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app.py

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+import streamlit as st
+import numpy as np
+import pickle
+from sklearn.preprocessing import StandardScaler
+
+# Load the trained model
+with open('iris_classifier_model.pkl', 'rb') as f:
+    model = pickle.load(f)
+
+# Load the scaler
+with open('scaler.pkl', 'rb') as f:
+    scaler = pickle.load(f)
+
+# Define the class names
+class_names = ['Setosa', 'Versicolor', 'Virginica']
+
+def predict_species(features):
+    # Standardize the features
+    features = np.array(features).reshape(1, -1)
+    features = scaler.transform(features)
+    
+    # Make prediction
+    prediction = model.predict(features)
+    predicted_species = class_names[prediction[0]]
+    return predicted_species
+
+# Streamlit app
+st.title("Iris Species Classifier")
+st.write("Enter the features of the Iris flower to classify its species:")
+
+# Input fields for the features
+sepal_length = st.number_input('Sepal Length (cm)', min_value=0.0, max_value=10.0, value=5.0)
+sepal_width = st.number_input('Sepal Width (cm)', min_value=0.0, max_value=10.0, value=3.0)
+petal_length = st.number_input('Petal Length (cm)', min_value=0.0, max_value=10.0, value=4.0)
+petal_width = st.number_input('Petal Width (cm)', min_value=0.0, max_value=10.0, value=1.0)
+
+# Button to make the prediction
+if st.button('Predict'):
+    features = [sepal_length, sepal_width, petal_length, petal_width]
+    species = predict_species(features)
+    st.write(f'The predicted species is: **{species}**')

iris_classifier_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ea02d9bda0503a7a6e3ce99394dec80dc7e84bf35a94eacfe679b54c89b1b7b6
+size 177217

model.py

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+import numpy as np
+import pickle
+from sklearn.datasets import load_iris
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from sklearn.ensemble import RandomForestClassifier
+
+# Load the dataset
+iris = load_iris()
+X, y = iris.data, iris.target
+
+# Split the data into training and test sets
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Standardize the data
+scaler = StandardScaler()
+X_train = scaler.fit_transform(X_train)
+X_test = scaler.transform(X_test)
+
+# Save the scaler
+with open('scaler.pkl', 'wb') as f:
+    pickle.dump(scaler, f)
+
+# Train the RandomForestClassifier
+model = RandomForestClassifier(n_estimators=100, random_state=42)
+model.fit(X_train, y_train)
+
+# Save the model
+with open('iris_classifier_model.pkl', 'wb') as f:
+    pickle.dump(model, f)

requirements.txt

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+numpy==1.24.3
+scikit-learn==1.2.2
+pandas==1.5.3
+streamlit==1.15.1
+distutils

scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c8b83a49565446fb922c1f6ca54743848eb0184bd7d4f9a974137fdf2de2769e
+size 545