Create app.py

app.py

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+from fastapi import FastAPI, Form, Request
+from fastapi.responses import HTMLResponse
+from fastapi.templating import Jinja2Templates
+import joblib
+import numpy as np
+from sklearn.preprocessing import StandardScaler
+
+# Initialize FastAPI app
+app = FastAPI()
+
+# Load saved models
+logistic_regression_model = joblib.load('logistic_regression_model.pkl')
+svm_model = joblib.load('svm_model.pkl')
+rfc_model = joblib.load('random_forest_model.pkl')
+knn_model = joblib.load('knn_model.pkl')
+neural_network_model = joblib.load('neural_network_model.pkl')
+
+# Load scaler (assuming you saved it as scaler.pkl)
+scaler = joblib.load('scaler.pkl')
+
+# Jinja2 template renderer
+templates = Jinja2Templates(directory="templates")
+
+# Define function to make predictions
+def make_prediction(model, data):
+    prediction = model.predict([data])
+    return prediction[0]
+
+# Home page route
+@app.get("/", response_class=HTMLResponse)
+async def home(request: Request):
+    return templates.TemplateResponse("index.html", {"request": request})
+
+# Prediction route
+@app.post("/predict", response_class=HTMLResponse)
+async def predict(request: Request, variance: float = Form(...), skewness: float = Form(...), 
+                  curtosis: float = Form(...), entropy: float = Form(...)):
+    # Prepare the feature vector
+    features = np.array([variance, skewness, curtosis, entropy])
+
+    # Scale the input features
+    scaled_features = scaler.transform([features])
+
+    # Make predictions using each model
+    logistic_regression_prediction = make_prediction(logistic_regression_model, scaled_features)
+    svm_prediction = make_prediction(svm_model, scaled_features)
+    rfc_prediction = make_prediction(rfc_model, scaled_features)
+    knn_prediction = make_prediction(knn_model, scaled_features)
+    nn_prediction = make_prediction(neural_network_model, scaled_features)
+
+    # Render the results page with predictions
+    return templates.TemplateResponse("result.html", {
+        "request": request,
+        "logistic_regression": logistic_regression_prediction,
+        "svm": svm_prediction,
+        "random_forest": rfc_prediction,
+        "knn": knn_prediction,
+        "neural_network": nn_prediction
+    })