Upload 9 files

This is a Web app tool to generate profile report & trained model of your uploaded dataset !!

.gitignore

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+
+logs.log
+test.py
+AutoML
+_pycache_

Dockerfile

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+FROM python:3.10
+COPY . /app
+WORKDIR /app
+RUN pip install --no-cache-dir -r requirements.txt
+EXPOSE 8501
+CMD streamlit run app.py --server.port $PORT

README.md

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----
-title: AutomatedML
-emoji: 🌖
-colorFrom: gray
-colorTo: pink
-sdk: streamlit
-sdk_version: 1.31.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+This is a Web app tool to generate profile report & trained model of your uploaded dataset !! Try it on your own  👉 https://automatedml-viue.onrender.com/ 

Sourcedata.csv

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app.py

@@ -0,0 +1,319 @@
+import streamlit as st
+import pandas as pd
+import os
+from intro import Intro  # importing function from python file from same directory
+
+
+st.set_page_config(
+    page_title="Automated ML",
+    page_icon="🤖",
+    layout="wide",
+    initial_sidebar_state="expanded",
+)
+
+
+page_bg_img_link = f"""
+<style>
+[data-testid="stAppViewContainer"]> .main{{
+
+background-color: #FFDEE9;
+background-image: linear-gradient(0deg,  #B5FFFC 0%, #FFDEE9 100%);
+
+
+
+
+}}
+
+
+[data-testid="stHeader"]{{
+background-color: rgba(0,0,0,0)
+
+}}
+
+[data-testid="stToolbar"]{{
+right : 2 rem;
+}}
+
+[data-testid="stSidebar"] > div:first-child{{
+
+
+background: linear-gradient(to right bottom,
+                 rgba(285,205,205,0.7),
+                 rgba(285,205,205,0.3));
+}}
+
+
+</style>
+"""
+st.markdown(page_bg_img_link, unsafe_allow_html=True)
+
+
+# Import profiling capability
+from ydata_profiling import profile_report
+from streamlit_pandas_profiling import st_profile_report
+
+# ML Pycaret Modules
+from pycaret.classification import (
+    setup as classification_setup,
+    compare_models as classification_compare_model,
+    pull as classification_pull,
+    predict_model as classification_predict_model,
+    save_model as classification_save_model,
+)
+
+from pycaret.regression import (
+    setup as regression_setup,
+    compare_models as regression_compare_models,
+    pull as regression_pull,
+    predict_model as regression_predict_model,
+    save_model as regression_save_model,
+)
+
+from pycaret.clustering import (
+    setup as clustering_steup,
+    pull as clustering_pull,
+    create_model as clustering_create_model,
+    predict_model as clustering_predict_model,
+    plot_model as clustering_plot_model,
+    save_model as clustering_save_model,
+)
+
+from pycaret.anomaly import (
+    setup as anomaly_setup,
+    pull as anomaly_pull,
+    create_model as anomaly_create_model,
+    models as anomaly_models,
+    plot_model as anomaly_plot_model,
+    assign_model as anomaly_assign_model,
+    save_model as anomaly_save_model,
+)
+
+
+def main():
+    st.sidebar.image("https://www.onepointltd.com/wp-content/uploads/2020/03/inno2.png")
+    st.sidebar.title("AutoStreamML")
+    choice = st.sidebar.radio(
+        "Navigation", ["Instruction", "Upload", "Profiling", "ML", "Download"]
+    )
+    st.sidebar.info(
+        "This Application allows us to build an automated ML Pipeline using Streamlit, Pandas and PyCaret. And it's magic!"
+    )
+
+    if os.path.exists("Sourcedata.csv"):
+        df = pd.read_csv("Sourcedata.csv", index_col=None)
+    else:
+        df = None
+
+    ## Section1 - Instruction ##
+    if choice == "Instruction":
+        Intro()
+
+    ## Section2 - Upload ##
+    elif choice == "Upload":
+        upload_section(df)
+
+    ## Section3 - Profiling dataset ##
+    elif choice == "Profiling":
+        profiling_section(df)
+
+    ## Section4 - Machine Learning ##
+    elif choice == "ML":
+        ml_section(df)
+
+    ## Section5 - Download pipeline ##
+    elif choice == "Download":
+        download_section()
+
+
+## Section2 - Upload ##
+def upload_section(df):
+    st.title("Upload Your Data for Modelling!")
+    file = st.file_uploader("Upload Your Dataset Here")
+    if file:
+        df = pd.read_csv(file, index_col=None)
+        df.to_csv("Sourcedata.csv", index=None)
+        st.dataframe(df)
+
+
+## Section3 - Profiling dataset ##
+def profiling_section(df):
+    st.title("Automated Exploratory Data Analysis")
+    if df is not None:
+        profile_report = df.profile_report()
+        st_profile_report(profile_report)
+    else:
+        st.info("Upload a dataset for profiling.")
+
+
+## Section4 - Machine Learning ##
+def ml_section(df):
+    st.title("Machine Learning Go 🤘🤘🤘")
+    model = st.radio(
+        "Select Your Model",
+        [
+            "Classification",
+            "Regression",
+            "Clustering",
+            "Anomaly Detection",
+        ],
+    )
+
+    if model == "Classification":
+        classification_subsection(df)
+    elif model == "Regression":
+        regression_subsection(df)
+    elif model == "Clustering":
+        clustering_subsection(df)
+    elif model == "Anomaly Detection":
+        AnomalyDetection_subsection(df)
+
+    # Add other model subsections here
+
+
+## for CLASSIFICATION
+def classification_subsection(df):
+    st.subheader("Classification")
+    chosen_target = st.selectbox("Select Your Target for Classification", df.columns)
+    if st.button("Train Model"):
+        st.info("This is dataset")
+        st.dataframe(df.head())
+
+        classification_setup(df, target=chosen_target)
+        setup_df = classification_pull()
+        st.info("This is ML Experiment Settings ")
+        st.dataframe(setup_df)
+
+        best_model = classification_compare_model()
+        compare_df = classification_pull()
+        st.info("This is the ML Model")
+        st.dataframe(compare_df)
+
+        predict = classification_predict_model(best_model)
+        compare_df = classification_pull()
+        st.info("This is predicted value of best Model")
+        st.dataframe(predict)
+
+        classification_save_model(best_model, "best_model")
+
+
+## for Regression
+def regression_subsection(df):
+    st.subheader("Regression")
+    chosen_target = st.selectbox("Select Your Target for Regresssion", df.columns)
+    if st.button("Train Model"):
+        st.info("This is dataset")
+        st.dataframe(df.head())
+
+        regression_setup(df, target=chosen_target)
+        setup_df = regression_pull()
+        st.info("This is ML Experiment Settings ")
+        st.dataframe(setup_df)
+
+        best_model = regression_compare_models()
+        compare_df = regression_pull()
+        st.info("This is the ML Model")
+        st.dataframe(compare_df)
+
+        predict = regression_predict_model(best_model)
+        st.info("This is predicted value of best Model")
+        st.dataframe(predict)
+
+        regression_save_model(best_model, "best_model")
+
+
+## for Clustering
+def clustering_subsection(df):
+    st.subheader("Clustering")
+    st.info("We are using KMean Clustering Method : ")
+    if st.button("Train Model"):
+        st.info("This is dataset")
+        st.dataframe(df.head())
+        clustering_steup(df)
+        setup_df = clustering_pull()
+        st.info("This is ML Experiment Settings ")
+        st.dataframe(setup_df)
+
+        kmeans = clustering_create_model("kmeans")
+        created_model_df = clustering_pull()
+        st.info("This is the ML Kmean Clustering Model")
+        st.dataframe(created_model_df)
+
+        predict = clustering_predict_model(kmeans, data=df)
+        # predict_df = clustering_pull()
+        st.info(
+            "generates cluster labels using a trained model to Kmean Clustering Model"
+        )
+        st.dataframe(predict)
+        clustering_save_model(kmeans, "best_model")
+
+        plot_mod = clustering_plot_model(kmeans)
+        plot_mod
+
+
+## for  Anomaly Detection
+def AnomalyDetection_subsection(df):
+    st.subheader("Anomaly Detection")
+    method = st.selectbox(
+        "Select Your method",
+        [
+            "iforest",
+            "knn",
+            "svm",
+            "abod",
+            "cluster",
+        ],
+    )
+    if st.button("Train Model"):
+        st.info("This is dataset")
+        st.dataframe(df.head())
+        anomaly_setup(df, session_id=123)
+        setup_df = anomaly_pull()
+        st.info("This is ML Experiment Settings ")
+        st.dataframe(setup_df)
+
+        iforest = anomaly_create_model(method)  # we use iforest model
+        models = anomaly_models()
+        st.info("We use these models for anomaly detection ! ")
+        st.dataframe(models)
+
+        st.info(
+            "predict anomaly labels to the dataset for a given model. (1 = outlier, 0 = inlier)"
+        )
+        predictions = anomaly_assign_model(iforest)
+        st.dataframe(predictions)
+
+        anomaly_save_model(iforest, "best_model")
+
+        plot_mod = anomaly_plot_model(iforest)
+        plot_mod
+
+
+## Section5 - Download pipeline ##
+
+
+def download_section():
+    st.title("Download trained pipeline!")
+    with open("best_model.pkl", "rb") as file:
+        st.download_button("Download", file, "trained_model.pkl")
+
+    st.markdown("""""")
+    st.markdown("""""")
+    st.markdown("""""")
+    st.markdown("""""")
+    st.markdown("""""")
+    st.markdown("""""")
+    st.markdown("""""")
+    st.markdown("""""")
+    st.markdown("""""")
+
+    with st.container():
+        col = st.columns([1])
+        # HARSH #
+        with col[0]:
+            st.write(
+                "This app was created by [Harsh Narayan](https://portfolio-7bwl.onrender.com/)"
+            )
+
+
+if __name__ == "__main__":
+    main()

best_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d4cba6807b045150419d77d0e76ccbff971fe9f58c96d0e938a4990403ddc6d9
+size 231443

intro.py

@@ -0,0 +1,95 @@
+import streamlit as st
+
+
+# once we excuted this bottom code one time then it will cash it and it is available for next time again
+def Intro():
+    st.markdown(
+        '<div style=" margin-top: 40px; color:black; font-size: 2.1rem;font-weight: 400;text-align: center; border-radius: 15px 10vw; background:yellow;">Instructions for using this web app!</div>',
+        unsafe_allow_html=True,
+    )
+    # st.header(":blue[Instructions for using this web app!]")
+
+    # st.subheader( " Introduction page is in process :blue[colors] and emojis :sunglasses: ")
+
+    # st.divider()
+
+    st.markdown(
+        '<div style=" height:auto; width: 100%; padding: 1rem 8rem ; margin-top:2rem; font-size:20px; color:black; border-radius:1.5rem ;background:white;">  This Web page is about automated Machine Learninig stuff , where we create machine learning pipeline. <br> Here we use Streamlit framework to develop an interactive web application  for automated machine learning (AutoML) tasks. <br>The application consists of several sections: <br><br> &#9830 <strong>Upload</strong> :  Allows users to upload their dataset for further analysis and modeling. <br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;    <mark>Note</mark> : Uploaded dataset should be in CSV format and Pre-processed. <br> <br> &#9830 <strong>Profiling</strong> : Conducts automated exploratory data analysis (EDA) using ydata Profiling, generating detailed reports on the uploaded dataset. <br> <br> &#9830 <strong>Machine Learning(ML)</strong> : Offers various AutoML capabilities for different types of tasks: <br><br> &nbsp;  &nbsp; &nbsp; &#8226; <strong>Classification</strong> : Enables users to select a target variable for classification tasks, train classification models, and displays model performance metrics. <br><br> &nbsp;  &nbsp; &nbsp; &#8226; <strong>Regression</strong> : Allows users to choose a target variable for regression tasks, trains regression models, and presents model evaluation results. <br><br> &nbsp;  &nbsp; &nbsp; &#8226; <strong>Clustering</strong> : Utilizes K-Means clustering for unsupervised clustering tasks, provides model training, and displays clustering results. <br><br> &nbsp;  &nbsp; &nbsp; &#8226; <strong>Anomaly Detection</strong> : Supports various anomaly detection methods, such as Isolation Forest and K-Nearest Neighbors, to identify outliers in the data.  <br><br> &#9830 <strong>Download</strong> : Provides the option to download the trained ML pipeline as a Pickle file.</div>',
+        unsafe_allow_html=True,
+    )
+    # for bullet point
+    # for white space &nbsp;
+    # for bullet point use this diamond &#9830;
+
+    st.markdown(
+        '<div style=" margin-top: 40px; color:black; font-size: 2.1rem;font-weight: 400;text-align: center; border-radius: 15px 10vw; background:yellow;">Methods of Machine Learning! </div>',
+        unsafe_allow_html=True,
+    )
+
+    # classification
+
+    st.markdown(
+        '<div style="margin-top: 40px; margin-left: 40px; color:black; font-size: 1.8rem;font-weight: 300;text-align: left;">🐥Classification : </div>',
+        unsafe_allow_html=True,
+    )
+
+    st.markdown(
+        '<div style=" height:auto; width: 100%; padding: 1rem 8rem ; margin-top:2rem; font-size:20px; color:black; border-radius:1.5rem ;background:white;"> PyCaret’s Classification Module is a supervised machine learning module that is used for classifying elements into groups. <br><br> The goal is to predict the categorical class labels which are discrete and unordered. Some common use cases include predicting customer default (Yes or No), predicting customer churn (customer will leave or stay), the disease found (positive or negative). </div>',
+        unsafe_allow_html=True,
+    )
+
+    # regression
+
+    st.markdown(
+        '<div style="margin-top: 40px; margin-left: 40px; color:black; font-size: 1.8rem;font-weight: 300;text-align: left;">🐥Regression : </div>',
+        unsafe_allow_html=True,
+    )
+
+    st.markdown(
+        '<div style=" height:auto; width: 100%; padding: 1rem 8rem ; margin-top:2rem; font-size:20px; color:black; border-radius:1.5rem ;background:white;"> PyCaret’s Regression Module is a supervised machine learning module that is used for estimating the relationships between a dependent variable (often called the ‘outcome variable’, or ‘target’) and one or more independent variables (often called ‘features’, ‘predictors’, or ‘covariates’). <br><br> The objective of regression is to predict continuous values such as predicting sales amount, predicting quantity, predicting temperature, etc.  </div>',
+        unsafe_allow_html=True,
+    )
+
+    # Clustering
+
+    st.markdown(
+        '<div style="margin-top: 40px; margin-left: 40px; color:black; font-size: 1.8rem;font-weight: 300;text-align: left;">🐥Clustering : </div>',
+        unsafe_allow_html=True,
+    )
+
+    st.markdown(
+        '<div style=" height:auto; width: 100%; padding: 1rem 8rem ; margin-top:2rem; font-size:20px; color:black; border-radius:1.5rem ;background:white;"> PyCaret’s Clustering Module is an unsupervised machine learning module that performs the task of grouping a set of objects in such a way that objects in the same group (also known as a cluster) are more similar to each other than to those in other groups. <br><br> <mark>Note</mark> : Clustering is somewhere similar to the classification algorithm, but the difference is the type of dataset that we are using. In classification, we work with the labeled data set, whereas in clustering, we work with the unlabelled dataset.</div>',
+        unsafe_allow_html=True,
+    )
+
+    # Anomaly Detection
+
+    st.markdown(
+        '<div style="margin-top: 40px; margin-left: 40px; color:black; font-size: 1.8rem;font-weight: 300;text-align: left;">🐥Anomaly Detection : </div>',
+        unsafe_allow_html=True,
+    )
+
+    st.markdown(
+        '<div style=" height:auto; width: 100%; padding: 1rem 8rem ; margin-top:2rem; font-size:20px; color:black; border-radius:1.5rem ;background:white;"> PyCaret’s Anomaly Detection Module is an unsupervised machine learning module that is used for identifying rare items, events, or observations that raise suspicions by differing significantly from the majority of the data. <br><br>Typically, the anomalous items will translate to some kind of problems such as bank fraud, a structural defect, medical problems, or errors.<br><br> For a dataset having all the feature gaussian in nature, then the statistical approach can be generalized by defining an elliptical hypersphere that covers most of the regular data points, and the data points that lie away from the hypersphere can be considered as anomalies. </div>',
+        unsafe_allow_html=True,
+    )
+
+    # # Time Series
+
+    # st.markdown(
+    #     '<div style="margin-top: 40px; margin-left: 40px; color:black; font-size: 1.8rem;font-weight: 300;text-align: left;">🐥Time Series : </div>',
+    #     unsafe_allow_html=True,
+    # )
+
+    # st.markdown(
+    #     '<div style=" height:auto; width: 100%; padding: 1rem 8rem ; margin-top:2rem; font-size:20px; color:black; border-radius:1.5rem ;background:white;"> PyCaret Time Series module is a powerful tool for analyzing and predicting time series data using machine learning and classical statistical techniques. This module enables users to easily perform complex time series forecasting tasks by automating the entire process from data preparation to model deployment. <br><br> PyCaret Time Series Forecasting module supports a wide range of forecasting methods such as ARIMA, Prophet, and LSTM. It also provides various features to handle missing values, time series decomposition, and data visualizations.<br><br>Time series analysis is used for non-stationary data—things that are constantly fluctuating over time or are affected by time. Industries like finance, retail, and economics frequently use time series analysis because currency and sales are always changing.</div>',
+    #     unsafe_allow_html=True,
+    # )
+
+    st.markdown("""""")
+    st.markdown("""""")
+    st.markdown("""""")
+
+    st.write(
+        "This app was created by [Harsh Narayan](https://portfolio-7bwl.onrender.com/)"
+    )

test.py

@@ -0,0 +1,5 @@
+import streamlit as st
+from pycaret.regression import load_model
+
+pipeline = load_model("trained_model")
+print(pipeline)