Improved Error Handling

To get the error message that I updated in the code, you can follow these steps:

Scenario 1: File Loading Error

If there's an issue loading the CSV file (e.g., it's missing, corrupted, or not a valid CSV file), the st.error function will display a message like:
lua
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An error occurred while loading data: <specific error message>
The <specific error message> is the exact Python exception raised by the error, such as FileNotFoundError, PermissionError, ParserError or pd.errors.EmptyDataError.
Scenario 2: Prediction Error

If an error occurs during the prediction process (e.g., model input mismatch or other issues during prediction), the st.error function will display:
go
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An error occurred during prediction: <specific error message>
This will also show the specific Python error encountered, such as ValueError or TypeError.

Author: Ankush0286

opensource_analysis/app.py

@@ -18,134 +18,138 @@
 if not os.path.exists(file_path):
     st.error(f"File not found: {file_path}. Please ensure the file is in the correct directory.")
 else:
-    # Load the dataset
-    data = pd.read_csv(file_path)
-
-    # Define the necessary columns
-    columns = ['Employment', 'FormalEducation', 'CompanySize', 'DevType', 'Exercise', 'Age', 'OpenSource']
-    data = data[columns].copy()
-
-    # Map age values to numerical values
-    age_mapping = {
-        'Under 18 years old': 0,
-        '18 - 24 years old': 1,
-        '25 - 34 years old': 2,
-        '35 - 44 years old': 3,
-        '45 - 54 years old': 4,
-        '55 - 64 years old': 5,
-        '65 years or older': 6
-    }
-    data['Age'] = data['Age'].map(age_mapping)
-
-    # Define target variable and feature columns
-    target_variable = 'OpenSource'
-    categorical_features = ['Employment', 'FormalEducation', 'CompanySize', 'DevType', 'Exercise', 'Age']
-    numerical_features = []
-
-    # Preprocessing for categorical data
-    preprocessor = ColumnTransformer(
-        transformers=[
-            ('cat', OneHotEncoder(handle_unknown='ignore'), categorical_features)
-        ]
-    )
-
-    # Split the data
-    X = data.drop(target_variable, axis=1)
-    y = data[target_variable]
-    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
-
-    # Create and train the model
-    model = Pipeline(steps=[
-        ('preprocessor', preprocessor),
-        ('classifier', RandomForestClassifier(random_state=42))
-    ])
-    model.fit(X_train, y_train)
-
-    # Evaluate the model
-    y_pred = model.predict(X_test)
-    classification_rep = classification_report(y_test, y_pred)
-    roc_auc = roc_auc_score(y_test, model.predict_proba(X_test)[:, 1])
-
-    # Get feature importance
-    importances = model.named_steps['classifier'].feature_importances_
-    feature_names = list(model.named_steps['preprocessor'].transformers_[0][1].get_feature_names_out())
-    feature_importance_df = pd.DataFrame({'Feature': feature_names, 'Importance': importances}).sort_values(by='Importance', ascending=False)
-
-    # Streamlit App
-    st.title('Machine Learning Model Evaluation')
-
-    # Show classification report
-    st.header('Classification Report')
-    st.text(classification_rep)
-
-    # Show ROC-AUC Score
-    st.header('ROC-AUC Score')
-    st.text(f"ROC-AUC Score: {roc_auc:.2f}")
-
-    # Plot confusion matrix
-    st.header('Confusion Matrix')
-    cm = confusion_matrix(y_test, y_pred)
-    fig, ax = plt.subplots()
-    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=['No', 'Yes'], yticklabels=['No', 'Yes'], ax=ax)
-    plt.xlabel('Predicted')
-    plt.ylabel('Actual')
-    st.pyplot(fig)
-
-    # Plot ROC Curve
-    st.header('ROC Curve')
-    y_test_binary = y_test.map({'No': 0, 'Yes': 1})
-    fpr, tpr, _ = roc_curve(y_test_binary, model.predict_proba(X_test)[:, 1])
-    roc_auc = auc(fpr, tpr)
-    fig, ax = plt.subplots()
-    ax.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC curve (area = {roc_auc:.2f})')
-    ax.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
-    ax.set_xlim([0.0, 1.0])
-    ax.set_ylim([0.0, 1.05])
-    ax.set_xlabel('False Positive Rate')
-    ax.set_ylabel('True Positive Rate')
-    ax.set_title('ROC Curve')
-    ax.legend(loc='lower right')
-    st.pyplot(fig)
-
-    # Plot feature importance
-    st.header('Feature Importance')
-    fig, ax = plt.subplots()
-    sns.barplot(x='Importance', y='Feature', data=feature_importance_df.head(20), palette='viridis', ax=ax)
-    ax.set_title('Top Feature Importances')
-    ax.set_xlabel('Importance')
-    ax.set_ylabel('Feature')
-    st.pyplot(fig)
-
-    # Section for new data input and prediction
-    st.header('Predict for New Data')
-
-    # Input fields for new data
-    employment = st.selectbox('Employment', data['Employment'].unique())
-    education = st.selectbox('Formal Education', data['FormalEducation'].unique())
-    company_size = st.selectbox('Company Size', data['CompanySize'].unique())
-    dev_type = st.selectbox('Dev Type', data['DevType'].unique())
-    exercise = st.selectbox('Exercise', data['Exercise'].unique())
-    age = st.selectbox('Age', list(age_mapping.keys()))
-
-    # Convert inputs to dataframe
-    new_data = pd.DataFrame({
-        'Employment': [employment],
-        'FormalEducation': [education],
-        'CompanySize': [company_size],
-        'DevType': [dev_type],
-        'Exercise': [exercise],
-        'Age': [age_mapping[age]]
-    })
-
-    # Handle any NaN values
-    new_data = new_data.fillna('')
-
-    # Predict the output for new data
-    if st.button('Predict'):
-        try:
-            prediction = model.predict(new_data)
-            prediction_prob = model.predict_proba(new_data)[:, 1]
-            st.write(f'Prediction: {"Yes" if prediction[0] == "Yes" else "No"}')
-            st.write(f'Prediction Probability: {prediction_prob[0]:.2f}')
-        except Exception as e:
-            st.error(f"An error occurred during prediction: {e}")
+    try:
+        # Try to load the dataset
+        data = pd.read_csv(file_path)
+
+        # Define the necessary columns
+        columns = ['Employment', 'FormalEducation', 'CompanySize', 'DevType', 'Exercise', 'Age', 'OpenSource']
+        data = data[columns].copy()
+
+        # Map age values to numerical values
+        age_mapping = {
+            'Under 18 years old': 0,
+            '18 - 24 years old': 1,
+            '25 - 34 years old': 2,
+            '35 - 44 years old': 3,
+            '45 - 54 years old': 4,
+            '55 - 64 years old': 5,
+            '65 years or older': 6
+        }
+        data['Age'] = data['Age'].map(age_mapping)
+
+        # Define target variable and feature columns
+        target_variable = 'OpenSource'
+        categorical_features = ['Employment', 'FormalEducation', 'CompanySize', 'DevType', 'Exercise', 'Age']
+        numerical_features = []
+
+        # Preprocessing for categorical data
+        preprocessor = ColumnTransformer(
+            transformers=[
+                ('cat', OneHotEncoder(handle_unknown='ignore'), categorical_features)
+            ]
+        )
+
+        # Split the data
+        X = data.drop(target_variable, axis=1)
+        y = data[target_variable]
+        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+        # Create and train the model
+        model = Pipeline(steps=[
+            ('preprocessor', preprocessor),
+            ('classifier', RandomForestClassifier(random_state=42))
+        ])
+        model.fit(X_train, y_train)
+
+        # Evaluate the model
+        y_pred = model.predict(X_test)
+        classification_rep = classification_report(y_test, y_pred)
+        roc_auc = roc_auc_score(y_test, model.predict_proba(X_test)[:, 1])
+
+        # Get feature importance
+        importances = model.named_steps['classifier'].feature_importances_
+        feature_names = list(model.named_steps['preprocessor'].transformers_[0][1].get_feature_names_out())
+        feature_importance_df = pd.DataFrame({'Feature': feature_names, 'Importance': importances}).sort_values(by='Importance', ascending=False)
+
+        # Streamlit App
+        st.title('Machine Learning Model Evaluation')
+
+        # Show classification report
+        st.header('Classification Report')
+        st.text(classification_rep)
+
+        # Show ROC-AUC Score
+        st.header('ROC-AUC Score')
+        st.text(f"ROC-AUC Score: {roc_auc:.2f}")
+
+        # Plot confusion matrix
+        st.header('Confusion Matrix')
+        cm = confusion_matrix(y_test, y_pred)
+        fig, ax = plt.subplots()
+        sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=['No', 'Yes'], yticklabels=['No', 'Yes'], ax=ax)
+        plt.xlabel('Predicted')
+        plt.ylabel('Actual')
+        st.pyplot(fig)
+
+        # Plot ROC Curve
+        st.header('ROC Curve')
+        y_test_binary = y_test.map({'No': 0, 'Yes': 1})
+        fpr, tpr, _ = roc_curve(y_test_binary, model.predict_proba(X_test)[:, 1])
+        roc_auc = auc(fpr, tpr)
+        fig, ax = plt.subplots()
+        ax.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC curve (area = {roc_auc:.2f})')
+        ax.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
+        ax.set_xlim([0.0, 1.0])
+        ax.set_ylim([0.0, 1.05])
+        ax.set_xlabel('False Positive Rate')
+        ax.set_ylabel('True Positive Rate')
+        ax.set_title('ROC Curve')
+        ax.legend(loc='lower right')
+        st.pyplot(fig)
+
+        # Plot feature importance
+        st.header('Feature Importance')
+        fig, ax = plt.subplots()
+        sns.barplot(x='Importance', y='Feature', data=feature_importance_df.head(20), palette='viridis', ax=ax)
+        ax.set_title('Top Feature Importances')
+        ax.set_xlabel('Importance')
+        ax.set_ylabel('Feature')
+        st.pyplot(fig)
+
+        # Section for new data input and prediction
+        st.header('Predict for New Data')
+
+        # Input fields for new data
+        employment = st.selectbox('Employment', data['Employment'].unique())
+        education = st.selectbox('Formal Education', data['FormalEducation'].unique())
+        company_size = st.selectbox('Company Size', data['CompanySize'].unique())
+        dev_type = st.selectbox('Dev Type', data['DevType'].unique())
+        exercise = st.selectbox('Exercise', data['Exercise'].unique())
+        age = st.selectbox('Age', list(age_mapping.keys()))
+
+        # Convert inputs to dataframe
+        new_data = pd.DataFrame({
+            'Employment': [employment],
+            'FormalEducation': [education],
+            'CompanySize': [company_size],
+            'DevType': [dev_type],
+            'Exercise': [exercise],
+            'Age': [age_mapping[age]]
+        })
+
+        # Handle any NaN values
+        new_data = new_data.fillna('')
+
+        # Predict the output for new data
+        if st.button('Predict'):
+            try:
+                prediction = model.predict(new_data)
+                prediction_prob = model.predict_proba(new_data)[:, 1]
+                st.write(f'Prediction: {"Yes" if prediction[0] == "Yes" else "No"}')
+                st.write(f'Prediction Probability: {prediction_prob[0]:.2f}')
+            except Exception as e:
+                st.error(f"An error occurred during prediction: {e}")
+
+    except Exception as e:
+        st.error(f"An error occurred while loading data: {e}")