Create src/agents/genai_integration.py

src/agents/genai_integration.py

@@ -0,0 +1,98 @@
+import openai
+import pandas as pd
+import numpy as np
+from typing import Dict, List, Optional
+import json
+import logging
+
+logger = logging.getLogger(__name__)
+
+class ForecastingAIAssistant:
+    """AI assistant for forecasting tasks using GenAI."""
+    
+    def __init__(self, api_key: str, model_name: str = "gpt-3.5-turbo"):
+        self.api_key = api_key
+        self.model_name = model_name
+        openai.api_key = api_key
+    
+    def generate_forecast_interpretation(self, data_summary: Dict, 
+                                       forecast_results: Dict,
+                                       metrics: Dict) -> str:
+        """Generate comprehensive interpretation of forecasting results."""
+        try:
+            prompt = f"""
+            As a senior data scientist with 35 years of experience, provide a comprehensive analysis of these forecasting results:
+            
+            Data Summary: {json.dumps(data_summary, indent=2)}
+            Forecast Results: {json.dumps(forecast_results, indent=2)}
+            Performance Metrics: {json.dumps(metrics, indent=2)}
+            
+            Please provide:
+            1. Key insights about the forecast quality
+            2. Potential business implications
+            3. Limitations of the current approach
+            4. Recommendations for improvement
+            5. Any anomalies or patterns worth noting
+            
+            Write in a professional yet accessible tone suitable for both technical and business audiences.
+            """
+            
+            response = openai.ChatCompletion.create(
+                model=self.model_name,
+                messages=[
+                    {"role": "system", "content": "You are a senior data scientist with expertise in time series forecasting."},
+                    {"role": "user", "content": prompt}
+                ],
+                temperature=0.3,
+                max_tokens=1000
+            )
+            
+            return response.choices[0].message.content
+        except Exception as e:
+            logger.error(f"Error generating interpretation: {str(e)}")
+            return f"Interpretation generation failed: {str(e)}"
+    
+    def generate_business_recommendations(self, business_context: str,
+                                        forecast_results: Dict,
+                                        historical_data: pd.Series) -> str:
+        """Generate business recommendations based on forecasts."""
+        try:
+            # Create historical data summary
+            hist_summary = {
+                "period": f"{historical_data.index.min()} to {historical_data.index.max()}" if hasattr(historical_data, 'index') else "N/A",
+                "data_points": len(historical_data),
+                "mean_value": historical_data.mean(),
+                "trend": "upward" if historical_data.iloc[-1] > historical_data.iloc[0] else "downward" if len(historical_data) > 1 else "stable"
+            }
+            
+            prompt = f"""
+            Based on the forecasting results and business context, provide actionable recommendations:
+            
+            Business Context: {business_context}
+            Forecast Results: {json.dumps(forecast_results, indent=2)}
+            Historical Trends: {json.dumps(hist_summary, indent=2)}
+            
+            Provide specific, actionable recommendations including:
+            1. Operational adjustments
+            2. Risk mitigation strategies
+            3. Opportunities to capitalize on
+            4. Timeline considerations
+            5. Key metrics to monitor
+            
+            Tailor recommendations to the specific business context.
+            """
+            
+            response = openai.ChatCompletion.create(
+                model=self.model_name,
+                messages=[
+                    {"role": "system", "content": "You are a business strategy consultant with expertise in data-driven decision making."},
+                    {"role": "user", "content": prompt}
+                ],
+                temperature=0.3,
+                max_tokens=1000
+            )
+            
+            return response.choices[0].message.content
+        except Exception as e:
+            logger.error(f"Error generating recommendations: {str(e)}")
+            return f"Recommendation generation failed: {str(e)}"