Fix: Add on-demand prediction for /timeseries when no cache

app.py

@@ -527,10 +527,103 @@ async def get_timeseries(request: TimeSeriesRequest):
             timestamp=datetime.now().isoformat()
         )
     
-    # No cache - run prediction
-    logger.info(f"No cache for {field_hash}, running fresh prediction...")
-    # ... (keep original logic for fresh prediction)
-    raise HTTPException(404, f"No cached data. Use POST /predict to start prediction job.")
+    # No cache - run on-demand prediction for single metric
+    logger.info(f"No cache for {field_hash}, running on-demand prediction...")
+    
+    try:
+        # Fetch satellite data
+        fetcher = SatelliteFetcher(polygon)
+        
+        if request.metric in ['VV', 'VH']:
+            fetcher.fetch_sar_data('sar_data.csv')
+            csv_file = 'sar_data.csv'
+            target_col = f'{request.metric}_mean_dB'
+        else:
+            fetcher.fetch_sentinel2_data('sentinel2_data.csv')
+            csv_file = 'sentinel2_data.csv'
+            target_col = request.metric
+        
+        # Read historical data
+        if not os.path.exists(csv_file):
+            raise HTTPException(404, "No satellite data available for this location")
+        
+        df = pd.read_csv(csv_file)
+        if 'ds' not in df.columns:
+            raise HTTPException(500, "Invalid data format")
+        
+        # Convert to DataPoint list
+        historical = []
+        for _, row in df.iterrows():
+            if target_col in row and not pd.isna(row[target_col]):
+                historical.append(DataPoint(
+                    date=str(row['ds']),
+                    value=round(float(row[target_col]), 4)
+                ))
+        
+        if len(historical) < 10:
+            raise HTTPException(400, f"Insufficient data points ({len(historical)}) for forecasting")
+        
+        logger.info(f"Historical data: {len(historical)} points")
+        
+        # Run AutoNHITS prediction
+        logger.info(f"Running AutoNHITS prediction...")
+        predictor = AutoTimeSeriesPredictor()
+        
+        predictions = predictor.tune_and_predict(
+            csv_path=csv_file,
+            field_coords=polygon,
+            target_col=target_col,
+            output_file='predictions.csv',
+            num_samples=3  # Quick tuning for API
+        )
+        
+        # Convert predictions to ForecastPoint list
+        forecast = []
+        for _, row in predictions.iterrows():
+            value = float(row['predicted_y'])
+            forecast.append(ForecastPoint(
+                date=str(row['ds'].date()) if hasattr(row['ds'], 'date') else str(row['ds']),
+                value=round(value, 4),
+                confidence_low=round(value * 0.9, 4),
+                confidence_high=round(value * 1.1, 4)
+            ))
+        
+        logger.info(f"Forecast: {len(forecast)} points")
+        
+        # Calculate stats
+        all_values = [p.value for p in historical]
+        stats = {
+            "min": round(min(all_values), 4),
+            "max": round(max(all_values), 4),
+            "mean": round(sum(all_values) / len(all_values), 4),
+            "count": len(all_values),
+            "forecast_count": len(forecast)
+        }
+        
+        # Cleanup temp files
+        for f in ['sar_data.csv', 'sentinel2_data.csv', 'predictions.csv']:
+            if os.path.exists(f):
+                os.remove(f)
+        
+        logger.info(f"SUCCESS - Trend: {calculate_trend(all_values)}")
+        
+        return TimeSeriesResponse(
+            success=True,
+            metric=request.metric,
+            historical=historical,
+            forecast=forecast,
+            trend=calculate_trend(all_values[-20:] if len(all_values) > 20 else all_values),
+            stats=stats,
+            timestamp=datetime.now().isoformat()
+        )
+        
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"Error: {str(e)}")
+        import traceback
+        logger.error(traceback.format_exc())
+        raise HTTPException(500, str(e))
 
 
 if __name__ == "__main__":