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app.py

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+import streamlit as st
+import joblib
+import numpy as np
+import pandas as pd
+
+# Load the pre-trained model
+model = joblib.load("iris_model.pkl")
+
+# Define the mapping for iris species
+species = {0: "setosa", 1: "versicolor", 2: "virginica"}
+
+# App Title
+st.title("Iris Species Classifier")
+st.write("Enter the measurements of an Iris flower to predict its species.")
+
+# Input widgets for user to enter measurements
+sepal_length = st.number_input("Sepal Length (cm)", min_value=0.0, value=5.1)
+sepal_width  = st.number_input("Sepal Width (cm)", min_value=0.0, value=3.5)
+petal_length = st.number_input("Petal Length (cm)", min_value=0.0, value=1.4)
+petal_width  = st.number_input("Petal Width (cm)", min_value=0.0, value=0.2)
+
+if st.button("Predict"):
+    # Prepare the input as a 2D array for prediction
+    input_features = np.array([[sepal_length, sepal_width, petal_length, petal_width]])
+    prediction = model.predict(input_features)
+    st.success(f"The predicted Iris species is **{species[prediction[0]]}**.")
+
+
+### TO-DO: ADD A BAR CHART WITH THE MEASUREMENTS ENTERED.
+### Create a dataframe first with the input values. Then use streamlit's bar_chart function: https://docs.streamlit.io/develop/api-reference/charts/st.bar_chart
+    df = pd.DataFrame({
+            "Measurement": ["Sepal Length", "Sepal Width", "Petal Length", "Petal Width"],
+            "Value (cm)": [sepal_length, sepal_width, petal_length, petal_width]
+        })
+
+    # Use the measurement as index for better display
+    df.set_index("Measurement", inplace=True)
+
+    st.subheader("Entered Measurements")
+    st.bar_chart(df)
+

iris_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a5829ca86fd591898be1994c42c919f4f705748401e08ccf788ffeb8a50ae2f4
+size 943

requirements.txt

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+streamlit
+scikit-learn
+joblib
+numpy