Upload app.py

app.py

@@ -0,0 +1,25 @@
+import streamlit as st
+import pickle
+import pandas as pd
+
+st.title("Classification the Iris")
+
+sepal_length = st.text_input('Sepal_Length (cm)')
+sepal_width = st.text_input('Sepal_Width (cm)')
+petal_length = st.text_input('Petal_Length (cm)')
+petal_width = st.text_input('Petal_Width (cm)')
+
+dataframe = pd.DataFrame({"sepal length (cm)":[sepal_length],"sepal width (cm)":[sepal_width],'petal length (cm)':[petal_length],'petal width (cm)':[petal_width]})
+
+if st.button('Prediction'):
+    with open('model.pkl', 'rb') as file:
+        loaded_model = pickle.load(file)
+        final_output = loaded_model.predict(dataframe)
+        if final_output == '0':
+            st.write("The output class is setosa")
+        elif final_output == '1':
+            st.write("The output class is versicolor")
+        elif final_output == '2':
+            st.write("The output class is virginica")
+
+