python_code_executor_service.py

import os
from dotenv import load_dotenv
import uuid
import matplotlib.pyplot as plt
from pathlib import Path
from typing import Dict, Any, List, Optional
import pandas as pd
import numpy as np
import json
import io
import contextlib
import traceback
import time
from datetime import datetime, timedelta
import seaborn as sns
import scipy.stats as stats
from pydantic import BaseModel

from supabase_service import upload_file_to_supabase

# Load environment variables from .env file
load_dotenv()

class CodeResponse(BaseModel):
    """Container for code-related responses"""
    language: str = "python"
    code: str


class ChartSpecification(BaseModel):
    """Details about requested charts"""
    image_description: str
    code: Optional[str] = None


class AnalysisOperation(BaseModel):
    """Container for a single analysis operation with its code and result"""
    code: CodeResponse
    description: str


class CsvChatResult(BaseModel):
    """Structured response for CSV-related AI interactions"""
    response_type: str  # Literal["casual", "data_analysis", "visualization", "mixed"]
    casual_response: str
    analysis_operations: List[AnalysisOperation]
    charts: Optional[List[ChartSpecification]] = None


class PythonExecutor:
    """Handles execution of Python code with comprehensive data analysis libraries"""
    
    def __init__(self, df: pd.DataFrame, charts_folder: str = "generated_charts"):
        """
        Initialize the PythonExecutor with a DataFrame
        
        Args:
            df (pd.DataFrame): The DataFrame to operate on
            charts_folder (str): Folder to save charts in
        """
        self.df = df
        self.charts_folder = Path(charts_folder)
        self.charts_folder.mkdir(exist_ok=True)
        
    def execute_code(self, code: str) -> Dict[str, Any]:
        """
        Execute Python code with full data analysis context and return results
        
        Args:
            code (str): Python code to execute
            
        Returns:
            dict: Dictionary containing execution results and any generated plots
        """
        output = ""
        error = None
        plots = []
        
        # Capture stdout
        stdout = io.StringIO()
        
        # Monkey patch plt.show() to save figures
        original_show = plt.show
        
        def custom_show():
            """Custom show function that saves plots instead of displaying them"""
            for i, fig in enumerate(plt.get_fignums()):
                figure = plt.figure(fig)
                # Save plot to bytes buffer
                buf = io.BytesIO()
                figure.savefig(buf, format='png', bbox_inches='tight')
                buf.seek(0)
                plots.append(buf.read())
            plt.close('all')
        
        try:
            # Create comprehensive execution context with data analysis libraries
            exec_globals = {
                # Core data analysis
                'pd': pd,
                'np': np,
                'df': self.df,
                
                # Visualization
                'plt': plt,
                'sns': sns,
                
                # Statistics
                'stats': stats,
                
                # Date/time
                'datetime': datetime,
                'timedelta': timedelta,
                'time': time,
                
                # Utilities
                'json': json,
                '__builtins__': __builtins__,
            }
            
            # Replace plt.show with custom implementation
            plt.show = custom_show
            
            # Execute code and capture output
            with contextlib.redirect_stdout(stdout):
                exec(code, exec_globals)
            
            output = stdout.getvalue()
            
        except Exception as e:
            error = {
                "message": str(e),
                "traceback": traceback.format_exc()
            }
        finally:
            # Restore original plt.show
            plt.show = original_show
            
        return {
            'output': output,
            'error': error,
            'plots': plots
        }
    
    async def save_plot_to_supabase(self, plot_data: bytes, description: str, chat_id: str) -> str:
        """
        Save plot to Supabase storage and return the public URL
        
        Args:
            plot_data (bytes): Image data in bytes
            description (str): Description of the plot
            chat_id (str): ID of the chat session
            
        Returns:
            str: Public URL of the uploaded chart
        """
        # Generate unique filename
        filename = f"chart_{uuid.uuid4().hex}.png"
        filepath = self.charts_folder / filename
        
        # Save the plot locally first
        with open(filepath, 'wb') as f:
            f.write(plot_data)
            
        try:
            # Upload to Supabase
            public_url = await upload_file_to_supabase(
                file_path=str(filepath),
                file_name=filename,
                chat_id=chat_id
            )
            
            # Remove the local file after upload
            os.remove(filepath)
            
            return public_url
        except Exception as e:
            # Clean up local file if upload fails
            if os.path.exists(filepath):
                os.remove(filepath)
            raise Exception(f"Failed to upload plot to Supabase: {e}")
    
    def _looks_like_structured_data(self, output: str) -> bool:
        """Helper to detect JSON-like or array-like output"""
        output = output.strip()
        return (
            output.startswith('{') and output.endswith('}') or  # JSON object
            output.startswith('[') and output.endswith(']') or  # Array
            '\n' in output and '=' in output  # Python console output
        )
    
    async def process_response(self, response: CsvChatResult, chat_id: str) -> str:
        """
        Process the CsvChatResult response and generate formatted output
        with markdown code blocks for structured data.
        
        Args:
            response (CsvChatResult): Response from CSV analysis
            chat_id (str): ID of the chat session
            
        Returns:
            str: Formatted output with results and image URLs
        """
        output_parts = []
        
        # Add casual response
        output_parts.append(response.casual_response)
        
        # Process analysis operations
        for operation in response.analysis_operations:
            # Execute the code
            result = self.execute_code(operation.code.code)
            
            # Add operation description
            output_parts.append(f"\n{operation.description}:")
            
            # Add output or error with markdown wrapping
            if result['error']:
                output_parts.append("```python\n" + f"Error: {result['error']['message']}" + "\n```")
            else:
                output = result['output'].strip()
                if self._looks_like_structured_data(output):
                    output_parts.append("```python\n" + output + "\n```")
                else:
                    output_parts.append(output)
        
        # Process charts
        if response.charts:
            output_parts.append("\nVisualizations:")
            for chart in response.charts:
                if chart.code:
                    result = self.execute_code(chart.code)
                    if result['plots']:
                        for plot_data in result['plots']:
                            try:
                                public_url = await self.save_plot_to_supabase(
                                    plot_data=plot_data,
                                    description=chart.image_description,
                                    chat_id=chat_id
                                )
                                output_parts.append(f"\n{chart.image_description}")
                                output_parts.append(f"![{chart.image_description}]({public_url})")
                            except Exception as e:
                                output_parts.append(f"\nError uploading chart: {str(e)}")
                    elif result['error']:
                        output_parts.append("```python\n" + f"Error generating {chart.image_description}: {result['error']['message']}" + "\n```")
        
        return "\n".join(output_parts)







# Table formatter

# import os
# from dotenv import load_dotenv
# import uuid
# import matplotlib.pyplot as plt
# from pathlib import Path
# from typing import Dict, Any, List, Optional
# import pandas as pd
# import numpy as np
# import json
# import io
# import contextlib
# import traceback
# import time
# from datetime import datetime, timedelta
# import seaborn as sns
# import scipy.stats as stats
# from pydantic import BaseModel

# from supabase_service import upload_file_to_supabase

# # Load environment variables from .env file
# load_dotenv()

# class CodeResponse(BaseModel):
#     """Container for code-related responses"""
#     language: str = "python"
#     code: str


# class ChartSpecification(BaseModel):
#     """Details about requested charts"""
#     image_description: str
#     code: Optional[str] = None


# class AnalysisOperation(BaseModel):
#     """Container for a single analysis operation with its code and result"""
#     code: CodeResponse
#     description: str


# class CsvChatResult(BaseModel):
#     """Structured response for CSV-related AI interactions"""
#     response_type: str  # Literal["casual", "data_analysis", "visualization", "mixed"]
#     casual_response: str
#     analysis_operations: List[AnalysisOperation]
#     charts: Optional[List[ChartSpecification]] = None


# class PythonExecutor:
#     """Handles execution of Python code with comprehensive data analysis libraries"""
    
#     def __init__(self, df: pd.DataFrame, charts_folder: str = "generated_charts"):
#         """
#         Initialize the PythonExecutor with a DataFrame
        
#         Args:
#             df (pd.DataFrame): The DataFrame to operate on
#             charts_folder (str): Folder to save charts in
#         """
#         self.df = df
#         self.charts_folder = Path(charts_folder)
#         self.charts_folder.mkdir(exist_ok=True)
        
#     def execute_code(self, code: str) -> Dict[str, Any]:
#         """
#         Execute Python code with full data analysis context and return results
        
#         Args:
#             code (str): Python code to execute
            
#         Returns:
#             dict: Dictionary containing execution results and any generated plots
#         """
#         output = ""
#         error = None
#         plots = []
        
#         # Capture stdout
#         stdout = io.StringIO()
        
#         # Monkey patch plt.show() to save figures
#         original_show = plt.show
        
#         def custom_show():
#             """Custom show function that saves plots instead of displaying them"""
#             for i, fig in enumerate(plt.get_fignums()):
#                 figure = plt.figure(fig)
#                 # Save plot to bytes buffer
#                 buf = io.BytesIO()
#                 figure.savefig(buf, format='png', bbox_inches='tight')
#                 buf.seek(0)
#                 plots.append(buf.read())
#             plt.close('all')
        
#         try:
#             # Create comprehensive execution context with data analysis libraries
#             exec_globals = {
#                 # Core data analysis
#                 'pd': pd,
#                 'np': np,
#                 'df': self.df,
                
#                 # Visualization
#                 'plt': plt,
#                 'sns': sns,
                
#                 # Statistics
#                 'stats': stats,
                
#                 # Date/time
#                 'datetime': datetime,
#                 'timedelta': timedelta,
#                 'time': time,
                
#                 # Utilities
#                 'json': json,
#                 '__builtins__': __builtins__,
#             }
            
#             # Replace plt.show with custom implementation
#             plt.show = custom_show
            
#             # Execute code and capture output
#             with contextlib.redirect_stdout(stdout):
#                 exec(code, exec_globals)
            
#             output = stdout.getvalue()
            
#         except Exception as e:
#             error = {
#                 "message": str(e),
#                 "traceback": traceback.format_exc()
#             }
#         finally:
#             # Restore original plt.show
#             plt.show = original_show
            
#         return {
#             'output': output,
#             'error': error,
#             'plots': plots
#         }
    
#     def _convert_dataframe_to_text(self, df: pd.DataFrame) -> str:
#         """
#         Convert pandas DataFrame to a text format that can be easily rendered
#         in the frontend using the ScrollableTableRenderer component.
        
#         Args:
#             df (pd.DataFrame): DataFrame to convert
            
#         Returns:
#             str: Text representation of the DataFrame
#         """
#         # Convert DataFrame to string with proper formatting
#         df_str = df.to_string(index=True)
        
#         # Split into lines and clean up
#         lines = df_str.split('\n')
        
#         # Remove any trailing whitespace from each line
#         cleaned_lines = [line.rstrip() for line in lines]
        
#         # Join back with newlines
#         return '\n'.join(cleaned_lines)
    
#     async def save_plot_to_supabase(self, plot_data: bytes, description: str, chat_id: str) -> str:
#         """
#         Save plot to Supabase storage and return the public URL
        
#         Args:
#             plot_data (bytes): Image data in bytes
#             description (str): Description of the plot
#             chat_id (str): ID of the chat session
            
#         Returns:
#             str: Public URL of the uploaded chart
#         """
#         # Generate unique filename
#         filename = f"chart_{uuid.uuid4().hex}.png"
#         filepath = self.charts_folder / filename
        
#         # Save the plot locally first
#         with open(filepath, 'wb') as f:
#             f.write(plot_data)
            
#         try:
#             # Upload to Supabase
#             public_url = await upload_file_to_supabase(
#                 file_path=str(filepath),
#                 file_name=filename,
#                 chat_id=chat_id
#             )
            
#             # Remove the local file after upload
#             os.remove(filepath)
            
#             return public_url
#         except Exception as e:
#             # Clean up local file if upload fails
#             if os.path.exists(filepath):
#                 os.remove(filepath)
#             raise Exception(f"Failed to upload plot to Supabase: {e}")
    
#     def _looks_like_structured_data(self, output: str) -> bool:
#         """Helper to detect JSON-like or array-like output"""
#         output = output.strip()
#         return (
#             output.startswith('{') and output.endswith('}') or  # JSON object
#             output.startswith('[') and output.endswith(']') or  # Array
#             '\n' in output and '=' in output  # Python console output
#         )
    
#     def _is_dataframe_output(self, output: str) -> bool:
#         """Helper to detect if output looks like a pandas DataFrame"""
#         lines = output.strip().split('\n')
#         if len(lines) < 2:
#             return False
        
#         # Check for typical DataFrame header pattern
#         first_line = lines[0].strip()
#         second_line = lines[1].strip()
        
#         # Look for column headers and separator line
#         if not first_line or not second_line:
#             return False
            
#         # Check if the first line contains column names
#         # and the second line has some alignment characters
#         return True
    
#     async def process_response(self, response: CsvChatResult, chat_id: str) -> str:
#         """
#         Process the CsvChatResult response and generate formatted output
#         with markdown code blocks for structured data.
        
#         Args:
#             response (CsvChatResult): Response from CSV analysis
#             chat_id (str): ID of the chat session
            
#         Returns:
#             str: Formatted output with results and image URLs
#         """
#         output_parts = []
        
#         # Add casual response
#         output_parts.append(response.casual_response)
        
#         # Process analysis operations
#         for operation in response.analysis_operations:
#             # Execute the code
#             result = self.execute_code(operation.code.code)
            
#             # Add operation description
#             output_parts.append(f"\n{operation.description}:")
            
#             # Add output or error with markdown wrapping
#             if result['error']:
#                 output_parts.append("```python\n" + f"Error: {result['error']['message']}" + "\n```")
#             else:
#                 output = result['output'].strip()
                
#                 # Check if the output is a DataFrame-like structure
#                 if self._is_dataframe_output(output):
#                     # Convert to a clean text format for frontend rendering
#                     try:
#                         # Get the last evaluated expression which might be the DataFrame
#                         # This is a simple approach - in practice you might need a more robust way
#                         # to capture the actual DataFrame from the execution context
#                         df_output = self._convert_dataframe_to_text(eval(operation.code.code.split('\n')[-1], globals(), locals()))
#                         output_parts.append("```text\n" + df_output + "\n```")
#                     except:
#                         # Fall back to regular output if we can't convert it
#                         output_parts.append("```text\n" + output + "\n```")
#                 elif self._looks_like_structured_data(output):
#                     output_parts.append("```python\n" + output + "\n```")
#                 else:
#                     output_parts.append(output)
        
#         # Process charts
#         if response.charts:
#             output_parts.append("\nVisualizations:")
#             for chart in response.charts:
#                 if chart.code:
#                     result = self.execute_code(chart.code)
#                     if result['plots']:
#                         for plot_data in result['plots']:
#                             try:
#                                 public_url = await self.save_plot_to_supabase(
#                                     plot_data=plot_data,
#                                     description=chart.image_description,
#                                     chat_id=chat_id
#                                 )
#                                 output_parts.append(f"\n{chart.image_description}")
#                                 output_parts.append(f"![{chart.image_description}]({public_url})")
#                             except Exception as e:
#                                 output_parts.append(f"\nError uploading chart: {str(e)}")
#                     elif result['error']:
#                         output_parts.append("```python\n" + f"Error generating {chart.image_description}: {result['error']['message']}" + "\n```")
        
#         return "\n".join(output_parts)