write an automl streamlit python tensorflow keras application with mechanics that takes a CSV dataset, performs pandas profiling and generates the code to do classification Here is an example of an Automated Machine Learning (AutoML) Streamlit application that takes a CSV dataset, performs pandas profiling, and generates the code to do classification using TensorFlow and Keras:

python Copy code import streamlit as st import pandas as pd import pandas_profiling from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense from sklearn.model_selection import train_test_split

st.set_page_config(page_title="AutoML Streamlit App", page_icon=":robot:", layout="wide")

st.title("AutoML Streamlit App")

Upload a CSV dataset

uploaded_file = st.file_uploader("Upload your dataset", type=["csv"]) if uploaded_file is not None: # Load the dataset and display the first 5 rows df = pd.read_csv(uploaded_file) st.dataframe(df.head())

# Perform pandas profiling and display the report
profile = pandas_profiling.ProfileReport(df)
st.write("Pandas Profiling Report:", profile)

# Split the data into features and target variables
X = df.drop(df.columns[-1], axis=1)
y = df[df.columns[-1]]

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Define a sequential model with TensorFlow and Keras
model = Sequential()
model.add(Dense(16, input_dim=X_train.shape[1], activation="relu"))
model.add(Dense(1, activation="sigmoid"))

# Compile the model
model.compile(loss="binary_crossentropy", optimizer="adam", metrics=["accuracy"])

# Train the model
model.fit(X_train, y_train, epochs=10, batch_size=32)

# Evaluate the model on the test set
test_loss, test_accuracy = model.evaluate(X_test, y_test)
st.write("Test Accuracy:", test_accuracy)

# Generate the code to do classification
st.write("Generated Code:")
st.code("""

import pandas as pd import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense

Load the dataset

df = pd.read_csv("dataset.csv")

Split the data into features and target variables

X = df.drop(df.columns[-1], axis=1) y = df[df.columns[-1]]

Define a sequential model with TensorFlow and Keras

model = Sequential() model.add(Dense(16, input_dim=X.shape[1], activation="relu")) model.add(Dense(1, activation="sigmoid"))

Compile the model

model.compile(loss="binary_crossentropy", optimizer="

Aaron Wacker continue writing the program above but simplify it and automatically generate a plotly visualization using treemap and sunburst plot from the dataset dynamically by interpreting the data types inside the CSV file Here is a simplified version of the AutoML Streamlit application that automatically generates a Plotly visualization using treemap and sunburst plots from the CSV dataset dynamically by interpreting the data types inside the file:

python Copy code import streamlit as st import pandas as pd import plotly.express as px

st.set_page_config(page_title="AutoML Streamlit App", page_icon=":robot:", layout="wide")

st.title("AutoML Streamlit App")

Upload a CSV dataset

# Generate a treemap or sunburst plot based on data types
numerical_cols = df.select_dtypes(include=["float", "int"]).columns
categorical_cols = df.select_dtypes(include=["object"]).columns

if len(numerical_cols) >= 2:
    fig = px.scatter_matrix(df, dimensions=numerical_cols)
    st.plotly_chart(fig)
elif len(categorical_cols) >= 2:
    fig = px.treemap(df, path=categorical_cols)
    st.plotly_chart(fig)
else:
    fig = px.sunburst(df, path=categorical_cols + numerical_cols)
    st.plotly_chart(fig)

In this simplified version, we use Plotly Express to generate a scatter matrix plot for numerical data, a treemap plot for categorical data, and a sunburst plot for both numerical and categorical data. The type of plot generated is based on the number of numerical and categorical columns present in the dataset.

Regenerate response