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app.py

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+from streamlit_pandas_profiling import st_profile_report
+from ydata_profiling import ProfileReport
+import streamlit as st
+import pandas as pd
+import numpy as np
+from prediction import predict
+from sklearn.datasets import load_iris
+from ydata_profiling.utils.cache import cache_file
+
+st.set_page_config(layout="wide")
+st.title('Iris Flowers - Classification')
+st.caption('Created by Bayhaqy')
+st.markdown('Classify iris flowers into \
+setosa, versicolor, virginica')
+
+st.image('https://machinelearninghd.com/wp-content/uploads/2021/03/iris-dataset.png')
+st.image('https://www.integratedots.com/wp-content/uploads/2019/06/iris_petal-sepal-e1560211020463.png')
+
+# Load Dataset
+#iris = load_iris(as_frame=True)
+
+@st.cache_data
+def load_data(url):
+    df = pd.read_csv(url)
+    return df
+
+iris = cache_file(
+  'Iris.csv',
+  'https://raw.githubusercontent.com/bayhaqy/Classification-Iris-Prediction/main/Iris.csv',
+)
+
+df = load_data(iris)
+
+# Create a DataFrame from the iris data
+#df = pd.DataFrame(iris.data, columns=iris.feature_names)
+
+# Add a target column to the DataFrame
+#df['Target'] = iris['target']
+
+# Translate the target
+#df['Target'] = df['Target'].apply(lambda x: iris['target_names'][x])
+
+st.header('Plant Features')
+col1, col2 = st.columns(2)
+with col1:
+  st.text('Sepal Size')
+  sepal_l = st.slider('Sepal lenght (cm)', 1.0, 8.0, 0.5)
+  sepal_w = st.slider('Sepal width (cm)', 2.0, 4.4, 0.5)
+
+with col2:
+  st.text('Pepal Size')
+  petal_l = st.slider('Petal lenght (cm)', 1.0, 7.0, 0.5)
+  petal_w = st.slider('Petal width (cm)', 0.1, 2.5, 0.5)
+
+if st.button('Predict type of Iris'):
+  result = predict(np.array([[sepal_l, sepal_w, petal_l, petal_w]]))
+  st.text(result[0])
+
+st.write("---")
+if st.checkbox("Sample preview the Iris Dataset"):
+    #st.write(df.sample(10))  # Same as st.write(df)
+    pr = ProfileReport(df,title="Dataset Report")
+    st_profile_report(pr)
+
+st.write("---")

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ba4bce9f362b6cf4fbf1de5c2292cab5e11f854a7da038eadf23ec2b82648341
+size 4228

prediction.py

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+import streamlit as st
+import pickle
+
+@st.cache_resource
+def predict(data):
+  model = pickle.load(open('model.pkl', 'rb'))
+  return model.predict(data)

requirements.txt

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+matplotlib
+ydata-profiling
+streamlit-pandas-profiling
+streamlit
+scikit-learn
+pandas
+pandas-profiling