Upload 4 files

Iris.csv

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+Id,SepalLengthCm,SepalWidthCm,PetalLengthCm,PetalWidthCm,Species
+1,5.1,3.5,1.4,0.2,Iris-setosa
+2,4.9,3.0,1.4,0.2,Iris-setosa
+3,4.7,3.2,1.3,0.2,Iris-setosa
+4,4.6,3.1,1.5,0.2,Iris-setosa
+5,5.0,3.6,1.4,0.2,Iris-setosa
+6,5.4,3.9,1.7,0.4,Iris-setosa
+7,4.6,3.4,1.4,0.3,Iris-setosa
+8,5.0,3.4,1.5,0.2,Iris-setosa
+9,4.4,2.9,1.4,0.2,Iris-setosa
+10,4.9,3.1,1.5,0.1,Iris-setosa
+11,5.4,3.7,1.5,0.2,Iris-setosa
+12,4.8,3.4,1.6,0.2,Iris-setosa
+13,4.8,3.0,1.4,0.1,Iris-setosa
+14,4.3,3.0,1.1,0.1,Iris-setosa
+15,5.8,4.0,1.2,0.2,Iris-setosa
+16,5.7,4.4,1.5,0.4,Iris-setosa
+17,5.4,3.9,1.3,0.4,Iris-setosa
+18,5.1,3.5,1.4,0.3,Iris-setosa
+19,5.7,3.8,1.7,0.3,Iris-setosa
+20,5.1,3.8,1.5,0.3,Iris-setosa
+21,5.4,3.4,1.7,0.2,Iris-setosa
+22,5.1,3.7,1.5,0.4,Iris-setosa
+23,4.6,3.6,1.0,0.2,Iris-setosa
+24,5.1,3.3,1.7,0.5,Iris-setosa
+25,4.8,3.4,1.9,0.2,Iris-setosa
+26,5.0,3.0,1.6,0.2,Iris-setosa
+27,5.0,3.4,1.6,0.4,Iris-setosa
+28,5.2,3.5,1.5,0.2,Iris-setosa
+29,5.2,3.4,1.4,0.2,Iris-setosa
+30,4.7,3.2,1.6,0.2,Iris-setosa
+31,4.8,3.1,1.6,0.2,Iris-setosa
+32,5.4,3.4,1.5,0.4,Iris-setosa
+33,5.2,4.1,1.5,0.1,Iris-setosa
+34,5.5,4.2,1.4,0.2,Iris-setosa
+35,4.9,3.1,1.5,0.1,Iris-setosa
+36,5.0,3.2,1.2,0.2,Iris-setosa
+37,5.5,3.5,1.3,0.2,Iris-setosa
+38,4.9,3.1,1.5,0.1,Iris-setosa
+39,4.4,3.0,1.3,0.2,Iris-setosa
+40,5.1,3.4,1.5,0.2,Iris-setosa
+41,5.0,3.5,1.3,0.3,Iris-setosa
+42,4.5,2.3,1.3,0.3,Iris-setosa
+43,4.4,3.2,1.3,0.2,Iris-setosa
+44,5.0,3.5,1.6,0.6,Iris-setosa
+45,5.1,3.8,1.9,0.4,Iris-setosa
+46,4.8,3.0,1.4,0.3,Iris-setosa
+47,5.1,3.8,1.6,0.2,Iris-setosa
+48,4.6,3.2,1.4,0.2,Iris-setosa
+49,5.3,3.7,1.5,0.2,Iris-setosa
+50,5.0,3.3,1.4,0.2,Iris-setosa
+51,7.0,3.2,4.7,1.4,Iris-versicolor
+52,6.4,3.2,4.5,1.5,Iris-versicolor
+53,6.9,3.1,4.9,1.5,Iris-versicolor
+54,5.5,2.3,4.0,1.3,Iris-versicolor
+55,6.5,2.8,4.6,1.5,Iris-versicolor
+56,5.7,2.8,4.5,1.3,Iris-versicolor
+57,6.3,3.3,4.7,1.6,Iris-versicolor
+58,4.9,2.4,3.3,1.0,Iris-versicolor
+59,6.6,2.9,4.6,1.3,Iris-versicolor
+60,5.2,2.7,3.9,1.4,Iris-versicolor
+61,5.0,2.0,3.5,1.0,Iris-versicolor
+62,5.9,3.0,4.2,1.5,Iris-versicolor
+63,6.0,2.2,4.0,1.0,Iris-versicolor
+64,6.1,2.9,4.7,1.4,Iris-versicolor
+65,5.6,2.9,3.6,1.3,Iris-versicolor
+66,6.7,3.1,4.4,1.4,Iris-versicolor
+67,5.6,3.0,4.5,1.5,Iris-versicolor
+68,5.8,2.7,4.1,1.0,Iris-versicolor
+69,6.2,2.2,4.5,1.5,Iris-versicolor
+70,5.6,2.5,3.9,1.1,Iris-versicolor
+71,5.9,3.2,4.8,1.8,Iris-versicolor
+72,6.1,2.8,4.0,1.3,Iris-versicolor
+73,6.3,2.5,4.9,1.5,Iris-versicolor
+74,6.1,2.8,4.7,1.2,Iris-versicolor
+75,6.4,2.9,4.3,1.3,Iris-versicolor
+76,6.6,3.0,4.4,1.4,Iris-versicolor
+77,6.8,2.8,4.8,1.4,Iris-versicolor
+78,6.7,3.0,5.0,1.7,Iris-versicolor
+79,6.0,2.9,4.5,1.5,Iris-versicolor
+80,5.7,2.6,3.5,1.0,Iris-versicolor
+81,5.5,2.4,3.8,1.1,Iris-versicolor
+82,5.5,2.4,3.7,1.0,Iris-versicolor
+83,5.8,2.7,3.9,1.2,Iris-versicolor
+84,6.0,2.7,5.1,1.6,Iris-versicolor
+85,5.4,3.0,4.5,1.5,Iris-versicolor
+86,6.0,3.4,4.5,1.6,Iris-versicolor
+87,6.7,3.1,4.7,1.5,Iris-versicolor
+88,6.3,2.3,4.4,1.3,Iris-versicolor
+89,5.6,3.0,4.1,1.3,Iris-versicolor
+90,5.5,2.5,4.0,1.3,Iris-versicolor
+91,5.5,2.6,4.4,1.2,Iris-versicolor
+92,6.1,3.0,4.6,1.4,Iris-versicolor
+93,5.8,2.6,4.0,1.2,Iris-versicolor
+94,5.0,2.3,3.3,1.0,Iris-versicolor
+95,5.6,2.7,4.2,1.3,Iris-versicolor
+96,5.7,3.0,4.2,1.2,Iris-versicolor
+97,5.7,2.9,4.2,1.3,Iris-versicolor
+98,6.2,2.9,4.3,1.3,Iris-versicolor
+99,5.1,2.5,3.0,1.1,Iris-versicolor
+100,5.7,2.8,4.1,1.3,Iris-versicolor
+101,6.3,3.3,6.0,2.5,Iris-virginica
+102,5.8,2.7,5.1,1.9,Iris-virginica
+103,7.1,3.0,5.9,2.1,Iris-virginica
+104,6.3,2.9,5.6,1.8,Iris-virginica
+105,6.5,3.0,5.8,2.2,Iris-virginica
+106,7.6,3.0,6.6,2.1,Iris-virginica
+107,4.9,2.5,4.5,1.7,Iris-virginica
+108,7.3,2.9,6.3,1.8,Iris-virginica
+109,6.7,2.5,5.8,1.8,Iris-virginica
+110,7.2,3.6,6.1,2.5,Iris-virginica
+111,6.5,3.2,5.1,2.0,Iris-virginica
+112,6.4,2.7,5.3,1.9,Iris-virginica
+113,6.8,3.0,5.5,2.1,Iris-virginica
+114,5.7,2.5,5.0,2.0,Iris-virginica
+115,5.8,2.8,5.1,2.4,Iris-virginica
+116,6.4,3.2,5.3,2.3,Iris-virginica
+117,6.5,3.0,5.5,1.8,Iris-virginica
+118,7.7,3.8,6.7,2.2,Iris-virginica
+119,7.7,2.6,6.9,2.3,Iris-virginica
+120,6.0,2.2,5.0,1.5,Iris-virginica
+121,6.9,3.2,5.7,2.3,Iris-virginica
+122,5.6,2.8,4.9,2.0,Iris-virginica
+123,7.7,2.8,6.7,2.0,Iris-virginica
+124,6.3,2.7,4.9,1.8,Iris-virginica
+125,6.7,3.3,5.7,2.1,Iris-virginica
+126,7.2,3.2,6.0,1.8,Iris-virginica
+127,6.2,2.8,4.8,1.8,Iris-virginica
+128,6.1,3.0,4.9,1.8,Iris-virginica
+129,6.4,2.8,5.6,2.1,Iris-virginica
+130,7.2,3.0,5.8,1.6,Iris-virginica
+131,7.4,2.8,6.1,1.9,Iris-virginica
+132,7.9,3.8,6.4,2.0,Iris-virginica
+133,6.4,2.8,5.6,2.2,Iris-virginica
+134,6.3,2.8,5.1,1.5,Iris-virginica
+135,6.1,2.6,5.6,1.4,Iris-virginica
+136,7.7,3.0,6.1,2.3,Iris-virginica
+137,6.3,3.4,5.6,2.4,Iris-virginica
+138,6.4,3.1,5.5,1.8,Iris-virginica
+139,6.0,3.0,4.8,1.8,Iris-virginica
+140,6.9,3.1,5.4,2.1,Iris-virginica
+141,6.7,3.1,5.6,2.4,Iris-virginica
+142,6.9,3.1,5.1,2.3,Iris-virginica
+143,5.8,2.7,5.1,1.9,Iris-virginica
+144,6.8,3.2,5.9,2.3,Iris-virginica
+145,6.7,3.3,5.7,2.5,Iris-virginica
+146,6.7,3.0,5.2,2.3,Iris-virginica
+147,6.3,2.5,5.0,1.9,Iris-virginica
+148,6.5,3.0,5.2,2.0,Iris-virginica
+149,6.2,3.4,5.4,2.3,Iris-virginica
+150,5.9,3.0,5.1,1.8,Iris-virginica

app.py

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+import streamlit as st
+from sklearn.tree import DecisionTreeClassifier
+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split
+
+# Create a decision tree classifier
+clf = DecisionTreeClassifier()
+
+# Load the data from an external CSV file
+@st.cache
+def load_data():
+    # Replace "data.csv" with the path to your external CSV file
+    data = pd.read_csv("iris.csv")
+    return data
+
+data = load_data()
+
+# Separate features and target variable
+X = data.drop(["Id", "Species"], axis=1)
+y = data["Species"]
+class_names = np.unique(y)
+
+# Perform train-test split
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Train the classifier
+clf.fit(X_train, y_train)
+
+# Define the Streamlit app
+def main():
+    # Set the title and the sidebar
+    st.title("Iris Species Classifier")
+    st.sidebar.title("Options")
+
+    # Add inputs for Sepal and Petal measurements
+    sepal_length = st.sidebar.slider("Sepal Length (cm)", float(X["SepalLengthCm"].min()), float(X["SepalLengthCm"].max()))
+    sepal_width = st.sidebar.slider("Sepal Width (cm)", float(X["SepalWidthCm"].min()), float(X["SepalWidthCm"].max()))
+    petal_length = st.sidebar.slider("Petal Length (cm)", float(X["PetalLengthCm"].min()), float(X["PetalLengthCm"].max()))
+    petal_width = st.sidebar.slider("Petal Width (cm)", float(X["PetalWidthCm"].min()), float(X["PetalWidthCm"].max()))
+
+    # Create a numpy array for the input features
+    input_data = np.array([[sepal_length, sepal_width, petal_length, petal_width]])
+
+    # Make predictions using the classifier
+    prediction = clf.predict(input_data)
+
+    # Display the predicted class
+    st.write(f"Predicted Class: {prediction[0]}")
+
+# Run the Streamlit app
+if __name__ == "__main__":
+    main()

requirements.txt

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+numpy
+pandas
+scikit_learn
+streamlit