Local ML Model Trainer Interface

import streamlit as st

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

import seaborn as sns

from sklearn.model_selection import train_test_split

from sklearn.metrics import (

    accuracy_score, precision_score, recall_score, f1_score,

    mean_squared_error, confusion_matrix

)

from sklearn.preprocessing import StandardScaler

from sklearn.linear_model import LogisticRegression, LinearRegression

from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor

from sklearn.svm import SVC, SVR

from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor

st.set_page_config(page_title="Local ML Model Trainer", layout="wide")

st.title("Local ML Model Trainer Interface")

st.write("Upload a dataset → choose an algorithm → train → view results")

# ────────────────────────────────────────────────

# Upload Dataset

# ────────────────────────────────────────────────

uploaded_file = st.file_uploader("📤 Upload CSV Dataset", type=["csv"])

if uploaded_file:

    df = pd.read_csv(uploaded_file)

    st.success("Dataset Loaded Successfully!")

    st.write("###  Data Preview")

    st.dataframe(df.head())

    st.write("###  Dataset Info")

    st.write(df.describe())

    # Target column selection

    target_col = st.selectbox(" Select Target Column (Y)", df.columns)

    # Feature columns

    X = df.drop(columns=[target_col])

    y = df[target_col]

    # Auto detect problem type

    if df[target_col].dtype == object or df[target_col].nunique() < 15:

        problem_type = "classification"

    else:

        problem_type = "regression"

    st.info(f"Detected Problem Type: **{problem_type.upper()}**")

    # Choose model based on problem type

    if problem_type == "classification":

        model_choice = st.selectbox(

            "Choose Model",

            ["Logistic Regression", "Random Forest Classifier", "SVM Classifier", "KNN Classifier"]

        )

    else:

        model_choice = st.selectbox(

            "Choose Model",

            ["Linear Regression", "Random Forest Regressor", "SVM Regressor", "KNN Regressor"]

        )

    test_size = st.slider("Test Size (Train %)", 0.1, 0.5, 0.2)

    # Train button

    if st.button(" Train Model"):

        # Preprocessing

        scaler = StandardScaler()

        X_scaled = scaler.fit_transform(X.select_dtypes(include=np.number))

        X_train, X_test, y_train, y_test = train_test_split(

            X_scaled, y, test_size=test_size, random_state=42

        )

        # Model Selection

        if model_choice == "Logistic Regression":

            model = LogisticRegression()

        elif model_choice == "Random Forest Classifier":

            model = RandomForestClassifier()

        elif model_choice == "SVM Classifier":

            model = SVC()

        elif model_choice == "KNN Classifier":

            model = KNeighborsClassifier()

        elif model_choice == "Linear Regression":

            model = LinearRegression()

        elif model_choice == "Random Forest Regressor":

            model = RandomForestRegressor()

        elif model_choice == "SVM Regressor":

            model = SVR()

        elif model_choice == "KNN Regressor":

            model = KNeighborsRegressor()

        # Train

        model.fit(X_train, y_train)

        y_pred = model.predict(X_test)

        st.success("Model Trained Successfully!")

        # ────────────────────────────────────────────────

        # Show Metrics

        # ────────────────────────────────────────────────

        st.write("## 📈 Model Performance")

        if problem_type == "classification":

            st.write("### 🔹 Classification Metrics")

            st.write(f"Accuracy: **{accuracy_score(y_test, y_pred):.4f}**")

            st.write(f"Precision: **{precision_score(y_test, y_pred, average='weighted'):.4f}**")

            st.write(f"Recall: **{recall_score(y_test, y_pred, average='weighted'):.4f}**")

            st.write(f"F1 Score: **{f1_score(y_test, y_pred, average='weighted'):.4f}**")

            # Confusion Matrix

            cm = confusion_matrix(y_test, y_pred)

            fig, ax = plt.subplots(figsize=(5, 4))

            sns.heatmap(cm, annot=True, fmt="d", cmap="Blues", ax=ax)

            st.write("###  Confusion Matrix")

            st.pyplot(fig)

        else:

            st.write("### 🔹 Regression Metrics")

            rmse = np.sqrt(mean_squared_error(y_test, y_pred))

            st.write(f"RMSE: **{rmse:.4f}**")

        # ────────────────────────────────────────────────

        # Feature Importance (for tree models)

        # ────────────────────────────────────────────────

        if "Forest" in model_choice:

            st.write("##  Feature Importance")

            importance = model.feature_importances_

            fig, ax = plt.subplots(figsize=(6, 4))

            sns.barplot(x=importance, y=X.columns, ax=ax)

            st.pyplot(fig)