Update app.py

app.py

@@ -28,10 +28,18 @@ def analyze_file(uploaded_file):
         # Check if required columns are present
         if 'Gain (dB)' in df.columns and 'Frequency (GHz)' in df.columns and 'Efficiency (%)' in df.columns:
             # Handle NaN values by replacing them with the mean of the column or 0
+            df['Gain (dB)'] = pd.to_numeric(df['Gain (dB)'], errors='coerce')  # Convert to numeric, coerce errors to NaN
+            df['Frequency (GHz)'] = pd.to_numeric(df['Frequency (GHz)'], errors='coerce')  # Convert to numeric, coerce errors to NaN
+            df['Efficiency (%)'] = pd.to_numeric(df['Efficiency (%)'], errors='coerce')  # Convert to numeric, coerce errors to NaN
+
+            # Replace NaN values with the mean of the respective columns
             df['Gain (dB)'].fillna(df['Gain (dB)'].mean(), inplace=True)
             df['Frequency (GHz)'].fillna(df['Frequency (GHz)'].mean(), inplace=True)
             df['Efficiency (%)'].fillna(df['Efficiency (%)'].mean(), inplace=True)
 
+            # Ensure that Frequency (GHz) column is treated as a float (for safe calculations)
+            df['Frequency (GHz)'] = df['Frequency (GHz)'].astype(float)
+
             # Convert pandas columns to numpy arrays before performing operations
             gain_values = np.array(df['Gain (dB)'])
             freq_values = np.array(df['Frequency (GHz)'])