Update app.py

app.py

@@ -1,91 +1,68 @@
-from operator import index
 import streamlit as st
-import plotly.express as px
-import numpy as np
-from lazypredict.Supervised import LazyRegressor, LazyClassifier
-from sklearn.model_selection import train_test_split
 import pandas as pd
-from autoviz.AutoViz_Class import AutoViz_Class
-from sklearn.datasets import load_iris
-import os
+import seaborn as sns
+import matplotlib.pyplot as plt
 
-@st.cache_resource
-def load_data():
-    X, y = load_iris(return_X_y=True)
-    df = pd.DataFrame(X, columns=["sepal_length", "sepal_width", "petal_length", "petal_width"])
-    df['target'] = y
-    return df
+# Load the Iris dataset
+iris_df = sns.load_dataset('iris')
 
-df = load_data()
+# Sidebar for file upload and dataset selection
+st.sidebar.title('Upload CSV File')
+uploaded_file = st.sidebar.file_uploader("Choose a CSV file", type=['csv'])
 
-with st.sidebar:
-    st.image("https://analyticsindiamag.com/wp-content/uploads/2020/10/lazypredict.jpg")
-    st.title("AutoML")
-    choice = st.radio("Navigation", ["Upload", "Profiling", "Modelling", "Download"])
-    st.info("This project application helps you build and explore your data.")
+if uploaded_file is not None:
+    # Read the uploaded file
+    custom_df = pd.read_csv(uploaded_file)
 
-if choice == "Upload":
-    st.title("Upload Your Dataset")
-    file = st.file_uploader("Upload Your Dataset")
-    if file:
-        df = pd.read_csv(file, index_col=None)
-        df.to_csv('dataset.csv', index=None)
-        st.dataframe(df)
+    # Display the uploaded dataset
+    st.write('**Uploaded Dataset:**')
+    st.write(custom_df.head())
 
-if choice == "Profiling":
-    st.title("Exploratory Data Analysis")
-    # AV = AutoViz_Class()
-    # config = {
-    #     'filename': None,
-    #     'sep': ',',
-    #     'depVar': '',
-    #     'dfte': df,
-    #     'header': 0,
-    #     'verbose': 0,
-    #     'lowess': False,
-    #     'chart_format': 'html',
-    #     'max_rows_analyzed': 10000,
-    #     'max_cols_analyzed': 50,
-    #     # 'save_plot_path': None,
-    # }
-    # AV.AutoViz()
-    # st.components.v1.html(AV.html, width=1000, height=1000, scrolling=True)
-    
-    AV = AutoViz_Class()
-    AV.AutoViz("",dfte=df)
-    report_path = os.path.join(os.path.dirname(__file__), "autoviz_report.html")
-    try:
-        AV.save_html(report_path)
-        HtmlFile = open(report_path, 'r', encoding='utf-8')
-        source_code = HtmlFile.read()
-        st.components.v1.html(source_code, width=1000, height=1000, scrolling=True)
-    except:
-        st.write("Error occurred while generating the AutoViz report.")
+    # Sidebar for plot selection
+    plot_type = st.sidebar.selectbox('Select Plot Type', ['Histogram', 'Scatter Plot'])
 
+    if plot_type == 'Histogram':
+        # Sidebar for selecting column
+        selected_column = st.sidebar.selectbox('Select Column for Histogram', custom_df.columns)
 
-if choice == "Modelling":
-    target_options = df.columns.tolist()
-    chosen_target = st.selectbox('Choose the Target Column', target_options)
-    model_type = st.selectbox('Choose Model Type', ['Regression', 'Classification'])
-    if st.button('Run Modelling'):
-        X = df.drop(chosen_target, axis=1)
-        y = df[chosen_target]
+        # Plot histogram
+        plt.figure(figsize=(8, 6))
+        sns.histplot(custom_df[selected_column])
+        st.pyplot()
 
-        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=65481254)
+    elif plot_type == 'Scatter Plot':
+        # Sidebar for selecting columns
+        x_axis = st.sidebar.selectbox('Select X-Axis Column', custom_df.columns)
+        y_axis = st.sidebar.selectbox('Select Y-Axis Column', custom_df.columns)
 
-        if model_type == 'Classification':
-            # Classification
-            clf = LazyClassifier(verbose=0, ignore_warnings=False, custom_metric=None)
-            models, predictions = clf.fit(X_train, X_test, y_train, y_test)
-            model_dictionary = clf.provide_models(X_train, X_test, y_train, y_test)
-        else:
-            # Regression
-            reg = LazyRegressor(verbose=0, ignore_warnings=False, custom_metric=None)
-            models, predictions = reg.fit(X_train, X_test, y_train, y_test)
-            model_dictionary = reg.provide_models(X_train, X_test, y_train, y_test)
+        # Plot scatter plot
+        plt.figure(figsize=(8, 6))
+        sns.scatterplot(x=x_axis, y=y_axis, data=custom_df)
+        st.pyplot()
 
-        st.dataframe(models)
+else:
+    # Display the default dataset
+    st.write('**Default Dataset (Iris):**')
+    st.write(iris_df.head())
 
-if choice == "Download":
-    with open('model_dictionary.pkl', 'wb') as f:
-        st.download_button('Download Model', f, file_name="model_dictionary.pkl")
+    # Sidebar for plot selection
+    plot_type = st.sidebar.selectbox('Select Plot Type', ['Histogram', 'Scatter Plot'])
+
+    if plot_type == 'Histogram':
+        # Sidebar for selecting column
+        selected_column = st.sidebar.selectbox('Select Column for Histogram', iris_df.columns)
+
+        # Plot histogram
+        plt.figure(figsize=(8, 6))
+        sns.histplot(iris_df[selected_column])
+        st.pyplot()
+
+    elif plot_type == 'Scatter Plot':
+        # Sidebar for selecting columns
+        x_axis = st.sidebar.selectbox('Select X-Axis Column', iris_df.columns)
+        y_axis = st.sidebar.selectbox('Select Y-Axis Column', iris_df.columns)
+
+        # Plot scatter plot
+        plt.figure(figsize=(8, 6))
+        sns.scatterplot(x=x_axis, y=y_axis, data=iris_df)
+        st.pyplot()