Update app.py

app.py

@@ -0,0 +1,62 @@
+from fastapi import FastAPI, Query, Request
+from fastapi.responses import HTMLResponse
+from fastapi.templating import Jinja2Templates
+import joblib 
+
+app = FastAPI()
+templates = Jinja2Templates(directory="templates")
+
+# Load the pickled XGBoost model
+xgb_model = joblib.load("xgb_model.joblib")
+
+@app.get("/display")
+def display_params(
+    request: Request,
+    prg: float = Query(..., description="Plasma glucose"),
+    pl: float = Query(..., description="Blood Work Result-1 (mu U/ml)"),
+    pr: float = Query(..., description="Blood Pressure (mm Hg)"),
+    sk: float = Query(..., description="Blood Work Result-2 (mm)"),
+    ts: float = Query(..., description="Blood Work Result-3 (mu U/ml)"),
+    m11: float = Query(..., description="Body mass index (weight in kg/(height in m)^2"),
+    bd2: float = Query(..., description="Blood Work Result-4 (mu U/ml)"),
+    age: int = Query(..., description="Patient's age (years)")
+):
+    # Prepare input features for prediction
+    input_features = [prg, pl, pr, sk, ts, m11, bd2, age]
+
+    # Make predictions using the loaded model
+    prediction = xgb_model.predict_proba([input_features])[0]
+
+    # Create a JSON response 
+    response = {
+        "request": {
+            "prg": prg,
+            "pl": pl,
+            "pr": pr,
+            "sk": sk,
+            "ts": ts,
+            "m11": m11,
+            "bd2": bd2,
+            "age": age
+        },
+        "prediction": {
+            "class_0_probability": prediction[0],
+            "class_1_probability": prediction[1]               
+        }
+    }
+
+    return templates.TemplateResponse(
+        "display_params.html",
+        {
+            "request": request,
+            "prg": prg,
+            "pl": pl,
+            "pr": pr,
+            "sk": sk,
+            "ts": ts,
+            "m11": m11,
+            "bd2": bd2,
+            "age": age,
+            "prediction": response["prediction"]
+        }
+    )