cerebras_csv_agent.py

import pandas as pd
import json
from typing import List, Literal, Optional
from pydantic import BaseModel
from dotenv import load_dotenv
from pydantic_ai import Agent
from csv_service import clean_data
from python_code_executor_service import PythonExecutor
from cerebras_instance_provider import InstanceProvider
import logging

load_dotenv()

instance_provider = InstanceProvider()

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

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
    result_var: str

class CsvChatResult(BaseModel):
    """Structured response for CSV-related AI interactions"""
    
    # Casual chat response
    casual_response: str
    
    # Data analysis components
    # analysis_operations: List[AnalysisOperation]
    analysis_operations: AnalysisOperation
    
    # Visualization components
    # charts: Optional[List[ChartSpecification]] = None
    charts: Optional[ChartSpecification] = None
    
    
def get_csv_info(df: pd.DataFrame) -> dict:
    """Get metadata/info about the CSV"""
    info = {
        'num_rows': len(df),
        'num_cols': len(df.columns),
        'example_rows': df.head(2).to_dict('records'),
        'dtypes': {col: str(df[col].dtype) for col in df.columns},
        'columns': list(df.columns),
        'numeric_columns': [col for col in df.columns if pd.api.types.is_numeric_dtype(df[col])],
        'categorical_columns': [col for col in df.columns if pd.api.types.is_string_dtype(df[col])]
    }
    return info


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:
- Shape: {csv_info['num_rows']} rows × {csv_info['num_cols']} cols
- Columns: {csv_info['columns']}
- Sample: {csv_info['example_rows']}
- Dtypes: {csv_info['dtypes']}

STRICT REQUIREMENTS:
1. NEVER calculate or predict values yourself - ALWAYS return executable code that would produce the result
2. Use existing 'df' - never recreate it
3. For any data structures (Lists, Records, Tables, Dictionaries, etc.), always return them as JSON with correct indentation
4. For charts:
   - Use matplotlib/seaborn only
   - Professional quality: proper sizing, labels, titles
   - Figure size: (14, 8) for complex, (12, 6) for simple
   - Clear titles (fontsize=16), labels (fontsize=14)
   - Rotate x-labels if needed (45°, fontsize=12)
   - Add annotations/gridlines where helpful
   - Use colorblind-friendly palettes
   - Always include plt.tight_layout()

Example professional chart:
plt.figure(figsize=(14, 8))
sns.barplot(x='category', y='value', data=df, palette='muted')
plt.title('Value by Category', fontsize=16)
plt.xlabel('Category', fontsize=14)
plt.ylabel('Value', fontsize=14)
plt.xticks(rotation=45)
plt.grid(alpha=0.3)
plt.tight_layout()
plt.show()

Example professional response for a dataframe:
num_rows = len(df)


Return complete, executable code.
"""
    return prompt


def create_csv_agent(df: pd.DataFrame, max_retries: int = 1) -> Agent:
    """Create and return a CSV analysis agent with API key rotation"""
    csv_system_prompt = get_csv_system_prompt(df)
    
    for attempt in range(max_retries):
        try:
            model = instance_provider.get_instance()
            if model is None:
                raise RuntimeError("No available API instances")
            
            csv_agent = Agent(
                model=model,
                output_type=CsvChatResult,
                system_prompt=csv_system_prompt,
            )
        
            return csv_agent
            
        except Exception as e:
            api_key = instance_provider.get_api_key_for_model(model)
            if api_key:
                logger.info(f"Error with API key (attempt {attempt + 1}): {str(e)}")
                instance_provider.report_error(api_key)
            continue
    
    raise RuntimeError(f"Failed to create agent after {max_retries} attempts")


async def query_csv_agent(csv_url: str, question: str, chat_id: str) -> str:
    """Query the CSV agent with a DataFrame and question and return formatted output"""
    
    # Get the DataFrame from the CSV URL
    df = clean_data(csv_url)
    
    # Create agent and get response
    agent = create_csv_agent(df)
    result = await agent.run(question)
    
    # Process the response through PythonExecutor
    executor = PythonExecutor(df)
    
    # Convert the raw output to CsvChatResult if needed
    if not isinstance(result.output, CsvChatResult):
        # Handle case where output needs conversion
        try:
            response_data = result.output if isinstance(result.output, dict) else json.loads(result.output)
            chat_result = CsvChatResult(**response_data)
        except Exception as e:
            raise ValueError(f"Could not parse agent response: {str(e)}")
    else:
        chat_result = result.output
        
        logger.info("Chat Result Original Object:", chat_result)
    
    # Process and format the response
    formatted_output = await executor.process_response(chat_result, chat_id)
    
    return formatted_output