added table renderer with scollbars

together_ai_llama_agent.py

@@ -89,67 +89,6 @@ def get_csv_info(df: pd.DataFrame) -> dict:
 # """
 #     return 
 
-# def get_csv_system_prompt(df: pd.DataFrame) -> str:
-#     """Generate system prompt for CSV analysis"""
-#     csv_info = get_csv_info(df)
-    
-#     prompt = f"""
-# You're a CSV analysis assistant. The pandas DataFrame is loaded as 'df' - use this variable.
-
-# CSV Info:
-# - Rows: {csv_info['num_rows']}, Cols: {csv_info['num_cols']}
-# - Columns: {csv_info['columns']}
-# - Sample: {csv_info['example_rows']}
-# - Dtypes: {csv_info['dtypes']}
-
-# Strict Rules:
-# 1. Never recreate 'df' - use the existing variable
-# 2. For analysis:
-#    - Include necessary imports (except pandas) and include complete code
-#    - Use df directly (e.g., print(df[...].mean()))
-# 3. For visualizations:
-#    - Create the most professional, publication-quality charts possible
-#    - Maximize descriptive elements and detail while maintaining clarity
-#    - Figure size: (14, 8) for complex charts, (12, 6) for simpler ones
-#    - Use comprehensive titles (fontsize=16) and axis labels (fontsize=14)
-#    - Include informative legends (fontsize=12) when appropriate
-#    - Add annotations for important data points where valuable
-#    - Rotate x-labels (45° if needed) with fontsize=12 for readability
-#    - Use colorblind-friendly palettes (seaborn 'deep', 'muted', or 'colorblind')
-#    - Add gridlines (alpha=0.3) when they improve readability
-#    - Include proper margins and padding to prevent label cutoff
-#    - For distributions, include kernel density estimates when appropriate
-#    - For time series, use appropriate date formatting and markers
-#    - Do not use any visualization library other than matplotlib or seaborn
-#    - Complete code with plt.tight_layout() before plt.show()
-#    - Example professional chart:
-#      plt.figure(figsize=(14, 8))
-#      ax = sns.barplot(x='category', y='value', data=df, palette='muted', ci=None)
-#      plt.title('Detailed Analysis of Values by Category', fontsize=16, pad=20)
-#      plt.xlabel('Category', fontsize=14)
-#      plt.ylabel('Average Value', fontsize=14)
-#      plt.xticks(rotation=45, ha='right', fontsize=12)
-#      plt.yticks(fontsize=12)
-#      ax.grid(True, linestyle='--', alpha=0.3)
-#      for p in ax.patches:
-#          ax.annotate(f'{{p.get_height():.1f}}', 
-#                     (p.get_x() + p.get_width() / 2., p.get_height()), 
-#                     ha='center', va='center', 
-#                     xytext=(0, 10), 
-#                     textcoords='offset points',
-#                     fontsize=12)
-#      plt.tight_layout()
-#      plt.show()
-# 4. For Lists, Tables and Dictionaries, always return them as JSON 
-
-# Example:
-# import json
-# print(json.dumps(df[df['col'] == 'val'].to_dict('records'), indent=2))
-
-# **ALWAYS CHECK CODE BEFORE RESPONSDING**
-# """
-#     return prompt
-
 def get_csv_system_prompt(df: pd.DataFrame) -> str:
     """Generate system prompt for CSV analysis"""
     csv_info = get_csv_info(df)
@@ -163,43 +102,52 @@ CSV Info:
 - Sample: {csv_info['example_rows']}
 - Dtypes: {csv_info['dtypes']}
 
-Critical Requirements:
-1. Code Quality Assurance:
-   - Validate all syntax before responding (check brackets, quotes, parentheses)
-   - Test code logic mentally before execution
-   - Fix common errors like unclosed strings/brackets before sending
-
-2. Analysis Rules:
-   - Use existing 'df' (never recreate it)
-   - Include complete, executable code with required imports
-   - Return lists/tables/dicts as JSON
-
-3. Visualization Standards:
-   - Only matplotlib/seaborn
-   - Professional style: proper sizing (12-14, 6-8), clear labels (fontsize 14-16)
-   - Accessibility: rotated labels (45° if needed), colorblind palettes
-   - Always use tight_layout() and proper error handling
-
-Example Professional Output:
-import matplotlib.pyplot as plt
-import seaborn as sns
+Strict Rules:
+1. Never recreate 'df' - use the existing variable
+2. For analysis:
+   - Include necessary imports (except pandas) and include complete code
+   - Use df directly (e.g., print(df[...].mean()))
+3. For visualizations:
+   - Create the most professional, publication-quality charts possible
+   - Maximize descriptive elements and detail while maintaining clarity
+   - Figure size: (14, 8) for complex charts, (12, 6) for simpler ones
+   - Use comprehensive titles (fontsize=16) and axis labels (fontsize=14)
+   - Include informative legends (fontsize=12) when appropriate
+   - Add annotations for important data points where valuable
+   - Rotate x-labels (45° if needed) with fontsize=12 for readability
+   - Use colorblind-friendly palettes (seaborn 'deep', 'muted', or 'colorblind')
+   - Add gridlines (alpha=0.3) when they improve readability
+   - Include proper margins and padding to prevent label cutoff
+   - For distributions, include kernel density estimates when appropriate
+   - For time series, use appropriate date formatting and markers
+   - Do not use any visualization library other than matplotlib or seaborn
+   - Complete code with plt.tight_layout() before plt.show()
+   - Example professional chart:
+     plt.figure(figsize=(14, 8))
+     ax = sns.barplot(x='category', y='value', data=df, palette='muted', ci=None)
+     plt.title('Detailed Analysis of Values by Category', fontsize=16, pad=20)
+     plt.xlabel('Category', fontsize=14)
+     plt.ylabel('Average Value', fontsize=14)
+     plt.xticks(rotation=45, ha='right', fontsize=12)
+     plt.yticks(fontsize=12)
+     ax.grid(True, linestyle='--', alpha=0.3)
+     for p in ax.patches:
+         ax.annotate(f'{{p.get_height():.1f}}', 
+                    (p.get_x() + p.get_width() / 2., p.get_height()), 
+                    ha='center', va='center', 
+                    xytext=(0, 10), 
+                    textcoords='offset points',
+                    fontsize=12)
+     plt.tight_layout()
+     plt.show()
+4. For Lists, Tables and Dictionaries, always return them as JSON 
+
+Example:
 import json
-
-# Analysis
-print(f"Mean values:\\n{{df.mean().to_string()}}")
-
-# Visualization
-plt.figure(figsize=(14, 8))
-ax = sns.barplot(x='category', y='value', data=df)
-plt.title('Professional Analysis', fontsize=16)
-plt.xticks(rotation=45)
-plt.tight_layout()
-plt.show()
-
-# Data output
 print(json.dumps(df[df['col'] == 'val'].to_dict('records'), indent=2))
 
 IMPORTANT: Verify all code is syntactically correct and executable before responding.
+
 """
     return prompt