developed streamlit for publishing on HFS

.github/workflows/main.yml

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+name: Sync to Hugging Face hub
+on:
+  push:
+    branches: [main]
+  # to run this workflow manually from the Actions tab
+  workflow_dispatch:
+
+jobs:
+  sync-to-hub:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+        with:
+          fetch-depth: 0
+          lfs: true
+      - name: Configure Git
+        run: |
+          git config --global user.email "[email protected]"
+          git config --global user.name "ferds003"
+
+      - name: Push to Hugging Face
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        run: |
+          git remote set-url space https://ferds003:[email protected]/spaces/ferds003/DATA103_filipino_spam_detection || \
+          git remote add space https://ferds003:[email protected]/spaces/ferds003/DATA103_filipino_spam_detection
+          git push --force-with-lease space main

Dockerfile

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+FROM python:3.9-slim
+
+WORKDIR /app
+
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    curl \
+    software-properties-common \
+    git \
+    && rm -rf /var/lib/apt/lists/*
+
+COPY requirements.txt ./
+COPY src/ ./src/
+
+RUN pip3 install -r requirements.txt
+
+EXPOSE 8501
+
+HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
+
+ENTRYPOINT ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0"]

app.py

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+import streamlit as st
+import plotly.express as px
+import plotly.graph_objects as go
+import pandas as pd
+import numpy as np
+import pickle
+import sklearn
+from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
+from wordcloud import WordCloud
+from PIL import Image
+
+MODEL_PATHS = {
+    "Support Vector Machine": "models/svm_model_2.pkl",
+    "Random Forest": "models/rf_model_2.pkl",
+    "Multinomial Naive Bayes": "models/mNB_model_2.pkl",
+    "Complement Naive Bayes": "models/cnb_model_2.pkl",
+}
+
+
+@st.cache_resource
+def load_model(model_name):
+    model_path = MODEL_PATHS[model_name]
+    with open(model_path, "rb") as f:
+        return pickle.load(f)
+
+
+sample_df = pd.read_csv("src/test.csv")
+eda_df = pd.read_csv("src/eda_df.csv")
+ml_df = pd.read_csv("src/cleaned_sms_text_labels_only_v2.csv")
+
+
+## define feature importance per model and put it into a plotly chart when classifying
+def explain_prediction(model, text, model_name, top_n=10):
+    # Unpack classifier as saved models are in made pipeline saved in mlflow
+    if hasattr(model, "named_steps"):
+        clf = model.named_steps.get("classifier")
+        vectorizer = model.named_steps.get(
+            "vectorizer"
+        )  # i was having errors since im calling a different vectorizer!
+    else:
+        clf = model
+    # Compute feature contributions for the chosen models; make contributions in absolute values
+    X_input = vectorizer.transform([text])
+    feature_names = np.array(vectorizer.get_feature_names_out())
+    if not X_input.nnz:
+        return pd.DataFrame(
+            columns=["Token", "Contribution"]
+        )  ## Handles edge cases like empty input
+
+    if model_name == "Multinomial Naive Bayes":
+        class_log_prob = clf.feature_log_prob_[1] - clf.feature_log_prob_[0]
+        contributions = X_input.toarray()[0] * class_log_prob
+        contributions = np.abs(contributions)
+    elif model_name == "Complement Naive Bayes":
+        class_log_prob = clf.feature_log_prob_[1] - clf.feature_log_prob_[0]
+        contributions = X_input.toarray()[0] * class_log_prob
+        contributions = np.abs(contributions)
+    elif model_name == "Random Forest":
+        contributions = clf.feature_importances_ * X_input.toarray()[0]
+        contributions = np.abs(contributions)
+    elif model_name == "Support Vector Machine":
+        contributions = clf.coef_.toarray()[0] * X_input.toarray()[0]
+        contributions = np.abs(contributions)
+    else:
+        return st.error("Invalid model name")
+
+    token_scores = list(zip(feature_names, contributions))
+    token_scores = sorted(token_scores, key=lambda x: abs(x[1]), reverse=True)
+    return pd.DataFrame(token_scores[:top_n], columns=["Token", "Contribution"])
+
+
+## --FOR THE WORDCLOUD AT EdATAB--
+def get_wordcloud_data(text):
+    wc = WordCloud(
+        width=1200,
+        height=600,
+        background_color="white",
+        colormap="viridis",
+        max_font_size=60,
+        prefer_horizontal=0.8,
+        collocations=False,
+        min_word_length=3,
+        max_words=75,
+        relative_scaling=0.5,
+        contour_color="black",
+        contour_width=0.2,
+    ).generate(text)
+    elements = []
+    for (word, freq), font_size, position, orientation, color in wc.layout_:
+        elements.append((word, freq, position))  # position is already a tuple (x, y)
+    return elements
+
+
+## --FOR THE WORDCLOUD AT EdATAB--
+def plot_wordcloud(elements, title):
+    words, frequencies, positions = zip(*elements)
+    x = [pos[0] for pos in positions]
+    y = [-pos[1] for pos in positions]  # Flip Y to display properly
+    sizes = [freq * 200 for freq in frequencies]
+
+    fig = go.Figure(
+        data=[
+            go.Scatter(
+                x=x,
+                y=y,
+                mode="text",
+                text=words,
+                textfont=dict(size=sizes),
+                hoverinfo="text",
+                textposition="middle center",
+            )
+        ]
+    )
+
+    fig.update_layout(
+        title=dict(text=title, x=0.5),
+        showlegend=False,
+        xaxis=dict(showgrid=False, visible=False),
+        yaxis=dict(showgrid=False, visible=False),
+        margin=dict(l=20, r=20, t=50, b=20),
+    )
+    st.plotly_chart(fig, use_container_width=True)
+
+
+with open("src/combined_stopwords.pkl", "rb") as f:
+    combined_stopwords = pickle.load(f)
+vectorizer = TfidfVectorizer(stop_words=list(combined_stopwords), max_features=20)
+
+bow_cv = CountVectorizer(stop_words=list(combined_stopwords), max_features=1000)
+tfidf = TfidfVectorizer(stop_words=list(combined_stopwords), max_features=1000)
+
+## ----- START OF APP ------
+st.set_page_config(
+    layout="wide", page_title="DATA103_filipino_spam_detection", page_icon="😎"
+)
+
+st.title("Spam SMS Detection in the Filipino Context")
+
+## SIDEBAR
+st.sidebar.markdown("---")
+st.sidebar.markdown(
+    """
+    About the Team:
+
+    Demo created by [Ferds Magallanes](https://ferds003.github.io), Hitika Motwani, Neil Penaflor, and Mark Abergos using Streamlit and Hugging Face Spaces.
+    Purposes to demonstrate their NLP classification project for their minor data science class.
+
+    Contributions:
+    - Data Curation - Ferds, Hitika
+    - EDA - Hitika, Neil
+    - Features Selection and NLP_Training - Mark, Ferds
+    - Eval and Demo - Ferds
+
+    Acknowledgements:
+
+    The team would like to thank Doc Jet Virtusio for the support and teachings he gave us in our minor class :))
+
+    """
+)
+
+## TABS PER PROJECT TASK
+(
+    DemoTAB,
+    DataCurationTAB,
+    EdATAB,
+    FeatureSelectionTAB,
+    TrainingPipelineTAB,
+    ModelEvaluationTAB,
+    ConTAB,
+) = st.tabs(
+    [
+        "Demo",
+        "Data Curation",
+        "EDA",
+        "Feature Selection",
+        "Training Pipeline",
+        "Model Evaluation",
+        "Conclusion and Recommendations",
+    ]
+)
+
+with DemoTAB:
+    st.write("")
+    st.markdown("""
+    Hi there! Input your sample sms messages for us to classify if it is spam or not.
+    Correspondingly, we will provide what text (tokens) signify in percentage is spam or not.
+    """)
+
+    ## Provide user with sample spam and ham messages
+    with st.expander(
+        "📋 Try a sample message! This is from our test.csv so this data is not trained on our model"
+    ):
+        st.markdown(
+            "Select a sample SMS message from below: Label is 0 for ham 🍗 and 1 for spam 🥫"
+        )
+        label_map = {0: "Ham", 1: "Spam"}
+        sample_index = st.selectbox(
+            "Select a sample SMS message",
+            sample_df.index,
+            format_func=lambda x: f"SMS {x} - {label_map[sample_df.loc[x, 'label']]}: {sample_df.loc[x, 'text'][:50]}",
+        )
+        if st.button("Use this sample"):
+            st.session_state["1"] = sample_df.loc[sample_index, "text"]
+
+    ## Model selection
+    selected_model_name = st.selectbox(
+        "Select Classification Model", list(MODEL_PATHS.keys())
+    )
+    text = st.text_area("Enter SMS to classify here!", height=100, key="1")
+
+    ## CASE WHEN BUTTON IS PRESSED
+    if st.button("Classify"):
+        if text:
+            with st.spinner("Analyzing..."):
+                clf = load_model(selected_model_name)
+                prediction = clf.predict([text])[0]
+                pred_proba = clf.predict_proba([text])[0]
+
+                st.success(f"Prediction: {'Spam' if prediction == 1 else 'Not Spam'}")
+                st.info(f"Probability of Spam: {pred_proba[1]:.2%}")
+                st.info(f"Probability of Not Spam: {pred_proba[0]:.2%}")
+
+                st.markdown("### Feature Importance")
+                explain_df = explain_prediction(clf, text, selected_model_name)
+                if (
+                    explain_df is not None and not explain_df.empty
+                ):  ## calling the  function
+                    fig1 = px.bar(
+                        explain_df,
+                        x="Contribution",
+                        y="Token",
+                        orientation="h",
+                        title="Top Contributing Tokens to Prediction",
+                        labels={"Contribution": "Impact Score"},
+                        color="Contribution",
+                        color_continuous_scale="RdBu",
+                        template="plotly_dark",
+                    )
+                    st.plotly_chart(fig1, use_container_width=True)
+                    print(
+                        "Top tokens:", explain_df.head()
+                    )  ## DEBUGGING LINE; Can be checked on streamlit terminal
+                else:
+                    st.warning("Unable to compute token contribution for this model.")
+        else:
+            st.warning("Please input text to classify.")
+with DataCurationTAB:
+    st.markdown(""" 
+     Data cleaning and pre-processing is necessary as we are considering three datasets with different contexts. Below is a summary of the data treatment and insights done to make the versions of the dataset.
+
+- For [Dataset 1](https://www.kaggle.com/datasets/scottleechua/ph-spam-marketing-sms-w-timestamps):
+  - drop any null values; drop any full redactions done in `text` column through regex. Drops 74% of the dataset as text sms data is salient to the project.
+  - checked any redactions of the similar <> format within `text` feature. Concluded that any other text with <> format are coming from spam and ads category
+  - Drops `date_read` column. Renamed `date_received` column
+  - Made a label columns that considers the text as label `spam` if it is within the category `spam` and `ads` based on `category` column
+  - applied `get_carrier` function to get sms local provider.
+- For [Dataset 2](https://www.kaggle.com/datasets/bwandowando/philippine-spam-sms-messages):
+  - drop any null values; all data will be considered under the label `spam` for its sms text messages data.
+  - checked any redactions of the similar <> format within `text` feature. found `<REAL NAME>` redactions; replaced it with a blankspace
+  - dropped `hashed_cellphone_number` and `carrier` column to apply own `get_carrier` function that considers also DITO sms provider.
+  - renamed column `masked_celphone_number` to `sender` and `date` to `date_received` similar to dataset 1.
+- For [Dataset 3](https://github.com/Yissuh/Filipino-Spam-SMS-Detection-Model/blob/main/data-set.csv):
+  - drop any null values; dropped any `<<Content not supported.>>|<#>` tags and any other tags that are labeled under ham messages.
+  - renamed column `message` to `text` in conformity with other datasets.
+  - checked any redactions of the similar <> format within `text` feature. found `<CODE>`, `<DATE>`, `<Last 4 digits of acct num>`, `<REFERENCE NUMBER>`, `<TIME>`, `<space>` redactions to be ham messages; replaced it with a blankspace.
+
+**Cleaning for Merged Dataset**
+- Each datasets considered was checked for null values afterwards and checked if this is acceptable for processing; Decision was to just do an SQL query `NOT NULL` to filter values on target features for each respective tasks
+- Used `concat` function to merged these three datasets knowing that column names are the same for all.
+- Used `drop_duplicates` to remove 43 duplicate observations.
+- Explicitly defined `date_received` as a datetime for EDA.
+
+**NLP Processing on Merged Dataset**
+- further processing done to get rid of unwanted characters; retrieves pure lowercased alphanumberic characters with no extract whitespace.
+- gets rid of any `http-like` text which are urls; This was considered as ham messages often provide urls and lessen tokens considered in vectorization.
+- noticed that both spam and ham have mixed english and tagalog text.   Used `langdetect` package to sort if english or tagalog then used packages `spacy` for english and `calamancy` for filipino/tagalog stopwords removal, punctuation removal, and tokenization towards BOW and TF-IDF vectorization.
+- `lemmatization` was also considered for both languages.           
+                """)
+
+    st.text("Below is the version of the dataset used for EDA purposes")
+    st.dataframe(eda_df, use_container_width=True)
+    st.markdown("### Quick Stats for this dataset")
+    st.write(eda_df.describe())
+
+    # Optional let user download it
+    st.download_button(
+        label="📥 Download CSV",
+        data=eda_df.to_csv(index=False).encode(
+            "utf-8"
+        ),  # Might be redundant since i have the csv already
+        file_name="spam_eda_df.csv",
+        mime="text/csv",
+    )
+
+    st.text("Below is the version of the dataset used for ML Training purposes")
+    st.dataframe(ml_df, use_container_width=True)
+    st.markdown("### Quick Stats for this dataset")
+    st.write(ml_df.describe())
+
+    # Optional let user download it
+    st.download_button(
+        label="📥 Download CSV",
+        data=ml_df.to_csv(index=False).encode(
+            "utf-8"
+        ),  # Might be redundant since i have the csv already
+        file_name="spam_ml_df.csv",
+        mime="text/csv",
+    )
+    st.markdown("""
+    **--Datasets Considered--**
+
+    Below are the following datasets sourced along with a simple description of its features:
+
+    > [ph-spam-marketing-sms-w-timestamps](https://www.kaggle.com/datasets/scottleechua/ph-spam-marketing-sms-w-timestamps) from Kaggle made by u/Scott_Lee_Chua:
+
+    | column | count | description | dtype |
+    |---|---|---|---|
+    | data_received | 1622 | Datetime SMS was received in timezone UTC+8. | object |
+    | sender | 1622 | A partially-masked phone number, unmasked alphanumeric Sender ID, or one of three special values: <br><br><br>-redacted_contact if sender is a person in my personal contact book; <br><br>-redacted_individual if sender is a person not in my contacts and the message is solicited (e.g., updates from delivery riders); or <br><br>-redacted_business if sender is a business/service and all their messages are solicited.<br> | object |
+    | category | 1622 | Takes one of five possible values: spam, ads, gov, notifs, or OTP. See categories below.<br><br><br>spam — unsolicited messages from unknown senders.<br><br>ads — marketing messages from known businesses or services, such as GCash and Smart.<br><br>gov — public service announcements from Philippine government agencies, such as NTC and NDRRMC. <br><br>notifs — a catch-all category for legitimate and private messages, such as transaction confirmations, delivery updates, and a handful of personal conversations. <br><br>OTP — genuine one-time passwords <br> | object |
+    | text | 1622 | Full text for spam, ads, and gov. | object |
+
+    > [🇵🇭 Philippine Spam/ Scam SMS](https://www.kaggle.com/datasets/bwandowando/philippine-spam-sms-messages) from Kaggle made by u/bwandowando:
+
+    | column | count | description | dtype |
+    |---|---|---|---|
+    | masked_celphone_number | 945 | cellphone number that is masked instead on the first five numbers and last three numbers.  | object |
+    | hashed_celphone_number | 945 | part of the XML data given that provides its unique identifier. | object |
+    | date | 945 | date when text was received. | object |
+    | text | 945 | Full text for spam, ads, and gov. | object |
+    | carrier | 945 | SMS registry fix that is associated with the first five numbers of the cellphone number. | object |
+
+
+    >[Filipino-Spam-SMS-Detection-Model](https://github.com/Yissuh/Filipino-Spam-SMS-Detection-Model/blob/main/data-set.csv) from Github made by u/Yissuh and TUP-M students:
+
+    | column | count | description | dtype |  |
+    |---|---|---|---|---|
+    | text | 873 | Full text for spam, ads, and gov. | object |  |
+    | label | 873 | text that is labeled is either spam or ham. | object |  |
+
+    <br>**--Final Version of the Dataset Considerd--**
+
+    With this, the dataset considered have 2 versions being considered for this project, one for EDA and one for ML_training that is saved in an SQLite database for querying. For simplicity, csv_files are provided done after data cleaning and pre-processing that you may check further in succeeding sections. Below is a summary of the version dataset:
+
+    > Version of the Dataset for EDA:
+
+    | column | count | description | dtype |
+    |---|---|---|---|
+    | date_received | 1713 | date when text was received. | datetime |
+    | sender | 1713 | partially-masked phone numbers. | object |
+    | preprocessed_text | 1713 | cleaned data without redactions and only alphanumeric characters. | object |
+    | carrier | 1713 | SMS registry fix from sender's number. | object |
+    | label | 1713 | label if text is either spam or ham. | category |
+
+
+    > Version of the Dataset for ML Training:
+
+    | column | count | description | dtype |
+    |---|---|---|---|
+    | text | 3379 | cleaned data without redactions and only alphanumeric characters. | object |
+    | label | 3379 | label if text is either spam or ham. | category |
+
+    > ham and spam count for ML Training: We recognize that there is a class imbalance between the two labels. We treat this as an acceptable data for training.
+
+    | label | text |
+    |-------|------|
+    | ham   | 418  |
+    | spam  | 2964 |
+                """)
+
+with EdATAB:
+    st.markdown("""
+    The objective of the EDA for this project is to make an interactive visualization for the exploration of the made dataset in regards to the SMS registration act of 2022. 
+    This will be displayed in the demo. The following are the questions answered through the EDA:
+    
+    - How many scam messages were received before/after SIM registration?
+    - Which telecom prefixes are most targeted or send the most spam?
+    - What words are most associated with scams?
+    """)
+
+    st.markdown("""
+    Summary of the insights from the EDA are the following:
+    - ham messages can be characterized to be typically shorter in text length while spam messages averages from 50 to 200 text length within the filipino-context.
+    - Most frequent SMS carrier that does unsolicited messages is coming from `Smart`.
+    - Most frequent messages associated with spam are the terms `bonus`, `BDO`, `com`, `free`, and `win` most likely persistent with unsolicited online gambling ads and solicitations to deposit on a unsafe link.
+    - Most frequent messages associated with ham are the terms `access`, `android`, `app`, `code` most likely persistent with government announcements and warnings. Other context for this would be retrieving one-time-passwords (OTP) code. 
+    - scam/spam messages are still prevalent despite the implementation of the sim registration act with known operations of this peaking during the middle of the week during the afternoon.
+
+    Additionally, EDA done for ml_training dataset version under `cleaned_sms_text_labels_only_v2.csv` is provided.            
+                """)
+
+    ## -- EDA VISUALIZATIONS HERE --
+    st.write("### Text Length Distribution")
+    eda_df["length"] = eda_df["preprocessed_text"].apply(len)
+    eda_df.head()
+    fig2 = px.histogram(
+        eda_df,
+        x="length",
+        nbins=50,  # optional, adjust bins
+        color_discrete_sequence=["#636EFA"],  # optional style
+    )
+    st.plotly_chart(fig2, use_container_width=True)
+
+    st.write("### Prefix-wise spam distribution")
+    fig3 = px.histogram(eda_df, x="carrier", color_discrete_sequence=["#636EFA"])
+    st.plotly_chart(fig3, use_container_width=True)
+
+    copy = eda_df.copy()
+    copy["date_received"] = pd.to_datetime(copy["date_received"], format="mixed")
+    copy["date_received"] = pd.to_datetime(copy["date_received"]).dt.normalize()
+    copy["date_count"] = copy.groupby("date_received")["date_received"].transform(
+        "size"
+    )
+    copy.sort_values(by="date_received", ascending=True, inplace=True)
+
+    st.write("### Time-series plot (Messages per Month/Year")
+    fig4 = px.line(
+        copy,
+        x="date_received",
+        y="date_count",
+        labels={"x": "Year", "y": "Number of Messages"},
+        color_discrete_sequence=["#636EFA"],
+    )
+    st.plotly_chart(fig4, use_container_width=True)
+
+    # Convert values in 'date_received' column to datetime datatype
+    eda_df["date_received"] = pd.to_datetime(eda_df["date_received"], format="mixed")
+    # Extract year from 'date_received' column
+    eda_df["year"] = eda_df["date_received"].dt.year
+    # Extract name of day from 'date_received' column
+    eda_df["day_name"] = eda_df["date_received"].dt.day_name()
+    # Extract hour of day from 'date_received' column
+    eda_df["hour_of_day"] = eda_df["date_received"].dt.hour
+    # Extract name of month from 'date_received' column
+    eda_df["month_name"] = eda_df["date_received"].dt.month_name()
+    # Check whether day in 'date_received' column falls on a weekend
+    eda_df["is_weekend"] = eda_df["date_received"].dt.dayofweek.isin([5, 6]).astype(int)
+
+    # Create a dataframe with the extracted features
+    datetime_features_df = eda_df[
+        ["date_received", "year", "day_name", "hour_of_day", "month_name", "is_weekend"]
+    ]
+    year_counts = eda_df["year"].value_counts().sort_index()
+
+    st.write("### Distribution of Messages by Year")
+    fig5 = px.bar(
+        x=year_counts.index,
+        y=year_counts.values,
+        labels={"x": "Year", "y": "Number of Messages"},
+        color=year_counts.values,
+        color_continuous_scale=px.colors.sequential.Viridis,
+    )
+    st.plotly_chart(fig5, use_container_width=True)
+
+    day_order = [
+        "Monday",
+        "Tuesday",
+        "Wednesday",
+        "Thursday",
+        "Friday",
+        "Saturday",
+        "Sunday",
+    ]
+    day_name_counts = eda_df["day_name"].value_counts().reindex(day_order).fillna(0)
+
+    st.write("### Distribution of Messages by Day of the Week")
+    fig6 = px.line(
+        x=day_name_counts.index,
+        y=day_name_counts.values,
+        labels={"x": "Day of the Week", "y": "Number of Messages"},
+        markers=True,
+        line_shape="linear",
+        color_discrete_sequence=["#636EFA"],
+    )
+    st.plotly_chart(fig6, use_container_width=True)
+
+    hour_of_day_counts = eda_df["hour_of_day"].value_counts().sort_index()
+    st.write("### Distribution of Messages by Hour of the Day")
+    fig7 = px.bar(
+        hour_of_day_counts,
+        x=hour_of_day_counts.index,
+        y=hour_of_day_counts.values,
+        labels={"x": "Hour of the Day (0-23)", "y": "Number of Messages"},
+        color=hour_of_day_counts.values,
+        color_continuous_scale=px.colors.sequential.Viridis,
+    )
+    st.plotly_chart(fig7, use_container_width=True)
+
+    month_order = [
+        "January",
+        "February",
+        "March",
+        "April",
+        "May",
+        "June",
+        "July",
+        "August",
+        "September",
+        "October",
+        "November",
+        "December",
+    ]
+
+    month_name_counts = (
+        eda_df["month_name"].value_counts().reindex(month_order).fillna(0)
+    )
+
+    st.write("### Distribution of Messages by Month")
+    fig8 = px.bar(
+        month_name_counts,
+        x=month_name_counts.values,
+        y=month_name_counts.index,
+        orientation="h",
+        labels={"x": "Number of Messages", "y": "Month"},
+        color=month_name_counts.values,
+        color_continuous_scale=px.colors.sequential.Viridis,
+    )
+    st.plotly_chart(fig8, use_container_width=True)
+
+    is_weekend_counts = (
+        eda_df["is_weekend"].map({0: "Weekday", 1: "Weekend"}).value_counts()
+    )
+    weekend_order = ["Weekday", "Weekend"]
+    is_weekend_counts = is_weekend_counts.reindex(weekend_order).fillna(0)
+
+    st.write("### Distribution of Messages: Weekday vs. Weekend")
+    fig9 = px.pie(
+        names=is_weekend_counts.index,
+        values=is_weekend_counts.values,
+        color_discrete_sequence=px.colors.sequential.Viridis,
+    )
+    st.plotly_chart(fig9, use_container_width=True)
+
+    st.write("## EDA for second version of the dataset to be used for training")
+
+    ## checking most common spam message values and its respective label
+    st.markdown("Checking most common `spam texts` for this version of the dataset")
+    spam_df = ml_df[ml_df["label"] == "spam"]
+    st.dataframe(spam_df.value_counts(), use_container_width=True)
+
+    st.markdown("Checking most common `ham texts` for this version of the dataset")
+    ham_df = ml_df[ml_df["label"] == "ham"]
+    st.dataframe(ham_df.value_counts(), use_container_width=True)
+
+    st.markdown("### ☁️ WordCloud of Ham and Spam Messages")
+    st.write(
+        "NOTE: issue on displaying this properly on streamlit as text are intersecting but this is acceptable"
+    )
+    ham_text = " ".join(ml_df[ml_df["label"] == "ham"]["text"].astype(str).tolist())
+    spam_text = " ".join(ml_df[ml_df["label"] == "spam"]["text"].astype(str).tolist())
+
+    col1, col2 = st.columns(2)
+    with col1:
+        st.subheader("Ham 🍗")
+        ham_elements = get_wordcloud_data(ham_text)
+        plot_wordcloud(ham_elements, "Ham Messages WordCloud")
+
+    with col2:
+        st.subheader("Spam 🥫")
+        spam_elements = get_wordcloud_data(spam_text)
+        plot_wordcloud(spam_elements, "Spam Messages WordCloud")
+
+with FeatureSelectionTAB:
+    st.markdown("""
+    We are only considering Bag-of-Words (BOW) using CountVectorizer and Term Frequency-Inverse Document Frequency (TF-IDF) for features purely on the text. 
+    :blue-background[The team did not consider length of the words and other potential feature that can be inferred from the text.] 
+    
+    BOW considers the count of the words, converted as tokens at this point, within the sentence. TF-IDF considers the frequency of the token throughout the whole corpus of documents.
+    Below is the implementation done to determine what feature is suited for the project. 
+    Based on the heatmap for tfidf, we were able to determine the top terms that have the most reoccurence between documents therefroe it is appropriate that `TF-IDF` will be used as the feature for training pipeline.
+    
+    Insight will be to make suspections on these top terms for spam: `bonus`, `account`, `araw`;
+    Ham messages that are frequent will be government announcements and OTPs therefore words like `access`, `code` and `app` are expected to associated with ham messages.
+                """)
+
+    st.markdown("### Top-N Terms Bar Chart for TF-IDF")
+    tfidf_vect = TfidfVectorizer(stop_words=list(combined_stopwords), max_features=20)
+    ml_df["text"] = ml_df["text"].fillna("")
+    X_tfidf = tfidf_vect.fit_transform(ml_df["text"])
+    counts = X_tfidf.toarray().sum(axis=0)
+    terms = tfidf_vect.get_feature_names_out()
+
+    df_top_terms = pd.DataFrame({"term": terms, "count": counts})
+
+    fig10 = px.bar(
+        df_top_terms.sort_values(by="count", ascending=False),
+        x="term",
+        y="count",
+        title="Top Terms by Tfidf Frequency",
+        text="count",
+        color_discrete_sequence=["#636EFA"],
+    )
+    fig10.update_traces(textposition="outside")
+    st.plotly_chart(fig10, use_container_width=True)
+
+    ham_df1 = ham_df.copy()
+    ham_df1["text"] = ham_df1["text"].fillna("")
+    X = vectorizer.fit_transform(ham_df1["text"])
+    tfidf_df = pd.DataFrame(X.toarray(), columns=vectorizer.get_feature_names_out())
+
+    st.write("### TF-IDF Heatmap: Top Terms vs Documents for Ham Messages")
+    fig11 = px.imshow(
+        tfidf_df.T,
+        labels=dict(x="Document", y="Term", color="TF-IDF"),
+        aspect="auto",
+        color_continuous_scale="Viridis",
+    )
+    st.plotly_chart(fig11, use_container_width=True)
+
+    st.write("### TF-IDF Heatmap: Top Terms vs Documents for Spam Messages")
+    spam_df1 = spam_df.copy()
+    spam_df1["text"] = spam_df1["text"].fillna("")
+
+    X = vectorizer.fit_transform(spam_df1["text"])
+    tfidf_df = pd.DataFrame(X.toarray(), columns=vectorizer.get_feature_names_out())
+
+    fig12 = px.imshow(
+        tfidf_df.T,
+        labels=dict(x="Document", y="Term", color="TF-IDF"),
+        aspect="auto",
+        color_continuous_scale="Viridis",
+    )
+    st.plotly_chart(fig12, use_container_width=True)
+
+    st.write("### Scatter Plot for top terms between features for selection")
+    bow_cv.fit(ml_df["text"].fillna(""))
+    X_ham = bow_cv.transform(ham_df["text"].fillna("")).toarray().sum(axis=0)
+    X_spam = bow_cv.transform(spam_df["text"].fillna("")).toarray().sum(axis=0)
+    label = ["spam" if s > h else "ham" for h, s in zip(X_ham, X_spam)]
+
+    X_counts = bow_cv.fit_transform(ml_df["text"]).toarray().sum(axis=0)
+    X_tfidf = tfidf.fit_transform(ml_df["text"]).toarray().sum(axis=0)
+
+    df_terms = pd.DataFrame(
+        {
+            "term": bow_cv.get_feature_names_out(),
+            "count": X_counts,
+            "tfidf": X_tfidf,
+            "label": label,
+        }
+    )
+
+    fig13 = px.scatter(
+        df_terms,
+        x="count",
+        y="tfidf",
+        text="term",
+        title="TF-IDF vs Frequency",
+        hover_data=["label"],
+    )
+    fig13.update_layout(
+        plot_bgcolor="#111111",  # dark gray background
+        paper_bgcolor="#111111",
+        font_color="white",  # makes text visible
+        title_font=dict(size=20, color="white"),
+        xaxis=dict(color="white", gridcolor="gray"),
+        yaxis=dict(color="white", gridcolor="gray"),
+    )
+    fig13.update_traces(
+        marker=dict(size=8, color="deepskyblue", line=dict(width=1, color="white")),
+        textposition="top center",
+        textfont=dict(color="white", size=12),
+    )
+    st.plotly_chart(fig13, use_container_width=True)
+
+with TrainingPipelineTAB:
+    st.markdown("### Training Pipeline")
+    st.markdown("""
+    The project will consider a train-val-test split for a cross-validation (cv) training with hyperparameter tuning considered per fold-run.
+
+    The group will consider :blue-background[our (4) traditional and explainable classifiers] that are known to be used for spam detection. These are to be the two variants of `Naive-Bayes (NB)`, multinomial and complement (noted to handle class imbalances well), `Support Vector Machine`, and `RandomForest`.
+
+    The project utilized `MLflow` to track training and artificats (evaluation metrics) per general run when the model is called; We have put this all under a function.""")
+
+    split_code = """
+    
+    ##define train-test vals
+    X = ml_df['text']
+    y = ml_df['label']
+    label_encoder = LabelEncoder()
+    y_encoded = label_encoder.fit_transform(y)
+
+    ##splitting
+    X_temp, X_test, y_temp, y_test = train_test_split(X, y_encoded, test_size=0.3, random_state=42, stratify=y_encoded)
+
+    ## will set split for validation to be also 0.2
+    X_train, X_val, y_train, y_val = train_test_split(X_temp, y_temp, test_size=0.3, random_state=42)
+    """
+    st.markdown("## Define Train-Val-Test Split")
+    st.code(split_code, language="python")
+
+    helper_code = """
+    
+    # I want to save also a confusion matrix in a image format to be displayed and checked
+    def plot_and_log_cf_matrix(y_true, y_pred, labels, model_type, run_id):
+        cm = confusion_matrix(y_true, y_pred)
+        plt.figure(figsize=(6,5))
+        sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=labels, yticklabels=labels)
+        plt.title(f'Confusion Matrix from run: {model_type}_{run_id}')
+        plt.ylabel('Actual')
+        plt.xlabel('Predicted')
+
+        # Saving the temp file here
+        with tempfile.NamedTemporaryFile(delete=False, suffix='.png') as temp_file:
+            plt.savefig(temp_file.name)
+        img_path = f'confusion_matrix_{model_type}_{run_id}.png'
+        plt.savefig(img_path)
+        plt.close()
+        mlflow.log_artifact(img_path)
+        os.remove(img_path) ## partial cache, will not be saved in drive
+
+    # I want to save the cross-val performance in mlflow too
+    def plot_cv_perf(cv_results,run_id):
+        mean_scores = cv_results['mean_test_score']
+        std_scores = cv_results['std_test_score']
+        plt.figure()
+        plt.errorbar(range(len(mean_scores)), mean_scores, yerr=std_scores, fmt='-o')
+        plt.title('Cross-Validation Performance per Parameter Set')
+        plt.xlabel('Parameter Set Index')
+        plt.ylabel('CV Accuracy')
+        img_path = f'cv_performance_{run_id}.png'
+        plt.savefig(img_path)
+        plt.close()
+        mlflow.log_artifact(img_path)
+        os.remove(img_path) ## partial cache, will not be saved in drive
+
+    # log the eval metrics in mlflow
+    def evaluate_and_log_metrics(y_true, y_pred, model_type, val_name, run_id):
+        acc = accuracy_score(y_true, y_pred)
+        prec = precision_score(y_true, y_pred, average='weighted', zero_division=0)
+        rec = recall_score(y_true, y_pred, average='weighted', zero_division=0)
+        f1 = f1_score(y_true, y_pred, average='weighted', zero_division=0)
+
+        mlflow.log_metric(f'{model_type}_{run_id}_accuracy', acc)
+        mlflow.log_metric(f'{model_type}_{run_id}_precision', prec)
+        mlflow.log_metric(f'{model_type}_{run_id}_recall', rec)
+        mlflow.log_metric(f'{model_type}_{run_id}_f1_score', f1)
+
+        report = classification_report(y_true, y_pred)
+        report_path = f'classification_report_{model_type}_{val_name}_{run_id}.txt'
+        with open(report_path, 'w') as f:
+            f.write(report)
+        mlflow.log_artifact(report_path)
+        os.remove(report_path)
+
+        return acc, prec, rec, f1
+    """
+
+    st.markdown("### Helper Functions for MLflow")
+    st.code(helper_code, language="python")
+
+    train_code = """
+    
+    ## init a function for mlflow tracking
+    def train(run_name='default_run', preprocessor='tfidf',model_type='svm', cv_folds=5):
+        with mlflow.start_run(run_name=run_name) as run:
+            run_id = run.info.run_id
+        vectorizer = TfidfVectorizer(ngram_range=(1,2)) if preprocessor == 'tfidf' else CountVectorizer()
+
+        ## IMPORTANT CHANGE THIS TO IMPROVE ACCURACY
+        #hyper-parameter tuning through gridsearch CV initialize here; call also model
+        if model_type == 'svm':
+            model = SVC(probability=True)
+            param_grid = {
+                'classifier__C': [0.1, 1, 10],
+                'classifier__kernel': ['linear', 'rbf']
+            }
+
+        elif model_type == 'random_forest':
+        model = RandomForestClassifier(class_weight='balanced')
+        param_grid = {
+            'classifier__n_estimators': [50, 100, 200],
+            'classifier__max_depth': [None, 10, 20],
+            'classifier__min_samples_split': [2, 5, 10],
+            'classifier__min_samples_leaf': [1, 5, 10]
+        }
+
+        elif model_type == 'complementNB':
+        model = ComplementNB()
+        param_grid = {
+            'classifier__alpha': [0.3, 0.5, 1.0, 1.5],
+        }
+
+        elif model_type == 'multinomialNB':
+        model = MultinomialNB()
+        param_grid = {
+            'classifier__alpha': [0.3, 0.5, 1.0, 1.5],
+        }
+        else:
+        raise ValueError(f"Invalid model type: {model_type}")
+
+        pipeline = Pipeline([
+            ('vectorizer', vectorizer),
+            ('classifier', model)
+        ])
+
+        # calling the stratifieed k-fold cv and grid search
+        strat_cv = StratifiedKFold(n_splits=cv_folds, shuffle=True, random_state=42)
+
+        grid_search = GridSearchCV(
+            estimator=pipeline,
+            param_grid=param_grid,
+            cv=strat_cv,
+            scoring='accuracy',
+            n_jobs=-1
+        )
+
+        ## THIS IS THE TRAINING PART IN FUNCTION
+        grid_search.fit(X_train, y_train)
+        best_model = grid_search.best_estimator_
+
+        ## LOGGING PARAMS IN MLFLOW
+        mlflow.log_param("preprocessor", preprocessor)
+        mlflow.log_param("model_type", model_type)
+        mlflow.log_params(grid_search.best_params_)
+
+        mlflow.log_metric("best_cv_accuracy", grid_search.best_score_)
+
+        plot_cv_perf(grid_search.cv_results_, run_id) ## variables needed for function: cv_results,run_id
+
+        # ! Validation set evaluation !
+        val_preds = best_model.predict(X_val)
+        evaluate_and_log_metrics(y_val, val_preds, model_type, 'val', run_id) ## variables needed for function: y_true, y_pred, model_type, val_name, run_id
+        plot_and_log_cf_matrix(y_val, val_preds, label_encoder.classes_, model_type, run_id) ## variables needed for function: y_true, y_pred, labels, model_type, run_id
+        val_acc = accuracy_score(y_val, val_preds)
+        val_f1 = f1_score(y_val, val_preds, average='weighted', zero_division=0)
+
+
+        # ! Test set evaluation !
+        test_preds=best_model.predict(X_test)
+        evaluate_and_log_metrics(y_test, test_preds, model_type, 'test', run_id) ## variables needed for function: y_true, y_pred, model_type, val_name, run_id
+        plot_and_log_cf_matrix(y_test, test_preds, label_encoder.classes_, model_type, run_id)
+        test_acc = accuracy_score(y_test, test_preds)
+        test_f1 = f1_score(y_test, test_preds, average='weighted', zero_division=0)
+
+        #save best model
+        mlflow.sklearn.log_model(best_model, f"best_model for run {run_name}")
+        print(f"\n\n--- RUN TEST FOR {run_name} using {preprocessor} and {model_type}; {run_id} ---")
+        print(f"Best CV Accuracy: {grid_search.best_score_:.4f}")
+        print(f"Validation Accuracy: {val_acc:.4f}, F1: {val_f1:.4f}")
+        print(f"Test Accuracy: {test_acc:.4f}, F1: {test_f1:.4f}")
+        print(f"Best Parameters: {grid_search.best_params_}\n\n")
+    """
+    st.markdown("### Training Pipeline Made with MLflow configurations")
+    st.code(train_code, language="python")
+
+    sample_run = """
+    
+    --- RUN TEST FOR multinomialNB_v2 using tfidf and multinomialNB; 747c6ac17b154a9284aad6f69b62e38a ---
+    Best CV Accuracy: 0.9268
+    Validation Accuracy: 0.9324, F1: 0.9214
+    Test Accuracy: 0.9339, F1: 0.9235
+    Best Parameters: {'classifier__alpha': 0.3}
+
+
+    🏃 View run multinomialNB_v2 at: http://localhost:2000/#/experiments/616295389774529782/runs/747c6ac17b154a9284aad6f69b62e38a
+    🧪 View experiment at: http://localhost:2000/#/experiments/616295389774529782
+    2025/07/28 14:30:00 WARNING mlflow.models.model: `artifact_path` is deprecated. Please use `name` instead.
+    2025/07/28 14:30:04 WARNING mlflow.models.model: Model logged without a signature and input example. Please set `input_example` parameter when logging the model to auto infer the model signature.
+
+
+    --- RUN TEST FOR complementNB_v2 using tfidf and complementNB; 71b18fe5ba9f4dd38d90c3033c298963 ---
+    Best CV Accuracy: 0.9541
+    Validation Accuracy: 0.9577, F1: 0.9564
+    Test Accuracy: 0.9645, F1: 0.9641
+    Best Parameters: {'classifier__alpha': 0.3}
+
+
+    🏃 View run complementNB_v2 at: http://localhost:2000/#/experiments/616295389774529782/runs/71b18fe5ba9f4dd38d90c3033c298963
+    🧪 View experiment at: http://localhost:2000/#/experiments/616295389774529782
+    2025/07/28 14:33:22 WARNING mlflow.models.model: `artifact_path` is deprecated. Please use `name` instead.
+    2025/07/28 14:33:26 WARNING mlflow.models.model: Model logged without a signature and input example. Please set `input_example` parameter when logging the model to auto infer the model signature.
+
+
+    --- RUN TEST FOR random_forest_v2 using tfidf and random_forest; e2ddf375fb104e8881b25d0cdbd5c2ac ---
+    Best CV Accuracy: 0.9522
+    Validation Accuracy: 0.9563, F1: 0.9566
+    Test Accuracy: 0.9675, F1: 0.9678
+    Best Parameters: {'classifier__max_depth': None, 'classifier__min_samples_leaf': 1, 'classifier__min_samples_split': 2, 'classifier__n_estimators': 50}
+
+
+    🏃 View run random_forest_v2 at: http://localhost:2000/#/experiments/616295389774529782/runs/e2ddf375fb104e8881b25d0cdbd5c2ac
+    🧪 View experiment at: http://localhost:2000/#/experiments/616295389774529782
+    2025/07/28 14:34:20 WARNING mlflow.models.model: `artifact_path` is deprecated. Please use `name` instead.
+    2025/07/28 14:34:25 WARNING mlflow.models.model: Model logged without a signature and input example. Please set `input_example` parameter when logging the model to auto infer the model signature.
+
+
+    --- RUN TEST FOR svm_v2 using tfidf and svm; 13449e470cb24e6c8839ca883fcf43c5 ---
+    Best CV Accuracy: 0.9474
+    Validation Accuracy: 0.9606, F1: 0.9579
+    Test Accuracy: 0.9665, F1: 0.9646
+    Best Parameters: {'classifier__C': 10, 'classifier__kernel': 'linear'}
+
+
+    🏃 View run svm_v2 at: http://localhost:2000/#/experiments/616295389774529782/runs/13449e470cb24e6c8839ca883fcf43c5
+    🧪 View experiment at: http://localhost:2000/#/experiments/616295389774529782
+    """
+
+    st.markdown("### Example output of run of cell; Check also sample of mflow ui")
+    st.code(sample_run, language="python")
+    img1 = Image.open("img/mlflow_ui.png")
+    st.image(img1, caption="MLFlow UI", use_container_width=True)
+
+
+with ModelEvaluationTAB:
+    st.markdown("## Summary of best model configuration and model metrics")
+    st.markdown("""
+    The model training above already provides how the model metrics are extracted; All evaluation metrics and visualization are saved as artificats under `mlflow`. 
+    
+    With this, presented below is the training summary done under initial run parameters for `preprocessor=tfidf` and `cv_folds=5` for all models considered of the study.
+
+    Models considered are the following: `complement_NB (cNB)`, `multinomial_NB (mNB)`, `random_forest (rf)`, `support_vector_machine (svm)`
+                """)
+
+    st.markdown("""
+                
+    ### Conclusion: 
+    - The best model that performed well are `rf` and `svm` classifiers with :blue-background [val_accuracy of both 0.95 and test_accuracy of both 0.97]. 
+    - Assumption for the high accuracy is that these models covered many hyperparameters during training and therefore it was able to find the optimal hyperparameters for the accuracy.
+    - Another key metric being checked is the precision and recall for the `ham` class. Due to the dataset having a class imbalance, its performance on recall is vital to determine how it deals with true positives and its classification for the minority class which is the ham messages.
+    -This is bad practice but given the scarcity and inability to do sampling techniques properly to combat the imbalance (like SMOTE), we are accepting precision and recall that are similar of value to each other.
+    - Based on these insights :blue-background[both rf and svm models] are the best models for this run as they were able to achieve _acceptable values_ for both validation and test evaluations.
+                """)
+
+    st.markdown("---")
+    st.markdown("## 1st Run")
+
+    st.markdown(
+        """This run used the default `ngram_range` for the TF-IDF vectorizer, which is `(1, 1)`. This means that only single-word tokens `(unigrams)` were considered during feature extraction."""
+    )
+
+    st.markdown(""" 
+               ### 1st_Run Validation Accuracy
+
+    | models | val_accuracy | precision_0 | recall_0 | f1-score | support | hyper_params | tfidf_ngram_range |
+    |---|---|---|---|---|---|---|---|
+    | cNB | 0.94 | 0.79 | 0.73 | 0.76 | {0:90,1:620} | 'classifier__alpha': [0.3, 0.5, 1.0, 1.5] | (1,1) |
+    | mNB | 0.93 | 0.98 | 0.44 | 0.66 | {0:90,1:620} | 'classifier__alpha': [0.3, 0.5, 1.0, 1.5] | (1,1) |
+    | rf | 0.95 | 0.85 | 0.79 | 0.82 | {0:90,1:620} | 'classifier__n_estimators':[50, 100, 200]<br>'classifier__max_depth':[None, 10, 20]<br>'classifier__min_samples_split':[1,2,5,10] | (1,1) |
+    | svm | 0.95 | 0.89 | 0.72 | 0.80 | {0:90,1:620} | 'classifier__C': [0.1, 1, 10]<br>'classifier__kernel': ['linear','rbf'] | (1,1) |               
+                               """)
+    st.markdown("""
+                ### 1st_Run Test Accuracy
+
+    | models | test_accuracy | precision_0 | recall_0 | f1-score_0 | support | hyper_params | tfidf_ngram_range |
+    |---|---|---|---|---|---|---|---|
+    | cNB | 0.96 | 0.84 | 0.80 | 0.82 | {0:125,1:889} | 'classifier__alpha': [0.3, 0.5, 1.0, 1.5] | (1,1) |
+    | mNB | 0.93 | 0.98 | 0.48 | 0.65 | {0:125,1:889} | 'classifier__alpha': [0.3, 0.5, 1.0, 1.5] | (1,1) |
+    | rf | 0.97 | 0.92 | 0.85 | 0.88 | {0:125,1:889} | 'classifier__n_estimators':[50, 100, 200]<br>'classifier__max_depth':[None, 10, 20]<br>'classifier__min_samples_split':[1,2,5,10] | (1,1) |
+    | svm | 0.97 | 0.90 | 0.81 | 0.85 | {0:125,1:889} | 'classifier__C': [0.1, 1, 10]<br>'classifier__kernel': ['linear','rbf'] | (1,1) |
+                """)
+
+    st.markdown("#### SVM Performance Metrics")
+    img2 = Image.open(
+        "img/1_run/svm/confusion_matrix_svm_cd28b67a16254e80b055bf50bab61d3d.png"
+    )
+    img3 = Image.open(
+        "img/1_run/svm/cv_performance_svm_cd28b67a16254e80b055bf50bab61d3d.png"
+    )
+    col1, col2 = st.columns(2)
+    with col1:
+        st.image(img2, caption="Confusion Matrix", use_container_width=True)
+    with col2:
+        st.image(img3, caption="CV Performance", use_container_width=True)
+
+    st.markdown("#### RF Performance Metrics")
+    img4 = Image.open(
+        "img/1_run/rf/confusion_matrix_random_forest_5d8f4c16e8aa4120b3eede32f17bbf78.png"
+    )
+    img5 = Image.open(
+        "img/1_run/rf/cv_performance_5d8f4c16e8aa4120b3eede32f17bbf78.png"
+    )
+    col1, col2 = st.columns(2)
+    with col1:
+        st.image(img4, caption="Confusion Matrix", use_container_width=True)
+    with col2:
+        st.image(img5, caption="CV Performance", use_container_width=True)
+
+    st.markdown("#### Multinomial Naive Bayes Performance Metrics")
+    img6 = Image.open(
+        "img/1_run/mNB/confusion_matrix_multinomialNB_981ba728e07048f48cf7ab2f9c9cc559.png"
+    )
+    img7 = Image.open(
+        "img/1_run/mNB/cv_performance_981ba728e07048f48cf7ab2f9c9cc559.png"
+    )
+    col1, col2 = st.columns(2)
+    with col1:
+        st.image(img6, caption="Confusion Matrix", use_container_width=True)
+    with col2:
+        st.image(img7, caption="CV Performance", use_container_width=True)
+
+    st.markdown("#### Complement Naive Bayes Performance Metrics")
+    img8 = Image.open(
+        "img/1_run/cNB/confusion_matrix_complementNB_af86bad539074f32bc26a9410173d98a.png"
+    )
+    img9 = Image.open(
+        "img/1_run/cNB/cv_performance_af86bad539074f32bc26a9410173d98a.png"
+    )
+    col1, col2 = st.columns(2)
+    with col1:
+        st.image(img8, caption="Confusion Matrix", use_container_width=True)
+    with col2:
+        st.image(img9, caption="CV Performance", use_container_width=True)
+
+    st.markdown("---")
+    st.write("## 2nd Run")
+    st.markdown("""
+                For this second run, the  `ngram_range` for the TF-IDF vectorizer was changed, which is now `(1, 2)`. 
+                This means that unigrams from previous run and two-word tokens `(bigrams)` were considered during feature extraction.
+                """)
+
+    st.markdown("""
+                ### 2nd_Run Validation Accuracy
+
+    | models | val_accuracy | precision_0 | recall_0 | f1-score_0 | support | hyper_params | tfidf_ngram_range |
+    |---|---|---|---|---|---|---|---|
+    | cNB | 0.96 | 0.88 | 0.77 | 0.82 | {0:90,1:620} | 'classifier__alpha': [0.3, 0.5, 1.0, 1.5] | (1,2) |
+    | mNB | 0.93 | 1.00 | 0.47 | 0.64 | {0:90,1:620} | 'classifier__alpha': [0.3, 0.5, 1.0, 1.5] | (1,2) |
+    | rf | 0.95 | 0.85 | 0.79 | 0.82 | {0:90,1:620} | 'classifier__n_estimators':[50, 100, 200]<br>'classifier__max_depth':[None, 10, 20]<br>'classifier__min_samples_split':[1,2,5,10] | (1,2) |
+    | svm | 0.95 | 0.89 | 0.72 | 0.80 | {0:90,1:620} | 'classifier__C': [0.1, 1, 10]<br>'classifier__kernel': ['linear','rbf'] | (1,2) |
+                """)
+    st.markdown("""
+                ### 2nd_Run Test Accuracy
+
+    | models | val_accuracy | precision_0 | recall_0 | f1-score_0 | support | hyper_params | tfidf_ngram_range |
+    |---|---|---|---|---|---|---|---|
+    | cNB | 0.96 | 0.87 | 0.83 | 0.85 | {0:125,1:889} | 'classifier__alpha': [0.3, 0.5, 1.0, 1.5] | (1,2) |
+    | mNB | 0.96 | 0.87 | 0.83 | 0.85 | {0:125,1:889} | 'classifier__alpha': [0.3, 0.5, 1.0, 1.5] | (1,2) |
+    | rf | 0.97 | 0.90 | 0.87 | 0.89 | {0:125,1:889} | 'classifier__n_estimators':[50, 100, 200]<br>'classifier__max_depth':[None, 10, 20]<br>'classifier__min_samples_split':[1,2,5,10] | (1,2) |
+    | svm | 0.97 | 0.90 | 0.81 | 0.85 | {0:125,1:889} | 'classifier__C': [0.1, 1, 10]<br>'classifier__kernel': ['linear','rbf'] | (1,2) |                
+                """)
+    st.markdown("#### SVM Performance Metrics")
+    img10 = Image.open(
+        "img/2_run/svm/confusion_matrix_svm_23523aa756d14d508efc9d7460aa2496.png"
+    )
+    img11 = Image.open(
+        "img/2_run/svm/cv_performance_23523aa756d14d508efc9d7460aa2496.png"
+    )
+    col1, col2 = st.columns(2)
+    with col1:
+        st.image(img10, caption="Confusion Matrix", use_container_width=True)
+    with col2:
+        st.image(img11, caption="CV Performance", use_container_width=True)
+
+    st.markdown("#### RF Performance Metrics")
+    img13 = Image.open(
+        "img/2_run/rf/confusion_matrix_random_forest_83f0e0d7df324e37908e3c80a008e51b.png"
+    )
+    img14 = Image.open(
+        "img/2_run/rf/cv_performance_83f0e0d7df324e37908e3c80a008e51b.png"
+    )
+    col1, col2 = st.columns(2)
+    with col1:
+        st.image(img13, caption="Confusion Matrix", use_container_width=True)
+    with col2:
+        st.image(img14, caption="CV Performance", use_container_width=True)
+
+    st.markdown("#### Multinomial Naive Bayes Performance Metrics")
+    img15 = Image.open(
+        "img/2_run/mNB/confusion_matrix_multinomialNB_0bd6e47a7ec541b590db14519fded6aa.png"
+    )
+    img16 = Image.open(
+        "img/2_run/mNB/cv_performance_0bd6e47a7ec541b590db14519fded6aa.png"
+    )
+    col1, col2 = st.columns(2)
+    with col1:
+        st.image(img15, caption="Confusion Matrix", use_container_width=True)
+    with col2:
+        st.image(img16, caption="CV Performance", use_container_width=True)
+
+    st.markdown("#### Complement Naive Bayes Performance Metrics")
+    img17 = Image.open(
+        "img/2_run/cNB/confusion_matrix_complementNB_f99dedcca3404ec483a07fe2773c9194.png"
+    )
+    img18 = Image.open(
+        "img/2_run/cNB/cv_performance_f99dedcca3404ec483a07fe2773c9194.png"
+    )
+    col1, col2 = st.columns(2)
+    with col1:
+        st.image(img17, caption="Confusion Matrix", use_container_width=True)
+    with col2:
+        st.image(img18, caption="CV Performance", use_container_width=True)
+
+with ConTAB:
+    st.markdown("""
+                ## Conclusion
+
+    The project was able to achieve spam classifiers specific to the filipino-context using three datasets that can be a direct tool for assessement for the SIM registration act of 2022.
+
+    This current project was able to do the following unique implementations that stood among the other related projects related to this topic to localize spam classification:
+    - additional EDA insights for the state of the filipino-context messages like plotly graphs and tfidf heatmaps to determine whic
+    - train-val-test cross-validation training with hyperparameter tuning directly using the `mlflow` package
+    - Considers traditional machine learning classifiers of the two NB variants, SVM, and RF.
+    - demo app available in `HuggingFace Space` for further collaboration and the feedback to target stakeholders which are SIM users.""")
+
+    st.markdown("""
+        ## Recommendations
+
+    Despite the novelty given above, the project can be further improved on these following aspects:
+    - There is a class imbalance by 3x spam than ham class due to sources. Additional data sources to make spam a minority class will significantly improve the evaluation metric particularly on the recall that undermines the true negatives
+    - The use of advanced NLP techniques that consider the whole context of the sprontence like BERT embeddings;
+    - Tuning the hyperparameters further to greatly scope the potential improvement on evaluation metrics
+    - The use of deep learning models and XAI techniques to improve accuracy and transparency or even fine-tune models like DOST-ASTI's roBERTa sentiment analysis that can be used for classification problem
+    - conversion of XML data directly from extraction into a data visualization with the use of trained classifiers to map out spam/ham in a timeseries plot. (was hoping to do that! will do it talaga when I have time! -Ferds)
+                    """)
+
+    st.markdown("""
+                ### References: Kindly check them below!
+                
+    For people that will check this project in the future and seek inspiration, we have consolidated below the references we deem helpful for the development of further spam classifications in the filipino context to combat spammers and scammers!       
+    
+    [Filipino-Spam-SMS-Detection-Model](https://github.com/Yissuh/Filipino-Spam-SMS-Detection-Model/tree/main)
+    - A text mining project implemented for Data Analytics course as a third year student in Technological University of the Philippines - Manila. The project focuses on developing a localized spam SMS detection in the Philippines.
+
+    [SIM Spam Slam](https://ktrin-u.github.io/CS132-Arbitrary/) 
+    - A simple website done by UP Students also did a scam classification on chosen datasets; consider what we are doing as a replication study of what they made with the dataset updated (kudos for the maintainers); We werent able to do the clustering and other methods they have done.
+
+    [Philippine-Scam-Analysis-ML](https://github.com/HiroshiJoe/Philippine-Scam-Analysis-ML/blob/main/PH_Scam_Analysis_and_Machine_Learning.ipynb)
+    - This repository includes analysis and models for distinguishing "Scam" from "Non-scam" messages. 
+    The data, originally extracted from images via OCR for the ScamGuard project, is used here to apply 
+    sentiment analysis and machine learning techniques to improve scam detection.
+
+    [A Machine Learning Approach for Efficient Spam Detection in Short Messaging System (SMS)](https://ieeexplore.ieee.org/document/10322491)
+
+    - A conference paper done by PUP Batangas students. Based on their abstract, Short Message Service (SMS) 
+    is widely used due to its affordability and convenience, but spam messages pose serious risks such as fraud, 
+    identity theft, and financial loss. To address this, machine learning models were applied to classify and filter 
+    spam from legitimate messages. The dataset consisted of 60% ham and 40% spam, combining internet-sourced and 
+    self-collected samples. Among the models tested, Bernoulli Naive Bayes achieved the best results with 96.63% accuracy after optimization.         
+                """)

requirements.txt

@@ -0,0 +1,7 @@
+streamlit>=1.27.0
+pandas>=1.3.0
+numpy>=1.21.0
+scikit-learn>=1.0
+plotly>=5.9.0
+wordcloud>=1.8.1
+Pillow>=8.0.0

src/combined_stopwords.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:917dd0dc66e3c64d7ad4cf70be1eda1b1f17b4806b0b7223915950e3ba33f412
+size 3820

src/test.csv

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