example": {
+            "gender": "Male",
+            "age": 67,
+            "hypertension": 1,
+            "heart_disease": 0,
+            "ever_married": "Yes",
+            "work_type": "Private",
+            "Residence_type": "Urban",
+            "avg_glucose_level": 228.69,
+            "bmi": 36.6,
+            "smoking_status": "formerly smoked"
+        }
+    }
+
+@app.post("/")
+async def predict(request: Request):
+    """
+    Make a stroke risk prediction based on input features
+    Example input:
+    {
+        "gender": "Male",
+        "age": 67,
+        "hypertension": 1,
+        "heart_disease": 0,
+        "ever_married": "Yes",
+        "work_type": "Private",
+        "Residence_type": "Urban",
+        "avg_glucose_level": 228.69,
+        "bmi": 36.6,
+        "smoking_status": "formerly smoked"
+    }
+    """
+    data = await request.json()
+    
+    # Convert to DataFrame
+    input_df = pd.DataFrame([data])
+    
+    # Preprocess the input
+    processed_df = pd.DataFrame()
+    for col in numeric_cols:
+        processed_df[col] = input_df[col]
+    processed_df[encoded_cols] = preprocessor.transform(input_df)
+    X = processed_df[numeric_cols + encoded_cols]
+    
+    # Get prediction probability
+    prediction_proba = stroke_model['model'].predict_proba(X)[0, 1]
+    
+    # Classify risk level
+    risk_level = "Very Low Risk"
+    if prediction_proba > 0.8:
+        risk_level = "Very High Risk"
+    elif prediction_proba > 0.6:
+        risk_level = "High Risk"
+    elif prediction_proba > 0.4:
+        risk_level = "Moderate Risk"
+    elif prediction_proba > 0.2:
+        risk_level = "Low Risk"
+    
+    return {
+        "probability": float(prediction_proba),
+        "prediction": risk_level,
+        "stroke_prediction": int(prediction_proba > 0.5)
     }