created app.py

app.py

@@ -0,0 +1,44 @@
+import streamlit as st
+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})
+    del df["Id"]
+    X = df.loc[:, df.columns != 'Species']
+    y = df['Species']
+    X_train, _, y_train, _ = train_test_split(X, y, test_size=0.3, random_state=42)
+
+    model = KNeighborsClassifier()
+    model.fit(X_train, y_train)
+
+    return model
+
+def predict_iris_species(model, input_data):
+    # Reshape the input data to (1, -1) to make it compatible with model.predict
+    input_data = np.array(input_data).reshape(1, -1)
+
+    # Make predictions using the trained model
+    prediction = model.predict(input_data)
+
+    return prediction
+
+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)
+
+    trained_model = train_iris_model()
+    input_values = [sepal_length, sepal_width, petal_length, petal_width]
+    prediction_result = predict_iris_species(trained_model, input_values)
+
+    st.write(f"Predicted Species: {prediction_result[0]}")
+
+if __name__ == "__main__":
+    main()