Update app.py

app.py

@@ -235,9 +235,7 @@ def format_output(result):
         f" • Project: {summary.get('Project', 'N/A')}",
         f" • Usage Hours: {summary.get('Usage Hours', 0):.2f}",
         f" • Idle Hours: {summary.get('Idle Hours', 0):.2f}",
-        f" • Cost per
-
- Hour: {cost_str}",
+        f" • Cost per Hour: {cost_str}",
         f" • Last Maintenance: {summary.get('Last Maintenance', 'N/A')}"
     ]
     return "\n".join(lines)
@@ -253,10 +251,43 @@ def manual_input(equipment, project, usage, idle, freq, cost, last, ai_suggestio
             raise ValueError("Usage Hours must be a non-negative number.")
         if idle is None or idle < 0 or np.isnan(idle):
             raise ValueError("Idle Hours must be a non-negative number.")
-        if freq is None or freqNUMERIC_FIELDS = ['usage_hours', 'idle_hours', 'movement_frequency', 'cost_per_hour']
+        if freq is None or freq < 0 or np.isnan(freq):
+            raise ValueError("Movement Frequency must be a non-negative number.")
+        if cost is None or cost < 0 or np.isnan(cost):
+            raise ValueError("Cost per Hour must be a non-negative number.")
+
+        last_val = last or "N/A"
+        res = process_equipment_utilization(equipment, project, usage, idle, freq, cost, last_val, ai_suggestion)
+        formatted = format_output(res)
+        return formatted, res.get("Report_File_Path"), res.get("CSV_Report_Link")
+    except Exception as e:
+        logger.error(f"Error in manual_input: {e}")
+        return f"Error: {str(e)}", None, None
+
+def batch_upload(csv_file):
+    try:
+        if not csv_file:
+            raise ValueError("Please upload a CSV file.")
+        df = pd.read_csv(csv_file.name)
+        
+        # Validate required columns
+        required_columns = ['equipment_name', 'project_name', 'usage_hours', 'idle_hours', 'movement_frequency', 'cost_per_hour']
+        missing_columns = [col for col in required_columns if col not in df.columns]
+        if missing_columns:
+            raise ValueError(f"CSV file is missing required columns: {', '.join(missing_columns)}")
+
+        # Validate numeric columns for NaN or non-numeric values
+        NUMERIC_FIELDS = ['usage_hours', 'idle_hours', 'movement_frequency', 'cost_per_hour']
         for col in NUMERIC_FIELDS:
             if df[col].isna().any():
-                raise ValueError(f"ColumnACHINERY_FIELDS = ['equipment_name', 'project_name']
+                raise ValueError(f"Column '{col}' contains missing or invalid values (e.g., NaN). Please ensure all values are valid numbers.")
+            try:
+                df[col] = df[col].astype(float)
+            except ValueError as e:
+                raise ValueError(f"Column '{col}' contains non-numeric values: {str(e)}")
+
+        # Validate equipment_name and project_name for missing values
+        MACHINERY_FIELDS = ['equipment_name', 'project_name']
         for col in MACHINERY_FIELDS:
             if col not in df.columns:
                 raise ValueError(f"Missing required column: {col}")