Create app.py

app.py

@@ -0,0 +1,27 @@
+import pickle
+import sklearn
+import numpy as np
+import pandas as pd
+import streamlit as st
+
+model_file = 'model-6.pkl'
+try:
+    with open(model_file,'rb') as file:
+        model = pickle.load(file)
+except FileNotFoundError:
+    st.error("The file was not found or may be its corrupted")
+
+st.title("IRIS FLOWER CLASSIFICATION")
+st.header("Enter the features for your prediction")
+
+sepal_length = st.number_input("Sepal length (cm):",min_value=0.0,max_value=10.0)
+sepal_width = st.number_input("Sepal Width (cm):",min_value=0.0,max_value=10.0)
+petal_length = st.number_input("Petal length (cm):",min_value=0.0,max_value=10.0)
+petal_width = st.number_input("Petal Width (cm):",min_value=0.0,max_value=10.0)
+
+if st.button("PREDICT") :
+    features = np.array([[sepal_length,sepal_width,petal_length,petal_width]])
+    prediction = model.predict(features)[0]
+
+    st.subheader("Prediction has been made: ")
+    st.write("The prediction for your given parameters is:",prediction)