--- a
+++ b/app.py
@@ -0,0 +1,55 @@
+from flask import Flask, request, jsonify, render_template
+import joblib
+import pandas as pd
+
+
+app = Flask(__name__)
+
+
+# Load the model
+model = joblib.load('diabetic_patients_readmission_model.pkl')
+
+
+# Specify the top features
+top_features = [
+    'number_inpatient', 'number_emergency', 'number_diagnoses',
+    'number_outpatient', 'diag_1_428', 'diabetesMed_Yes',
+    'num_medications', 'time_in_hospital'
+]
+
+
+@app.route('/')
+def index():
+    return render_template('index.html')
+
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    data = request.form.to_dict(flat=True)
+
+    # Convert string values to float and handle conversion errors
+    try:
+        data_converted = {key: float(value) for key, value in data.items()}
+    except ValueError:
+        error_message = "Please enter valid numeric values."
+        return render_template('index.html', error=error_message)
+
+    # Create DataFrame from converted data
+    df = pd.DataFrame([data_converted], columns=top_features)
+
+    # Check for negative values
+    if (df[top_features] < 0).any().any():
+        error_message = "Please enter non-negative values only."
+        return render_template('index.html', error=error_message)
+
+    # Replace NaN values if any
+    df.fillna(0, inplace=True)
+
+    prediction = model.predict(df)
+    prediction_value = prediction[0].item()
+
+    return render_template('index.html', prediction=prediction_value)
+
+
+if __name__ == '__main__':
+    app.run(debug=True)