Create app.py

app.py

@@ -0,0 +1,131 @@
+import numpy as np
+import pandas as pd
+from statsmodels.tsa.seasonal import seasonal_decompose
+from statsmodels.tsa.arima.model import ARIMA
+from sklearn.ensemble import IsolationForest
+from sklearn.preprocessing import StandardScaler
+import gradio as gr
+import traceback
+import logging
+
+# Set up logging
+logging.basicConfig(level=logging.ERROR)
+
+class TrendAnalysisAgent:
+    def analyze(self, data):
+        result = seasonal_decompose(data, model='additive', period=1)
+        return result.trend
+
+class SeasonalityDetectionAgent:
+    def detect(self, data):
+        result = seasonal_decompose(data, model='additive', period=12)
+        return result.seasonal
+
+class AnomalyDetectionAgent:
+    def detect(self, data):
+        scaler = StandardScaler()
+        data_scaled = scaler.fit_transform(data.reshape(-1, 1))
+        iso_forest = IsolationForest(contamination=0.1, random_state=42)
+        anomalies = iso_forest.fit_predict(data_scaled)
+        return anomalies == -1
+
+class FeatureExtractionAgent:
+    def extract(self, data):
+        features = pd.DataFrame({
+            'mean': [np.mean(data)],
+            'std': [np.std(data)],
+            'min': [np.min(data)],
+            'max': [np.max(data)]
+        })
+        return features
+
+class ForecastingAgent:
+    def forecast(self, data, steps):
+        model = ARIMA(data, order=(1,1,1))
+        results = model.fit()
+        forecast = results.forecast(steps=steps)
+        return forecast
+
+class RetrievalMechanism:
+    def __init__(self):
+        self.database = {}
+    
+    def store(self, key, data):
+        self.database[key] = data
+    
+    def retrieve(self, key):
+        return self.database.get(key, None)
+
+class MockLanguageModel:
+    def generate_insight(self, data, trend, seasonality, anomalies, features, forecast):
+        insight = f"The time series has a mean of {features['mean'].values[0]:.2f} and standard deviation of {features['std'].values[0]:.2f}. "
+        insight += f"There {'are' if anomalies.sum() > 1 else 'is'} {anomalies.sum()} anomal{'ies' if anomalies.sum() > 1 else 'y'} detected. "
+        insight += f"The forecast suggests a {'upward' if forecast[-1] > data[-1] else 'downward'} trend in the near future."
+        return insight
+
+class AgenticRAG:
+    def __init__(self):
+        self.trend_agent = TrendAnalysisAgent()
+        self.seasonality_agent = SeasonalityDetectionAgent()
+        self.anomaly_agent = AnomalyDetectionAgent()
+        self.feature_agent = FeatureExtractionAgent()
+        self.forecasting_agent = ForecastingAgent()
+        self.retrieval = RetrievalMechanism()
+        self.language_model = MockLanguageModel()
+    
+    def process(self, data, forecast_steps):
+        trend = self.trend_agent.analyze(data)
+        seasonality = self.seasonality_agent.detect(data)
+        anomalies = self.anomaly_agent.detect(data)
+        features = self.feature_agent.extract(data)
+        forecast = self.forecasting_agent.forecast(data, forecast_steps)
+        
+        insight = self.language_model.generate_insight(data, trend, seasonality, anomalies, features, forecast)
+        
+        return trend, seasonality, anomalies, features, forecast, insight
+
+def analyze_time_series(data, forecast_steps):
+    try:
+        data = np.array([float(x) for x in data.split(',')])
+        if len(data) < 2:
+            raise ValueError("Input data must contain at least two values.")
+        
+        agentic_rag = AgenticRAG()
+        trend, seasonality, anomalies, features, forecast, insight = agentic_rag.process(data, forecast_steps)
+        
+        return {
+            "Trend": trend,
+            "Seasonality": seasonality,
+            "Anomalies": anomalies,
+            "Features": features.to_dict(orient='records')[0],
+            "Forecast": forecast.tolist(),
+            "Insight": insight
+        }
+    except Exception as e:
+        error_msg = f"An error occurred: {str(e)}\n{traceback.format_exc()}"
+        logging.error(error_msg)
+        return {
+            "Error": error_msg
+        }
+
+iface = gr.Interface(
+    fn=analyze_time_series,
+    inputs=[
+        gr.Textbox(label="Enter comma-separated time series data"),
+        gr.Number(label="Number of steps to forecast", value=5)
+    ],
+    outputs=[
+        gr.Plot(label="Trend"),
+        gr.Plot(label="Seasonality"),
+        gr.Plot(label="Anomalies"),
+        gr.JSON(label="Features"),
+        gr.Plot(label="Forecast"),
+        gr.Textbox(label="Insight"),
+        gr.Textbox(label="Error", visible=False)
+    ],
+    title="Agentic RAG Time Series Analysis",
+    description="Enter a comma-separated list of numbers representing your time series data, and specify the number of steps to forecast."
+)
+
+if __name__ == "__main__":
+    iface.launch()