oxygen nitrogen forecast added or summary

app.py

@@ -151,8 +151,22 @@ class DataContextManager:
                 "sample_countries": ["India", "Brazil", "Germany"],
                 "data_type": "chemical time-series",
                 "good_for": "Tracking organic pollution trends over time by country"
-            }
-
+            },
+            
+            "oxygen_nitrogen_summary": {
+                "title": "Oxygen Demand vs Oxidized Nitrogen Levels",
+                "description": "Yearly average of oxygen demand and oxidized nitrogen by country. Helps track water pollution and ecological stress.",
+                "contains": "Average biochemical oxygen demand and oxidized nitrogen levels across countries and years.",
+                "columns": {
+                    "Country": "Country name",
+                    "Year": "Year of sample",
+                    "Average Oxygen Demand (mg/L)": "Mean oxygen demand (mg/L)",
+                    "Average Oxidized Nitrogen (mg/L)": "Mean oxidized nitrogen concentration (mg/L)"
+                },
+                "sample_countries": ["India", "Germany", "Brazil"],
+                "data_type": "chemical time-series",
+                "good_for": "Comparing pollution trends over time, analyzing nutrient and organic load impacts on ecosystems"
+            },
 
         }
         
@@ -242,6 +256,11 @@ class DataContextManager:
             if any(keyword in query_lower for keyword in ['ph', 'pH levels', 'acidity', 'alkaline', 'acidic', 'neutral']):
                 if dataset_name == 'ph_levels':
                     score += 3
+                    
+            if any(keyword in query_lower for keyword in ['oxygen demand', 'oxidized nitrogen', 'pollution correlation', 'combined', 'organic pollution', 'dead zones']):
+                if dataset_name == 'oxygen_nitrogen_summary':
+                    score += 3
+
 
 
 
@@ -610,6 +629,15 @@ def enhanced_determine_query_type(query: str) -> Tuple[str, Dict[str, Any]]:
             'focus_columns': ['Country', 'Year', 'Average Oxygen Demand (mg/L)', 'Notes'],
             'data_type': 'chemical time-series'
         }
+    
+    if re.search(r'\b(oxygen demand|oxidized nitrogen|joint pollution|combined pollution|bod vs nitrate|pollution indicators)\b', query_lower):
+        logger.info("Classified as: data (oxygen + nitrogen)")
+        return "data", {
+            'dataset_preference': 'oxygen_nitrogen_summary',
+            'focus_columns': ['Country', 'Year', 'Average Oxygen Demand (mg/L)', 'Average Oxidized Nitrogen (mg/L)'],
+            'data_type': 'chemical time-series'
+        }
+
 
 
     # 5. COUNTRY QUERIES
@@ -691,6 +719,7 @@ csv_paths = {
     "oxidized_nitrogen": "data/oxidized_nitrogen_country_year.csv",
     "ph_levels": "data/ph_country_year_summary.csv",
     "oxygen_demand": "data/oxygen_demand_country_year.csv",
+    "oxygen_nitrogen_summary": "data/oxygen_nitrogen_merged.csv",
 }
 data_manager = DataContextManager(csv_paths)
 

data/oxygen_nitrogen_merged.csv

@@ -0,0 +1,288 @@
+Country,Year,Average Oxygen Demand (mg/L),Average Oxidized Nitrogen (mg/L)
+Argentina,2011,14.0,1.865
+Argentina,2012,3.625,1.92875
+Argentina,2013,8.875,1.645
+Argentina,2021,9.0,0.6574427425237054
+Austria,2014,2.0376708860759494,3.376383121735209
+Austria,2017,2.475,1.3159159514257557
+Austria,2021,1.213720238095238,1.1715438317590858
+Belgium,2013,15.338392434988181,1.8688082959641257
+Belgium,2014,18.248337837837838,1.7404879385964913
+Belgium,2015,18.228374816983894,1.7644712765957447
+Belgium,2016,16.041923076923077,1.9332676501580612
+Belgium,2017,15.747077625570777,1.7452829989440337
+Belgium,2018,15.696199494949495,1.7752469631440162
+Belgium,2019,16.125688073394496,1.9046028263414636
+Belgium,2020,15.737297668038408,1.907051225473251
+Belgium,2021,15.312514757969304,1.9739512348949582
+Bulgaria,2013,12.3275,0.410585912
+Bulgaria,2014,10.646153846153846,0.41482244143999997
+Bulgaria,2015,9.57826923076923,0.60752570477
+Bulgaria,2016,10.511176470588236,0.55052248682
+Bulgaria,2017,10.855294117647059,0.5040116374
+Bulgaria,2018,11.2659375,0.5032869307500001
+Bulgaria,2019,10.415909090909091,0.475721078765
+Bulgaria,2020,8.674615384615384,0.43285865713529414
+Canada,2015,4.45,0.1907995420583273
+Canada,2016,4.094117647058823,0.18714850557244175
+Canada,2017,7.616216216216216,0.19995251256281407
+Canada,2018,5.644444444444444,0.18171423348132673
+Canada,2019,5.9399999999999995,0.16807557312252963
+Croatia,2013,6.326722846441948,0.5885122269698675
+Croatia,2014,3.6418724409448817,0.519986851756404
+Croatia,2015,3.012723076923077,0.10748787040216902
+Croatia,2016,2.9133556149732622,0.08345541269784557
+Croatia,2017,3.0700136239782014,0.022022485557532785
+Croatia,2018,2.9204411764705887,0.024047645773629032
+Croatia,2019,3.3106880733944957,0.420117989596497
+Croatia,2021,4.5291015625,0.3672388579704961
+Estonia,2013,9.707692307692307,0.1340197361076923
+Estonia,2014,9.41547619047619,0.12397294644444445
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+Uruguay,2003,3.6077777777777778,1.1195847457627117
+Uruguay,2007,2.4,0.5562118959107807
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+Uruguay,2016,6.625877192982457,0.4873419972640219
+Uruguay,2017,6.270354609929078,0.5411356521739131
+Uruguay,2018,5.557230046948357,0.42352941176470593
+Uruguay,2019,6.568865546218488,0.28055714285714284
+Uruguay,2020,5.817242990654206,0.3460952671755725
+Uruguay,2021,8.024545454545455,0.3644077363896848
+Uruguay,2022,7.0,0.29176
+Uruguay,2023,9.09442857142857,0.5483135338345865