diff --git a/app.py b/app.py
new file mode 100644
index 0000000..8d6f6e8
--- /dev/null
+++ b/app.py
@@ -0,0 +1,57 @@
+from flask import Flask,request, url_for, redirect, render_template
+import pickle
+import numpy as np
+import requests
+
+app = Flask(__name__)
+
+model=pickle.load(open('models/RandomForest.pkl','rb'))
+
+
+@app.route('/')
+def hello_world():
+    return render_template("form.html")
+
+
+
+# @app.route('/submit', methods=['GET', 'POST'])
+# def predict():
+#     if request.method == 'POST':
+#         try:
+#             features = [float(x) for x in request.form.values()]
+#             final = [np.array(features)]
+#             prediction = model.predict(final)
+#             print("Prediction successful:", prediction)
+#             return render_template('prediction.html', pred=prediction)
+#         except Exception as e:
+#             print("An error occurred:", e)
+#             return "An error occurred while processing the prediction."
+#     else:
+#         # Handle GET request (if needed)
+#         return render_template("form.html")
+
+feature_names = ['no2','so2','pm2_5','pm10','Leq','DO','pH','BOD','Land_use','NBR']
+
+@app.route('/submit', methods=['GET','POST'])
+def submit():
+    try:
+        features=[float(x) for x in request.form.values()]
+        final=[np.array(features)]
+        print("Input features:", final)
+        prediction = model.predict(final)
+        output=int(prediction[0])
+        print(output)
+        return render_template('prediction.html', result=output)
+    except Exception as e:
+        print("An error occurred:", e)
+        return f"An error occurred while processing the prediction: {str(e)}"
+
+@app.route('/predict_api',methods=['POST'])
+def predict_api():
+    data=request.get_json(force=True)
+    prediction=model.predict([np.array(list(data.values()))])
+    output=prediction[0]
+    return jsonify(output)
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=3000)
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