Update app.py

app.py

@@ -52,38 +52,38 @@ class EnhancedOceanClimateAgent:
         }
         
         try:
-            print(f"🌐 Fetching {product} data for station {station_id}")
-            print(f"📅 Date range: {begin_str} to {end_str}")
+            print(f"Fetching {product} data for station {station_id}")
+            print(f"Date range: {begin_str} to {end_str}")
             
             response = requests.get(self.noaa_base_url, params=params, timeout=30)
             
             if response.status_code != 200:
-                print(f"❌ HTTP Error {response.status_code}: {response.text}")
+                print(f"HTTP Error {response.status_code}: {response.text}")
                 return None
                 
             data = response.json()
             
             if 'data' in data and data['data']:
-                print(f"✅ Successfully fetched {len(data['data'])} records for {product}")
+                print(f"Successfully fetched {len(data['data'])} records for {product}")
                 return pd.DataFrame(data['data'])
             elif 'error' in data:
-                print(f"❌ NOAA API error for {product}: {data['error']['message']}")
+                print(f"NOAA API error for {product}: {data['error']['message']}")
                 return None
             else:
-                print(f"❌ No data returned for {product}")
+                print(f"No data returned for {product}")
                 return None
                 
         except requests.exceptions.Timeout:
-            print(f"⏰ Timeout fetching {product} data")
+            print(f"Timeout fetching {product} data")
             return None
         except requests.exceptions.RequestException as e:
-            print(f"❌ Request failed for {product}: {str(e)}")
+            print(f"Request failed for {product}: {str(e)}")
             return None
         except json.JSONDecodeError as e:
-            print(f"❌ JSON decode error for {product}: {str(e)}")
+            print(f"JSON decode error for {product}: {str(e)}")
             return None
         except Exception as e:
-            print(f"❌ Unexpected error fetching {product}: {str(e)}")
+            print(f"Unexpected error fetching {product}: {str(e)}")
             return None
 
     def get_comprehensive_station_data(self, station_name, days_back=30):
@@ -96,8 +96,8 @@ class EnhancedOceanClimateAgent:
         end_date = datetime.utcnow() - timedelta(hours=2)  # 2 hour buffer
         start_date = end_date - timedelta(days=days_back)
         
-        print(f"🔍 Fetching data for {station_name} (ID: {station_id})")
-        print(f"📅 Date range: {start_date} to {end_date}")
+        print(f"Fetching data for {station_name} (ID: {station_id})")
+        print(f"Date range: {start_date} to {end_date}")
         
         # Priority order - start with most reliable products
         products_to_fetch = [
@@ -112,20 +112,20 @@ class EnhancedOceanClimateAgent:
         success_count = 0
         
         for product_name, product_code in products_to_fetch:
-            print(f"🔄 Attempting to fetch {product_name}...")
+            print(f"Attempting to fetch {product_name}...")
             data = self.get_noaa_data(station_id, product_code, start_date, end_date)
             
             if data is not None and not data.empty:
                 all_data[product_name] = data
                 success_count += 1
-                print(f"✅ {product_name}: {len(data)} records")
+                print(f"{product_name}: {len(data)} records")
             else:
-                print(f"❌ {product_name}: No data available")
+                print(f"{product_name}: No data available")
         
         if success_count == 0:
             return None, f"No data available for station {station_name} in the specified time period. This could be due to: station maintenance, data processing delays, or the station may not support the requested data types."
         
-        print(f"🎉 Successfully retrieved {success_count}/{len(products_to_fetch)} data types")
+        print(f"Successfully retrieved {success_count}/{len(products_to_fetch)} data types")
         return all_data, f"Successfully retrieved {success_count}/{len(products_to_fetch)} data types"
     
     def process_noaa_data(self, raw_data):
@@ -141,7 +141,7 @@ class EnhancedOceanClimateAgent:
             df['datetime'] = pd.to_datetime(df['t'])
             df['water_level'] = pd.to_numeric(df['v'], errors='coerce')
             base_df = df[['datetime', 'water_level']].copy()
-            print(f"📊 Base dataset: water_level with {len(base_df)} records")
+            print(f"Base dataset: water_level with {len(base_df)} records")
         
         # If no water level, try other datasets
         if base_df is None:
@@ -156,7 +156,7 @@ class EnhancedOceanClimateAgent:
                         column_name = product_name.replace('_temperature', '_temp')
                         df[column_name] = pd.to_numeric(df['v'], errors='coerce')
                         base_df = df[['datetime', column_name]].copy()
-                    print(f"📊 Base dataset: {product_name} with {len(base_df)} records")
+                    print(f"Base dataset: {product_name} with {len(base_df)} records")
                     break
         
         if base_df is None:
@@ -191,7 +191,7 @@ class EnhancedOceanClimateAgent:
         # Sort by datetime and remove duplicates
         base_df = base_df.sort_values('datetime').drop_duplicates(subset=['datetime'])
         
-        print(f"📈 Final processed dataset: {len(base_df)} records with {len(base_df.columns)-1} parameters")
+        print(f"Final processed dataset: {len(base_df)} records with {len(base_df.columns)-1} parameters")
         return base_df
     
     def detect_anomalies(self, data, column, window=24):  # 24 hours for hourly data
@@ -266,7 +266,7 @@ class EnhancedOceanClimateAgent:
                     alerts.append(f"High {col.replace('_', ' ')} anomaly frequency: {anomaly_pct:.1f}% at {station_name}")
         
         if not alerts:
-            alerts.append(f"✅ No significant anomalies detected at {station_name}")
+            alerts.append(f"No significant anomalies detected at {station_name}")
         
         return analysis, alerts
 
@@ -279,12 +279,12 @@ def analyze_real_ocean_data(station_name, days_back, anomaly_sensitivity, use_re
     agent.anomaly_threshold = anomaly_sensitivity
     
     if use_real_data:
-        print(f"🚀 Starting real data analysis for {station_name}")
+        print(f"Starting real data analysis for {station_name}")
         # Fetch real NOAA data
         raw_data, status_msg = agent.get_comprehensive_station_data(station_name, days_back)
         
         if raw_data is None:
-            error_msg = f"❌ Error fetching real data: {status_msg}"
+            error_msg = f"Error fetching real data: {status_msg}"
             print(error_msg)
             return None, None, None, error_msg, "No alerts - data unavailable", None
         
@@ -292,12 +292,12 @@ def analyze_real_ocean_data(station_name, days_back, anomaly_sensitivity, use_re
         data = agent.process_noaa_data(raw_data)
         
         if data is None or data.empty:
-            error_msg = "❌ No processable data available after fetching from NOAA"
+            error_msg = "No processable data available after fetching from NOAA"
             print(error_msg)
             return None, None, None, error_msg, "No alerts - data unavailable", None
         
-        data_source = f"✅ Real NOAA data from {station_name} ({status_msg})"
-        print(f"🎯 {data_source}")
+        data_source = f"Real NOAA data from {station_name} ({status_msg})"
+        print(f"{data_source}")
         
     else:
         print("🔧 Using synthetic demonstration data")
@@ -320,7 +320,7 @@ def analyze_real_ocean_data(station_name, days_back, anomaly_sensitivity, use_re
     # Create CSV for download
     csv_file_path = save_csv_temp(data)
     
-    print("✅ Analysis completed successfully")
+    print("Analysis completed successfully")
     return fig1, fig2, fig3, analysis_text, alerts_text, csv_file_path