Prompts.md

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
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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
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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
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())

    # 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