Modelmetrics hub

app.py

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+import streamlit as st
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.svm import SVC
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import accuracy_score
+
+# Function to build and evaluate models
+def build_and_evaluate_model(model, X_train, X_test, y_train, y_test):
+    try:
+        model.fit(X_train, y_train)
+        y_pred = model.predict(X_test)
+        # Convert y_pred to the same data type as y_test for comparison
+        y_pred = y_pred.astype(str)  #Used in data manipulation
+        accuracy = accuracy_score(y_test.astype(str), y_pred)
+        return accuracy
+    except Exception as e:
+        return f"Not Applicable: {str(e)}"
+
+# Streamlit app
+def main():
+        # Custom CSS for styling the title
+    custom_css = """
+        <style>
+            .title-text {
+                font-size: 55px;
+                font-weight: bold;
+                color: #FF0A01; /* Dark Blue */
+                text-align: center;
+                margin-bottom: 0px;
+            }
+            .tagline-text {
+                font-size: 18px;
+                font-style: italic;
+                color: #2c3e50; /* White */
+                text-align: center;
+                margin-top:-10px;
+                margin-bottom: 20px;
+            }
+        </style>
+    """
+
+    # Streamlit app
+    st.markdown(custom_css, unsafe_allow_html=True)
+    # Title and tagline
+    st.markdown("<p class='title-text'>ModelMetrics Hub</p>", unsafe_allow_html=True)
+    st.markdown("<p class='tagline-text'>Optimal Model Explorer</p>", unsafe_allow_html=True)
+
+    #st.title("ModelMetrics Hub \n _Optimal Model Explorer_")
+    st.sidebar.title("Model Selection")
+    model_names = ["Random Forest", "Decision Tree", "KNN", "SVM", "Logistic Regression"]
+    selected_models = st.sidebar.multiselect("Choose models to compare", model_names, default=model_names)
+
+    uploaded_file = st.file_uploader("Upload a CSV file", type=["csv"])
+    if uploaded_file is not None:
+        data = pd.read_csv(uploaded_file, encoding='latin-1')  # Specify encoding here
+        st.write("Preview of the dataset:")
+        st.write(data.head())
+
+        feature_columns = st.multiselect("Select feature columns", data.columns.tolist())
+        target_column = st.selectbox("Select target column", data.columns.tolist())
+
+        if st.button("Run Models"):
+            X = data[feature_columns]
+            y = data[target_column]
+
+            y = y.astype(str)
+
+            # Perform one-hot encoding for categorical columns
+            categorical_cols = X.select_dtypes(include=['object']).columns.tolist()
+            X = pd.get_dummies(X, columns=categorical_cols, drop_first=True)
+
+            X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+            models = {
+                "Random Forest": RandomForestClassifier(n_estimators=100, random_state=42),
+                "Decision Tree": DecisionTreeClassifier(random_state=42),
+                "KNN": KNeighborsClassifier(n_neighbors=5),
+                "SVM": SVC(kernel='rbf', random_state=42),
+                "Logistic Regression": LogisticRegression(max_iter=1000, random_state=42)
+            }
+
+            results = {}
+            for model_name in selected_models:
+                accuracy = build_and_evaluate_model(models[model_name], X_train, X_test, y_train, y_test)
+                results[model_name] = accuracy
+
+            sorted_results = sorted(results.items(), key=lambda x: x[1], reverse=True)  #key=lambda x: is a custom sorting order 
+
+            st.subheader("Accuracy")
+            for model_name, accuracy in sorted_results:
+                st.write(f"{model_name}: {accuracy * 100:.2f}%")
+
+if __name__ == "__main__":
+    main()