Update llm_inference.py

llm_inference.py

@@ -1,400 +1,3 @@
-# """
-# LLM Inference Module
-
-# This module handles all interactions with the Groq API via LangChain,
-# allowing the application to generate EDA insights and feature engineering
-# recommendations from dataset analysis.
-# """
-
-# import os
-# from dotenv import load_dotenv
-# import logging
-# import time
-# from typing import Dict, Any, List, Optional
-# from langchain_community.callbacks.manager import get_openai_callback
-
-# # LangChain imports
-# from langchain_groq import ChatGroq
-# from langchain_core.messages import HumanMessage
-# from langchain_core.prompts import ChatPromptTemplate, HumanMessagePromptTemplate
-# # from langchain_community.callbacks.manager import get_openai_callbatck
-# from langchain_core.runnables import RunnableSequence
-
-# # Configure logging
-# logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s")
-# logger = logging.getLogger(__name__)
-
-# # Load environment variables
-# load_dotenv()
-# GROQ_API_KEY = os.getenv("GROQ_API_KEY")
-
-# if not GROQ_API_KEY:
-#     raise ValueError("GROQ_API_KEY not found in environment variables. Please add it to your .env file.")
-
-# # Create LLM model
-# try:
-#     llm = ChatGroq(model_name="llama3-8b-8192", groq_api_key=GROQ_API_KEY)
-#     logger.info("Successfully initialized Groq client")
-# except Exception as e:
-#     logger.error(f"Failed to initialize Groq client: {str(e)}")
-#     raise
-
-# class LLMInference:
-#     """Class for interacting with LLM via Groq API using LangChain"""
-    
-#     def __init__(self, model_id: str = "llama3-8b-8192"):
-#         """Initialize the LLM inference class with Groq model"""
-#         self.model_id = model_id
-#         self.llm = llm
-        
-#         # Initialize prompt templates and chains
-#         self._init_prompt_templates()
-#         self._init_chains()
-        
-#         logger.info(f"LLMInference initialized with model: {model_id}")
-    
-#     def _init_prompt_templates(self):
-#         """Initialize all prompt templates"""
-        
-#         # EDA insights prompt template
-#         self.eda_prompt_template = ChatPromptTemplate.from_messages([
-#             HumanMessagePromptTemplate.from_template(
-#                 """You are a data scientist tasked with performing Exploratory Data Analysis (EDA) on a dataset. 
-# Based on the following dataset information, provide comprehensive EDA insights:
-
-# Dataset Information:
-# - Shape: {shape}
-# - Columns and their types:
-# {columns_info}
-
-# - Missing values:
-# {missing_info}
-
-# - Basic statistics:
-# {basic_stats}
-
-# - Top correlations:
-# {correlations}
-
-# - Sample data:
-# {sample_data}
-
-# Please provide a detailed EDA analysis that includes:
-
-# 1. Summary of the dataset (what it appears to be about, key features, etc.)
-# 2. Distribution analysis of key variables
-# 3. Relationship analysis between variables
-# 4. Identification of patterns, outliers, or anomalies
-# 5. Recommended visualizations that would be insightful
-# 6. Initial hypotheses based on the data
-
-# Your analysis should be structured, thorough, and provide actionable insights for further investigation.
-# """
-#             )
-#         ])
-        
-#         # Feature engineering prompt template
-#         self.feature_engineering_prompt_template = ChatPromptTemplate.from_messages([
-#             HumanMessagePromptTemplate.from_template(
-#                 """You are a machine learning engineer specializing in feature engineering. 
-# Based on the following dataset information, provide recommendations for feature engineering:
-
-# Dataset Information:
-# - Shape: {shape}
-# - Columns and their types:
-# {columns_info}
-
-# - Basic statistics:
-# {basic_stats}
-
-# - Top correlations:
-# {correlations}
-
-# Please provide comprehensive feature engineering recommendations that include:
-
-# 1. Numerical feature transformations (scaling, normalization, log transforms, etc.)
-# 2. Categorical feature encoding strategies
-# 3. Feature interaction suggestions
-# 4. Dimensionality reduction approaches if applicable
-# 5. Time-based feature creation if applicable
-# 6. Text processing techniques if there are text fields
-# 7. Feature selection recommendations
-
-# For each recommendation, explain why it would be beneficial and how it could improve model performance.
-# Be specific to this dataset's characteristics rather than providing generic advice.
-# """
-#             )
-#         ])
-        
-#         # Data quality prompt template
-#         self.data_quality_prompt_template = ChatPromptTemplate.from_messages([
-#             HumanMessagePromptTemplate.from_template(
-#                 """You are a data quality expert. 
-# Based on the following dataset information, provide data quality insights and recommendations:
-
-# Dataset Information:
-# - Shape: {shape}
-# - Columns and their types:
-# {columns_info}
-
-# - Missing values:
-# {missing_info}
-
-# - Basic statistics:
-# {basic_stats}
-
-# Please provide a comprehensive data quality assessment that includes:
-
-# 1. Assessment of data completeness (missing values)
-# 2. Identification of potential data inconsistencies or errors
-# 3. Recommendations for data cleaning and preprocessing
-# 4. Advice on handling outliers
-# 5. Suggestions for data validation checks
-# 6. Recommendations to improve data quality
-
-# Your assessment should be specific to this dataset and provide actionable recommendations.
-# """
-#             )
-#         ])
-        
-#         # QA prompt template
-#         self.qa_prompt_template = ChatPromptTemplate.from_messages([
-#             HumanMessagePromptTemplate.from_template(
-#                 """You are a data scientist answering questions about a dataset. 
-# Based on the following dataset information, please answer the user's question:
-
-# Dataset Information:
-# - Shape: {shape}
-# - Columns and their types:
-# {columns_info}
-
-# - Basic statistics:
-# {basic_stats}
-
-# User's question: {question}
-
-# Please provide a clear, informative answer to the user's question based on the dataset information provided.
-# """
-#             )
-#         ])
-    
-#     def _init_chains(self):
-#         """Initialize all chains using modern RunnableSequence pattern"""
-        
-#         # EDA insights chain
-#         self.eda_chain = self.eda_prompt_template | self.llm
-        
-#         # Feature engineering chain
-#         self.feature_engineering_chain = self.feature_engineering_prompt_template | self.llm
-        
-#         # Data quality chain
-#         self.data_quality_chain = self.data_quality_prompt_template | self.llm
-        
-#         # QA chain
-#         self.qa_chain = self.qa_prompt_template | self.llm
-    
-#     def _format_columns_info(self, columns: List[str], dtypes: Dict[str, str]) -> str:
-#         """Format columns info for prompt"""
-#         return "\n".join([f"- {col} ({dtypes.get(col, 'unknown')})" for col in columns])
-    
-#     def _format_missing_info(self, missing_values: Dict[str, tuple]) -> str:
-#         """Format missing values info for prompt"""
-#         missing_info = "\n".join([f"- {col}: {count} missing values ({percent}%)" 
-#                                for col, (count, percent) in missing_values.items() if count > 0])
-        
-#         if not missing_info:
-#             missing_info = "No missing values detected."
-            
-#         return missing_info
-    
-#     def _execute_chain(
-#         self, 
-#         chain: RunnableSequence, 
-#         input_data: Dict[str, Any], 
-#         operation_name: str
-#     ) -> str:
-#         """
-#         Execute a chain with tracking and error handling
-        
-#         Args:
-#             chain: The LangChain chain to execute
-#             input_data: The input data for the chain
-#             operation_name: Name of the operation for logging
-            
-#         Returns:
-#             str: The generated text
-#         """
-#         try:
-#             start_time = time.time()
-#             with get_openai_callback() as cb:
-#                 result = chain.invoke(input_data).content
-#             elapsed_time = time.time() - start_time
-            
-#             logger.info(f"{operation_name} generated in {elapsed_time:.2f} seconds")
-#             logger.info(f"Tokens used: {cb.total_tokens}, "
-#                       f"Prompt tokens: {cb.prompt_tokens}, "
-#                       f"Completion tokens: {cb.completion_tokens}")
-            
-#             return result
-#         except Exception as e:
-#             error_msg = f"Error executing {operation_name.lower()}: {str(e)}"
-#             logger.error(error_msg)
-#             return error_msg
-    
-#     def generate_eda_insights(self, dataset_info: Dict[str, Any]) -> str:
-#         """
-#         Generate EDA insights based on dataset information using LangChain
-        
-#         Args:
-#             dataset_info: Dictionary containing dataset analysis
-        
-#         Returns:
-#             str: Detailed EDA insights and recommendations
-#         """
-#         logger.info("Generating EDA insights")
-        
-#         # Format the input data
-#         columns_info = self._format_columns_info(
-#             dataset_info.get("columns", []), 
-#             dataset_info.get("dtypes", {})
-#         )
-        
-#         missing_info = self._format_missing_info(
-#             dataset_info.get("missing_values", {})
-#         )
-        
-#         # Prepare input for the chain
-#         input_data = {
-#             "shape": dataset_info.get("shape", "N/A"),
-#             "columns_info": columns_info,
-#             "missing_info": missing_info,
-#             "basic_stats": dataset_info.get("basic_stats", ""),
-#             "correlations": dataset_info.get("correlations", ""),
-#             "sample_data": dataset_info.get("sample_data", "N/A")
-#         }
-        
-#         return self._execute_chain(self.eda_chain, input_data, "EDA insights")
-    
-#     def generate_feature_engineering_recommendations(self, dataset_info: Dict[str, Any]) -> str:
-#         """
-#         Generate feature engineering recommendations based on dataset information using LangChain
-        
-#         Args:
-#             dataset_info: Dictionary containing dataset analysis
-        
-#         Returns:
-#             str: Feature engineering recommendations
-#         """
-#         logger.info("Generating feature engineering recommendations")
-        
-#         # Format the input data
-#         columns_info = self._format_columns_info(
-#             dataset_info.get("columns", []), 
-#             dataset_info.get("dtypes", {})
-#         )
-        
-#         # Prepare input for the chain
-#         input_data = {
-#             "shape": dataset_info.get("shape", "N/A"),
-#             "columns_info": columns_info,
-#             "basic_stats": dataset_info.get("basic_stats", ""),
-#             "correlations": dataset_info.get("correlations", "")
-#         }
-        
-#         return self._execute_chain(
-#             self.feature_engineering_chain, 
-#             input_data, 
-#             "Feature engineering recommendations"
-#         )
-    
-#     def generate_data_quality_insights(self, dataset_info: Dict[str, Any]) -> str:
-#         """
-#         Generate data quality insights based on dataset information using LangChain
-        
-#         Args:
-#             dataset_info: Dictionary containing dataset analysis
-        
-#         Returns:
-#             str: Data quality insights and improvement recommendations
-#         """
-#         logger.info("Generating data quality insights")
-        
-#         # Format the input data
-#         columns_info = self._format_columns_info(
-#             dataset_info.get("columns", []), 
-#             dataset_info.get("dtypes", {})
-#         )
-        
-#         missing_info = self._format_missing_info(
-#             dataset_info.get("missing_values", {})
-#         )
-        
-#         # Prepare input for the chain
-#         input_data = {
-#             "shape": dataset_info.get("shape", "N/A"),
-#             "columns_info": columns_info,
-#             "missing_info": missing_info,
-#             "basic_stats": dataset_info.get("basic_stats", "")
-#         }
-        
-#         return self._execute_chain(
-#             self.data_quality_chain, 
-#             input_data, 
-#             "Data quality insights"
-#         )
-    
-#     def answer_dataset_question(self, question: str, dataset_info: Dict[str, Any]) -> str:
-#         """
-#         Answer a specific question about the dataset using LangChain
-        
-#         Args:
-#             question: User's question about the dataset
-#             dataset_info: Dictionary containing dataset analysis
-        
-#         Returns:
-#             str: Answer to the user's question
-#         """
-#         logger.info(f"Answering dataset question: {question[:50]}...")
-        
-#         # Format the input data
-#         columns_info = self._format_columns_info(
-#             dataset_info.get("columns", []), 
-#             dataset_info.get("dtypes", {})
-#         )
-        
-#         # Prepare input for the chain
-#         input_data = {
-#             "shape": dataset_info.get("shape", "N/A"),
-#             "columns_info": columns_info,
-#             "basic_stats": dataset_info.get("basic_stats", ""),
-#             "question": question
-#         }
-        
-#         return self._execute_chain(
-#             self.qa_chain, 
-#             input_data, 
-#             "Answer"
-#         )
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 """
 LLM Inference Module