Create Prompts.md

Prompts.md

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+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
+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