diff --git a/.gitignore b/.gitignore
index 8b29dff..717a0db 100644
--- a/.gitignore
+++ b/.gitignore
@@ -30,3 +30,4 @@ test*.py
 test*.xml
 dspy_agent.py
 !dspy_agent/static/*.png
+!examples.json
diff --git a/dspy_agent/examples.json b/dspy_agent/examples.json
new file mode 100644
index 0000000..b4fe49f
--- /dev/null
+++ b/dspy_agent/examples.json
@@ -0,0 +1,56 @@
+[
+  {
+    "question": "Perform a comprehensive EDA: summary statistics, missing values, data types, correlation matrix, distributions for numerical, counts for categorical. Conclude with 3-5 insights.",
+    "context": "The primary dataset is 'Chowell_train_Response.csv'. It is a 'csv' file. All generated files and plots must be saved to 'outputs_dir/generated_file/'.",
+    "rationale": "Thought: The user wants a comprehensive EDA. I will perform the following steps:\n1. Load the dataset from the path provided in the context.\n2. Calculate summary statistics and save to a CSV.\n3. Check for missing values and save the counts to a CSV.\n4. List data types and save to a CSV.\n5. Compute the correlation matrix for numerical features and save a heatmap.\n6. Plot distributions for key numerical features and save the plots.\n7. Plot counts for key categorical-like integer features (e.g., 'Systemic_therapy_history', 'Response') and save the plots.\n8. Formulate 3-5 insights based on the findings.\nAll outputs will go to 'outputs_dir/generated_file/'. The dataset path will be dynamically provided as 'dataset_path_in_tool_code'.\nAction: python_code_executor(code='import pandas as pd\\nimport numpy as np\\nimport seaborn as sns\\nimport matplotlib.pyplot as plt\\nfrom pathlib import Path\\n\\n# outputs_dir is provided by the PythonCodeTool environment\\noutput_subdir = outputs_dir / \"generated_file\"\\noutput_subdir.mkdir(parents=True, exist_ok=True)\\n\\n# dataset_path_in_tool_code will be the path to the active dataset, e.g., self.current_dataset_file_path\\ndf = pd.read_csv(dataset_path_in_tool_code)\\n\\nsummary_stats = df.describe(include=\\\"all\\\")\\nsummary_stats_file = output_subdir / \\\"eda_summary_statistics.csv\\\"\\nsummary_stats.to_csv(summary_stats_file)\\nprint(f\\\"Summary Statistics saved to {summary_stats_file}\\\")\\n\\nmissing_values = df.isnull().sum()\\nmissing_values_file = output_subdir / \\\"eda_missing_values.csv\\\"\\nmissing_values.to_frame(name=\\\"missing_count\\\").to_csv(missing_values_file)\\nprint(f\\\"Missing Values saved to {missing_values_file}\\\")\\n\\ndata_types = df.dtypes.astype(str)\\ndata_types_file = output_subdir / \\\"eda_data_types.csv\\\"\\ndata_types.to_frame(name=\\\"data_type\\\").to_csv(data_types_file)\\nprint(f\\\"Data Types saved to {data_types_file}\\\")\\n\\nnumerical_df = df.select_dtypes(include=[np.number])\\ncorrelation_matrix = numerical_df.corr()\\nplt.figure(figsize=(12, 10))\\nsns.heatmap(correlation_matrix, annot=True, fmt=\\\".2f\\\", cmap=\\\"coolwarm\\\")\\nplt.title(\\\"Correlation Matrix Heatmap\\\")\\ncorrelation_matrix_plot_file = output_subdir / \\\"eda_correlation_heatmap.png\\\"\\nplt.savefig(correlation_matrix_plot_file)\\nplt.close()\\nprint(f\\\"Correlation Matrix Heatmap saved to {correlation_matrix_plot_file}\\\")\\n\\n# Key numerical features for individual distribution plots\\nkey_numerical_cols = [\\\"TMB\\\", \\\"Albumin\\\", \\\"NLR\\\", \\\"Age\\\"] \\nnum_dist_plots_generated = []\\nfor col in key_numerical_cols:\\n    if col in df.columns:\\n        plt.figure(figsize=(8, 5))\\n        sns.histplot(df[col].dropna(), kde=True)\\n        plt.title(f\\\"{col} Distribution\\\")\\n        dist_path = output_subdir / f\\\"{col}_distribution_eda.png\\\"\\n        plt.savefig(dist_path)\\n        plt.close()\\n        num_dist_plots_generated.append(str(dist_path).replace(str(outputs_dir) + \\\"/\\\", \\\"\\\"))\\nprint(f\\\"Numerical distribution plots saved: {num_dist_plots_generated}\\\")\\n\\n# Key categorical-like features for count plots\\nkey_categorical_cols = [\\\"Systemic_therapy_history\\\", \\\"Response\\\"]\\ncat_count_plots_generated = []\\nfor col in key_categorical_cols:\\n    if col in df.columns:\\n        plt.figure(figsize=(8,5))\\n        sns.countplot(x=df[col])\\n        plt.title(f\\\"{col} Counts\\\")\\n        cat_path = output_subdir / f\\\"{col}_counts_eda.png\\\"\\n        plt.savefig(cat_path)\\n        plt.close()\\n        cat_count_plots_generated.append(str(cat_path).replace(str(outputs_dir) + \\\"/\\\", \\\"\\\"))\\nprint(f\\\"Categorical count plots saved: {cat_count_plots_generated}\\\")')",
+    "final_answer": "{\"explanation\": \"Comprehensive EDA performed. Key insights: 1. Dataset is complete (no missing values). 2. TMB and NLR show right-skewed distributions. 3. 'Response' variable is binary and moderately imbalanced. 4. 'Systemic_therapy_history' and 'TMB' show notable correlations with 'Response'.\", \"plots\": [\"generated_file/eda_correlation_heatmap.png\", \"generated_file/TMB_distribution_eda.png\", \"generated_file/Albumin_distribution_eda.png\", \"generated_file/NLR_distribution_eda.png\", \"generated_file/Age_distribution_eda.png\", \"generated_file/Systemic_therapy_history_counts_eda.png\", \"generated_file/Response_counts_eda.png\"], \"files\": [\"generated_file/eda_summary_statistics.csv\", \"generated_file/eda_missing_values.csv\", \"generated_file/eda_data_types.csv\"], \"next_steps_suggestion\": [\"Investigate TMB distribution per Response group.\", \"Explore transformations for skewed features like TMB and NLR.\", \"What is the exact correlation between Systemic_therapy_history and Response?\"]}"
+  },
+  {
+    "question": "Show the correlation matrix of the features.",
+    "context": "The primary dataset is 'Chowell_train_Response.csv'. It is a 'csv' file. Save plot to 'outputs_dir/generated_file/'.",
+    "rationale": "Thought: The user wants a correlation matrix, visualized as a heatmap. I will: 1. Load the dataset. 2. Select numerical columns. 3. Compute the correlation matrix. 4. Plot it using seaborn.heatmap and save it. I'll also save the matrix data as a CSV for detailed review.\nAction: python_code_executor(code='import pandas as pd\\nimport numpy as np\\nimport seaborn as sns\\nimport matplotlib.pyplot as plt\\nfrom pathlib import Path\\n\\noutput_subdir = outputs_dir / \"generated_file\"\\noutput_subdir.mkdir(parents=True, exist_ok=True)\\n\\ndf = pd.read_csv(dataset_path_in_tool_code) # dataset_path_in_tool_code is the active dataset path\\n\\nnumerical_df = df.select_dtypes(include=[np.number])\\ncorrelation_matrix = numerical_df.corr()\\ncorrelation_matrix_csv_path = output_subdir / \"correlation_matrix_data.csv\"\\ncorrelation_matrix.to_csv(correlation_matrix_csv_path)\\nplt.figure(figsize=(12, 10))\\nsns.heatmap(correlation_matrix, annot=True, fmt=\\\".2f\\\", cmap=\\\"coolwarm\\\")\\nplt.title(\\\"Correlation Matrix of Features\\\")\\nheatmap_plot_path = output_subdir / \\\"correlation_matrix_heatmap_display.png\\\"\\nplt.savefig(heatmap_plot_path)\\nplt.close()\\nprint(f\\\"Heatmap saved to {heatmap_plot_path}, CSV data saved to {correlation_matrix_csv_path}\\\")')",
+    "final_answer": "{\"explanation\": \"The correlation matrix for numerical features has been computed and visualized as a heatmap. The matrix data is also saved as a CSV file. This helps identify linear relationships between pairs of features.\", \"plots\": [\"generated_file/correlation_matrix_heatmap_display.png\"], \"files\": [\"generated_file/correlation_matrix_data.csv\"], \"next_steps_suggestion\": [\"Which features are most correlated with 'Response'?\", \"Are there any pairs with surprisingly low correlation?\", \"Plot scatter plots for highly correlated pairs.\"]}"
+  },
+  {
+    "question": "Plot a histogram of the 'Albumin' column.",
+    "context": "The primary dataset is 'Chowell_train_Response.csv'. It is a 'csv' file. Save plot to 'outputs_dir/generated_file/'.",
+    "rationale": "Thought: The user wants a histogram for the 'Albumin' column. I will: 1. Load the dataset. 2. Check if 'Albumin' column exists and is numerical. 3. Plot its histogram using seaborn.histplot and save it.\nAction: python_code_executor(code='import pandas as pd\\nimport numpy as np\\nimport seaborn as sns\\nimport matplotlib.pyplot as plt\\nfrom pathlib import Path\\n\\noutput_subdir = outputs_dir / \"generated_file\"\\noutput_subdir.mkdir(parents=True, exist_ok=True)\\n\\ndf = pd.read_csv(dataset_path_in_tool_code)\\n\\nif \\\"Albumin\\\" in df.columns and pd.api.types.is_numeric_dtype(df[\\\"Albumin\\\"]):\\n    plt.figure(figsize=(8, 6))\\n    sns.histplot(df[\\\"Albumin\\\"].dropna(), kde=True, bins=30)\\n    plt.title(\\\"Distribution of Albumin\\\")\\n    plt.xlabel(\\\"Albumin\\\")\\n    plt.ylabel(\\\"Frequency\\\")\\n    plot_filename = output_subdir / \\\"albumin_distribution_specific.png\\\"\\n    plt.savefig(plot_filename)\\n    plt.close()\\n    print(f\\\"Histogram of Albumin saved to {plot_filename}\\\")\\nelse:\\n    print(\\\"Error: Albumin column not found or is not numerical.\\\")')",
+    "final_answer": "{\"explanation\": \"A histogram showing the distribution of the 'Albumin' feature has been generated and saved. It visualizes the frequency of different Albumin levels.\", \"plots\": [\"generated_file/albumin_distribution_specific.png\"], \"files\": [], \"next_steps_suggestion\": [\"What are the summary statistics for Albumin?\", \"Compare Albumin distribution across Response groups.\", \"Are there outliers in the Albumin data?\"]}"
+  },
+  {
+    "question": "Are there any missing values in the dataset? If so, which columns contain them?",
+    "context": "The primary dataset is 'Chowell_train_Response.csv'. It is a 'csv' file.",
+    "rationale": "Thought: The user wants to check for missing values. I will: 1. Load the dataset. 2. Calculate the sum of null values for each column. 3. Report if any missing values are found and in which columns. If no missing values are found, I will state that.\nAction: python_code_executor(code='import pandas as pd\\nfrom pathlib import Path\\n\\noutput_subdir = outputs_dir / \"generated_file\"\\noutput_subdir.mkdir(parents=True, exist_ok=True)\\n\\ndf = pd.read_csv(dataset_path_in_tool_code)\\n\\nmissing_values = df.isnull().sum()\\ntotal_missing = missing_values.sum()\\n\\nif total_missing == 0:\\n    result_message = \\\"No missing values found in the dataset.\\\"\\nelse:\\n    result_message = f\\\"Missing values per column:\\\\n{missing_values[missing_values > 0]}\\\\\"\\n\\nprint(result_message)\\n\\n# Save a report even if no missing values\\nmissing_report_path = output_subdir / \\\"missing_values_report.txt\\\"\\nwith open(missing_report_path, \\\"w\\\") as f:\\n    f.write(result_message + f\\\"\\\\nTotal missing values: {total_missing}\\\")\\nprint(f\\\"Missing values report saved to {missing_report_path}\\\")')",
+    "final_answer": "{\"explanation\": \"The dataset 'Chowell_train_Response.csv' was analyzed for missing values. No missing values were found in any of its columns. A report has been saved.\", \"plots\": [], \"files\": [\"generated_file/missing_values_report.txt\"], \"next_steps_suggestion\": [\"Confirm data types for all columns.\", \"Generate summary statistics.\", \"How was missing data handled if it was imputed previously?\"]}"
+  },
+  {
+    "question": "Apply log1p transformation to reduce skewness in 'TMB' and 'NLR' numerical variables. Show comparison plots.",
+    "context": "The primary dataset is 'Chowell_train_Response.csv'. It is a 'csv' file. Save plots to 'outputs_dir/generated_file/'.",
+    "rationale": "Thought: The user wants to reduce skewness for 'TMB' and 'NLR' using log1p transformation and see comparison plots. I will: 1. Load the dataset. 2. For 'TMB' and 'NLR': a. Calculate original skewness. b. Apply log1p transformation. c. Calculate transformed skewness. d. Plot original and transformed distributions side-by-side and save.\nAction: python_code_executor(code='import pandas as pd\\nimport numpy as np\\nimport matplotlib.pyplot as plt\\nimport seaborn as sns\\nfrom pathlib import Path\\n\\noutput_subdir = outputs_dir / \"generated_file\"\\noutput_subdir.mkdir(parents=True, exist_ok=True)\\n\\ndf = pd.read_csv(dataset_path_in_tool_code)\\n\\nfeatures_to_transform = [\\\"TMB\\\", \\\"NLR\\\"]\\nplot_files_generated = []\\ndf_transformed_subset = pd.DataFrame()\\n\\nfor feature in features_to_transform:\\n    if feature in df.columns and pd.api.types.is_numeric_dtype(df[feature]):\\n        original_skew = df[feature].skew()\\n        transformed_column_name = feature + \\\"_log1p\\\"\\n        df_transformed_subset[transformed_column_name] = np.log1p(df[feature])\\n        transformed_skew = df_transformed_subset[transformed_column_name].skew()\\n\\n        plt.figure(figsize=(12, 5))\\n        plt.subplot(1, 2, 1)\\n        sns.histplot(df[feature].dropna(), kde=True, bins=30)\\n        plt.title(f\\\"Original {feature} (Skew: {original_skew:.2f})\\\")\\n        plt.subplot(1, 2, 2)\\n        sns.histplot(df_transformed_subset[transformed_column_name].dropna(), kde=True, bins=30)\\n        plt.title(f\\\"Log1p Transformed {feature} (Skew: {transformed_skew:.2f})\\\")\\n        plot_filename = output_subdir / f\\\"{feature}_log1p_comparison.png\\\"\\n        plt.savefig(plot_filename)\\n        plt.close()\\n        plot_files_generated.append(str(plot_filename).replace(str(outputs_dir) + \\\"/\\\", \\\"\\\"))\\n        print(f\\\"Transformation plot for {feature} saved to {plot_filename}\\\")\\n    else:\\n        print(f\\\"Feature {feature} not found or not numeric.\\\")\\nprint(f\\\"Generated comparison plots: {plot_files_generated}\\\")')",
+    "final_answer": "{\"explanation\": \"Log1p transformation was applied to 'TMB' and 'NLR' features to reduce skewness. Comparison plots showing the distributions before and after transformation have been generated and saved. This typically helps normalize the data for modeling.\", \"plots\": [\"generated_file/TMB_log1p_comparison.png\", \"generated_file/NLR_log1p_comparison.png\"], \"files\": [], \"next_steps_suggestion\": [\"Assess impact of transformation on model performance.\", \"Are there other skewed features to transform?\", \"Consider other transformation methods like Box-Cox.\"]}"
+  },
+  {
+    "question": "Identify numerical features with absolute correlation > 0.7, remove one from each highly correlated pair, and save the reduced dataset.",
+    "context": "The primary dataset is 'Chowell_train_Response.csv'. It is a 'csv' file. Save the reduced dataset to 'outputs_dir/generated_file/Chowell_train_Reduced_Corr.csv' and the original correlation heatmap to 'outputs_dir/generated_file/correlation_matrix_for_reduction_v2.png'.",
+    "rationale": "Thought: User wants to reduce features based on high correlation (threshold 0.7). I will: 1. Load data. 2. Compute correlation matrix for numerical features. 3. Save the original correlation heatmap. 4. Iterate through the upper triangle of the correlation matrix to find pairs exceeding the threshold. 5. For each such pair, add the second feature to a drop list (simple heuristic to avoid dropping both if one is part of multiple high-correlation pairs). 6. Drop the collected columns and save the new dataset.\nAction: python_code_executor(code='import pandas as pd\\nimport numpy as np\\nimport seaborn as sns\\nimport matplotlib.pyplot as plt\\nfrom pathlib import Path\\n\\noutput_subdir = outputs_dir / \"generated_file\"\\noutput_subdir.mkdir(parents=True, exist_ok=True)\\n\\ndf = pd.read_csv(dataset_path_in_tool_code)\\n\\nnumerical_df = df.select_dtypes(include=[np.number])\\ncorrelation_matrix = numerical_df.corr()\\n\\nplt.figure(figsize=(12, 10))\\nsns.heatmap(correlation_matrix, annot=True, fmt=\\\".2f\\\", cmap=\\\"coolwarm\\\")\\nplt.title(\\\"Original Correlation Matrix\\\")\\nheatmap_path = output_subdir / \\\"correlation_matrix_for_reduction_v2.png\\\"\\nplt.savefig(heatmap_path)\\nplt.close()\\nprint(f\\\"Original heatmap saved to {heatmap_path}\\\")\\n\\ncolumns_to_drop = set()\\ncolumns = correlation_matrix.columns\\nfor i in range(len(columns)):\\n    for j in range(i + 1, len(columns)):\\n        if abs(correlation_matrix.iloc[i, j]) > 0.7:\\n            if columns[j] not in columns_to_drop and columns[i] not in columns_to_drop: \\n                columns_to_drop.add(columns[j]) \\n                print(f\\\"Marking {columns[j]} for removal due to high correlation ({correlation_matrix.iloc[i,j]:.2f}) with {columns[i]}.\\\")\\n\\nprint(f\\\"Columns identified to drop: {columns_to_drop}\\\")\\n\\ndf_reduced = df.drop(columns=list(columns_to_drop))\\nreduced_dataset_path = output_subdir / \\\"Chowell_train_Reduced_Corr.csv\\\"\\ndf_reduced.to_csv(reduced_dataset_path, index=False)\\nprint(f\\\"Reduced dataset saved to {reduced_dataset_path}. New shape: {df_reduced.shape}\\\")')",
+    "final_answer": "{\"explanation\": \"Feature selection was performed by identifying numerical features with an absolute correlation greater than 0.7. One feature from each identified highly correlated pair was removed to reduce multicollinearity. The original correlation heatmap and the dataset with reduced features have been saved.\", \"plots\": [\"generated_file/correlation_matrix_for_reduction_v2.png\"], \"files\": [\"generated_file/Chowell_train_Reduced_Corr.csv\"], \"next_steps_suggestion\": [\"How does this reduced dataset perform in a model?\", \"Try other correlation thresholds.\", \"Consider PCA for dimensionality reduction.\"]}"
+  },
+  {
+    "question": "What are the summary statistics for the 'Age' and 'NLR' columns?",
+    "context": "The primary dataset is 'Chowell_train_Response.csv'. It is a 'csv' file. Save the statistics to 'outputs_dir/generated_file/age_nlr_summary_stats.csv'.",
+    "rationale": "Thought: The user wants summary statistics for specific columns 'Age' and 'NLR'. I will: 1. Load the dataset. 2. Select these two columns. 3. Use the .describe() method on the selection. 4. Save the result to a CSV file.\nAction: python_code_executor(code='import pandas as pd\\nfrom pathlib import Path\\n\\noutput_subdir = outputs_dir / \"generated_file\"\\noutput_subdir.mkdir(parents=True, exist_ok=True)\\n\\ndf = pd.read_csv(dataset_path_in_tool_code)\\n\\nselected_columns = [\\\"Age\\\", \\\"NLR\\\"]\\nif all(col in df.columns for col in selected_columns):\\n    summary_stats_subset = df[selected_columns].describe()\\n    stats_file_path = output_subdir / \\\"age_nlr_summary_stats.csv\\\"\\n    summary_stats_subset.to_csv(stats_file_path)\\n    print(f\\\"Summary statistics for {selected_columns} saved to {stats_file_path}\\\")\\n    print(summary_stats_subset)\\nelse:\\n    print(f\\\"Error: One or both columns {selected_columns} not found in the dataset.\\\")')",
+    "final_answer": "{\"explanation\": \"Summary statistics (count, mean, std, min, quartiles, max) for the 'Age' and 'NLR' columns have been calculated and saved to a CSV file. This provides a detailed statistical overview of these specific features.\", \"plots\": [], \"files\": [\"generated_file/age_nlr_summary_stats.csv\"], \"next_steps_suggestion\": [\"Plot distributions for Age and NLR.\", \"Are there outliers in Age or NLR?\", \"Correlate Age and NLR with the Response variable.\"]}"
+  },
+  {
+    "question": "Train a RandomForest classifier to predict the 'Response' variable. Evaluate the model and show me the confusion matrix, ROC curve, and key performance metrics.",
+    "context": "The primary dataset is 'Chowell_train_Response.csv'. It is a 'csv' file. All generated files and plots must be saved to 'outputs_dir/generated_file/'.",
+    "rationale": "Thought: The user wants to train a RandomForest classifier to predict 'Response'. I need to perform the following steps:\n1. Load the dataset using the provided path.\n2. Select only numerical features for the model and define the target variable 'Response'.\n3. Split the data into training and testing sets.\n4. Train a RandomForestClassifier model.\n5. Make predictions on the test set.\n6. Calculate key performance metrics using scikit-learn's classification_report.\n7. Generate and save a heatmap of the confusion matrix.\n8. Generate and save an ROC curve plot.\n9. Save the performance metrics to a text file.\n10. Construct the final JSON answer with an explanation, paths to the plots, and the path to the metrics file.\nAction: python_code_executor(code='import pandas as pd\\nimport numpy as np\\nimport matplotlib.pyplot as plt\\nimport seaborn as sns\\nfrom pathlib import Path\\nfrom sklearn.model_selection import train_test_split\\nfrom sklearn.ensemble import RandomForestClassifier\\nfrom sklearn.metrics import classification_report, confusion_matrix, roc_curve, auc\\n\\n# Define the output subdirectory where files will be saved\\noutput_subdir = outputs_dir / \"generated_file\"\\noutput_subdir.mkdir(parents=True, exist_ok=True)\\n\\n# Load the dataset\\ndf = pd.read_csv(dataset_path_in_tool_code)\\n\\n# Prepare data: Use only numeric features for this example\\nif \\'Response\\' not in df.columns:\\n    print(\"Error: Target column \\'Response\\' not found.\")\\nelse:\\n    X = df.select_dtypes(include=np.number).drop(columns=[\\'Response\\'], errors=\\'ignore\\')\\n    y = df[\\'Response\\']\\n\\n    # Split data\\n    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42, stratify=y)\\n\\n    # Train model\\n    model = RandomForestClassifier(random_state=42, n_estimators=100, class_weight=\\'balanced\\')\\n    model.fit(X_train, y_train)\\n\\n    # Predictions\\n    y_pred = model.predict(X_test)\\n    y_pred_proba = model.predict_proba(X_test)[:, 1]\\n\\n    # Save metrics report\\n    report = classification_report(y_test, y_pred)\\n    metrics_file_path = output_subdir / \\\"ml_classification_report.txt\\\"\\n    with open(metrics_file_path, \\\"w\\\") as f:\\n        f.write(\\\"Classification Report for RandomForest:\\\\n\\\")\\n        f.write(report)\\n    print(f\\\"Metrics report saved to {metrics_file_path.relative_to(outputs_dir)}\\\")\\n\\n    # Plot and save confusion matrix\\n    cm = confusion_matrix(y_test, y_pred)\\n    plt.figure(figsize=(8, 6))\\n    sns.heatmap(cm, annot=True, fmt=\\\"d\\\", cmap=\\\"Blues\\\", xticklabels=model.classes_, yticklabels=model.classes_)\\n    plt.title(\\\"Confusion Matrix\\\")\\n    plt.xlabel(\\\"Predicted Label\\\")\\n    plt.ylabel(\\\"True Label\\\")\\n    cm_plot_path = output_subdir / \\\"ml_confusion_matrix.png\\\"\\n    plt.savefig(cm_plot_path)\\n    plt.close()\\n    print(f\\\"Confusion matrix plot saved to {cm_plot_path.relative_to(outputs_dir)}\\\")\\n\\n    # Plot and save ROC curve\\n    fpr, tpr, _ = roc_curve(y_test, y_pred_proba)\\n    roc_auc = auc(fpr, tpr)\\n    plt.figure(figsize=(8, 6))\\n    plt.plot(fpr, tpr, color=\\'darkorange\\', lw=2, label=f\\\"ROC curve (area = {roc_auc:.2f})\\\")\\n    plt.plot([0, 1], [0, 1], color=\\'navy\\', lw=2, linestyle=\\'--\\')\\n    plt.xlim([0.0, 1.0])\\n    plt.ylim([0.0, 1.05])\\n    plt.xlabel(\\\"False Positive Rate\\\")\\n    plt.ylabel(\\\"True Positive Rate\\\")\\n    plt.title(\\\"Receiver Operating Characteristic (ROC) Curve\\\")\\n    plt.legend(loc=\\\"lower right\\\")\\n    roc_plot_path = output_subdir / \\\"ml_roc_curve.png\\\"\\n    plt.savefig(roc_plot_path)\\n    plt.close()\\n    print(f\\\"ROC curve plot saved to {roc_plot_path.relative_to(outputs_dir)}\\\")')",
+    "final_answer": "{\"explanation\": \"A RandomForest classifier was successfully trained to predict the 'Response' variable. The model's performance was evaluated on a held-out test set. The results, including a detailed classification report, a confusion matrix visualization, and an ROC curve, have been generated and saved. The model shows good predictive capability, with an AUC of 0.85.\", \"plots\": [\"generated_file/ml_confusion_matrix.png\", \"generated_file/ml_roc_curve.png\"], \"files\": [\"generated_file/ml_classification_report.txt\"], \"next_steps_suggestion\": [\"Analyze feature importances from the model.\", \"Perform hyperparameter tuning to potentially improve performance.\", \"Try other classification models like Gradient Boosting or Logistic Regression.\"]}"
+  },
+  {
+    "question": "Use AutoGluon to train a model predicting the 'Response' variable. Provide the leaderboard and save the best model's predictions.",
+    "context": "The primary dataset is 'Chowell_train_Response.csv'. It is a 'csv' file. All generated files must be saved to 'outputs_dir/generated_file/'.",
+    "rationale": "Thought: The user wants to use AutoGluon for automated machine learning. I will: 1. Load the data into a pandas DataFrame. 2. Split the data into training and testing sets. 3. Initialize AutoGluon's TabularPredictor, specifying the output path inside the agent's file directory. 4. Call the .fit() method on the training data. 5. Get the leaderboard of model performance and save it to a CSV. 6. Use the best model to make predictions on the test data and save the predictions. \nAction: python_code_executor(code='import pandas as pd\\nfrom autogluon.tabular import TabularPredictor\\nfrom sklearn.model_selection import train_test_split\\nfrom pathlib import Path\\n\\n# Load data\\ndf = pd.read_csv(dataset_path_in_tool_code)\\n\\n# Split data (80/20)\\ntrain_data, test_data = train_test_split(df, test_size=0.2, random_state=42, stratify=df[\\'Response\\'])\\n\\n# Define predictor settings\\nlabel = \\'Response\\'\\n# Save the autogluon models inside a subdir of the agent\\'s output directory\\nsave_path = outputs_dir / \"generated_file\" / \"autogluon_models\" \\npredictor = TabularPredictor(label=label, path=save_path).fit(train_data)\\n\\n# Get and save leaderboard\\nleaderboard = predictor.leaderboard(test_data, silent=True)\\nleaderboard_path = outputs_dir / \"generated_file\" / \\\"autogluon_leaderboard.csv\\\"\\nleaderboard.to_csv(leaderboard_path, index=False)\\nprint(f\\\"AutoGluon leaderboard saved to {leaderboard_path.relative_to(outputs_dir)}\\\")\\n\\n# Make and save predictions with the best model\\npredictions = predictor.predict(test_data.drop(columns=[label]))\\npredictions_path = outputs_dir / \"generated_file\" / \\\"autogluon_predictions.csv\\\"\\npredictions.to_csv(predictions_path, index=False)\\nprint(f\\\"Predictions from best model saved to {predictions_path.relative_to(outputs_dir)}\\\")')",
+    "final_answer": "{\"explanation\": \"AutoGluon has been used to automatically train and evaluate multiple models for predicting the 'Response' variable. The complete model leaderboard, showing the performance of each model, has been saved. The predictions from the best performing model have also been saved to a separate file.\", \"plots\": [], \"files\": [\"generated_file/autogluon_leaderboard.csv\", \"generated_file/autogluon_predictions.csv\"], \"next_steps_suggestion\": [\"Analyze the leaderboard to see which model types performed best.\", \"View the feature importance from the best model.\", \"Can you retrain with more time for potentially better models?\"]}"
+  }
+]
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