streamlit UI changes

app.py

@@ -3,7 +3,6 @@ import numpy as np
 import pandas as pd
 from sklearn.model_selection import train_test_split
 from sklearn.neighbors import KNeighborsClassifier
-
 def train_iris_model():
     df = pd.read_csv("Iris.csv")
     df['Species'] = df['Species'].map({'Iris-setosa': 0, 'Iris-virginica': 1, 'Iris-versicolor': 2})
@@ -29,10 +28,11 @@ def predict_iris_species(model, input_data):
 def main():
     st.title("Iris Species Prediction App")
 
-    sepal_length = st.slider("Sepal Length", 0.0, 10.0, 5.0)
-    sepal_width = st.slider("Sepal Width", 0.0, 10.0, 5.0)
-    petal_length = st.slider("Petal Length", 0.0, 10.0, 5.0)
-    petal_width = st.slider("Petal Width", 0.0, 10.0, 5.0)
+    st.sidebar.header("User Input")
+    sepal_length = st.sidebar.slider("Sepal Length", 0.0, 10.0, 5.0)
+    sepal_width = st.sidebar.slider("Sepal Width", 0.0, 10.0, 5.0)
+    petal_length = st.sidebar.slider("Petal Length", 0.0, 10.0, 5.0)
+    petal_width = st.sidebar.slider("Petal Width", 0.0, 10.0, 5.0)
 
     trained_model = train_iris_model()
     input_values = [sepal_length, sepal_width, petal_length, petal_width]
@@ -41,7 +41,14 @@ def main():
     species_mapping = {0: 'Iris-setosa', 1: 'Iris-virginica', 2: 'Iris-versicolor'}
     predicted_species = species_mapping.get(prediction_result[0], 'Unknown')
 
-    st.write(f"Predicted Species: {predicted_species}")
+    st.subheader("User Input Values:")
+    st.write(f"- Sepal Length: {sepal_length}")
+    st.write(f"- Sepal Width: {sepal_width}")
+    st.write(f"- Petal Length: {petal_length}")
+    st.write(f"- Petal Width: {petal_width}")
+
+    st.subheader("Prediction:")
+    st.success(f"Predicted Species: {predicted_species}")
 
 if __name__ == "__main__":
     main()