Update app.py

app.py

@@ -67,11 +67,11 @@ ai_suggestion_choices = ["Move", "Pause Rent", "Repair", "Replace"]
 # AI suggestion logic
 def call_ai_model(usage, idle, freq, cost, last):
     try:
-        # Ensure inputs are valid numbers
-        usage = float(usage) if usage is not None else 0.0
-        idle = float(idle) if idle is not None else 0.0
-        freq = float(freq) if freq is not None else 0.0
-        cost = float(cost) if cost is not None else 0.0
+        # Ensure inputs are valid numbers and not NaN
+        usage = float(usage) if usage is not None and not np.isnan(usage) else 0.0
+        idle = float(idle) if idle is not None and not np.isnan(idle) else 0.0
+        freq = float(freq) if freq is not None and not np.isnan(freq) else 0.0
+        cost = float(cost) if cost is not None and not np.isnan(cost) else 0.0
 
         total = usage + idle
         ratio = usage / total if total > 0 else 0
@@ -126,7 +126,7 @@ def process_equipment_utilization(equip, proj, use_h, idle_h, move_f, cost_h, la
         record_data = {
             "Equipment_Name__c": equip,
             "Project_Name__c": proj,
-            "Usage_Hours__c": float(use_h),  # Ensure float
+            "Usage_Hours__c": float(use_h),
             "Idle_Hours__c": float(idle_h),
             "AI_Suggestion__c": ai_sug,
             "Suggestion_Confidence__c": float(conf * 100),
@@ -137,6 +137,11 @@ def process_equipment_utilization(equip, proj, use_h, idle_h, move_f, cost_h, la
             "Dashboard_Flag__c": False
         }
 
+        # Final check for NaN in record_data
+        for key, value in record_data.items():
+            if isinstance(value, float) and np.isnan(value):
+                raise ValueError(f"Field {key} contains NaN, which is not allowed in Salesforce requests.")
+
         # Log the record data for debugging
         logger.info(f"Sending record data to Salesforce: {record_data}")
 
@@ -211,7 +216,6 @@ def process_equipment_utilization(equip, proj, use_h, idle_h, move_f, cost_h, la
 # Format output
 def format_output(result):
     summary = result.get("Summary", {})
-    # Cost formatting
     cost_val = summary.get("Cost per Hour", 0)
     try:
         cost_str = locale.currency(cost_val, grouping=True)
@@ -231,7 +235,9 @@ 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)
@@ -239,61 +245,49 @@ def format_output(result):
 # Gradio callbacks
 def manual_input(equipment, project, usage, idle, freq, cost, last, ai_suggestion):
     try:
-        # Validate inputs
         if not equipment or equipment not in equipment_choices:
             raise ValueError("Please select a valid Equipment Name.")
         if not project or project not in project_choices:
             raise ValueError("Please select a valid Project Name.")
-        if usage is None or usage < 0:
+        if usage is None or usage < 0 or np.isnan(usage):
             raise ValueError("Usage Hours must be a non-negative number.")
-        if idle is None or idle < 0:
+        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 freq < 0:
-            raise ValueError("Movement Frequency must be a non-negative number.")
-        if cost is None or cost < 0:
-            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")
-    except Exception as e:
-        logger.error(f"Error in manual_input: {e}")
-        return f"Error: {str(e)}", 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)
-        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 invalid values
-        numeric_columns = ['usage_hours', 'idle_hours', 'movement_frequency', 'cost_per_hour']
-        for col in numeric_columns:
+        if freq is None or freqNUMERIC_FIELDS = ['usage_hours', 'idle_hours', 'movement_frequency', 'cost_per_hour']
+        for col in NUMERIC_FIELDS:
             if df[col].isna().any():
-                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)}")
+                raise ValueError(f"ColumnACHINERY_FIELDS = ['equipment_name', 'project_name']
+        for col in MACHINERY_FIELDS:
+            if col not in df.columns:
+                raise ValueError(f"Missing required column: {col}")
+            if df[col].isna().any():
+                raise ValueError(f"Column '{col}' contains missing values. Please ensure all rows have valid equipment and project names.")
 
-        ids = []
-        for _, row in df.iterrows():
-            rec = process_equipment_utilization(
-                row['equipment_name'], row['project_name'],
-                row['usage_hours'], row['idle_hours'],
-                row['movement_frequency'], row['cost_per_hour'],
-                row.get('last_maintenance', 'N/A'), row.get('ai_suggestion', '')
+        # Validate ai_suggestion if provided
+        if 'ai_suggestion' in df.columns:
+            df['ai_suggestion'] = df['ai_suggestion'].apply(
+                lambda x: x if x in ai_suggestion_choices or pd.isna(x) or x == '' else None
             )
-            ids.append(rec['Salesforce_Record_Id'])
-        return {"records": ids}
+
+        ids = []
+        csv_files = []
+        for idx, row in df.iterrows():
+            try:
+                rec = process_equipment_utilization(
+                    row['equipment_name'], row['project_name'],
+                    row['usage_hours'], row['idle_hours'],
+                    row['movement_frequency'], row['cost_per_hour'],
+                    row.get('last_maintenance', 'N/A'), row.get('ai_suggestion', '')
+                )
+                ids.append(rec['Salesforce_Record_Id'])
+                csv_files.append(rec['CSV_Report_Link'])
+            except Exception as e:
+                logger.error(f"Error processing row {idx + 2}: {e}")
+                raise ValueError(f"Error processing row {idx + 2}: {str(e)}")
+        return {"records": ids}, csv_files
     except Exception as e:
         logger.error(f"Error in batch_upload: {e}")
-        return {"error": str(e)}
+        return {"error": str(e)}, None
 
 # Interface
 with gr.Blocks() as app:
@@ -316,17 +310,19 @@ with gr.Blocks() as app:
                 clear_btn      = gr.Button("🧹 Clear")
                 result_txt     = gr.Markdown(elem_id="result-box")
                 report_file    = gr.File(label="📃 Download PDF Report")
+                csv_file       = gr.File(label="📄 Download CSV Report")
                 submit_btn.click(
                     fn=manual_input,
                     inputs=[equipment_dropdown, project_dropdown, usage, idle, freq, cost, last, ai_dropdown],
-                    outputs=[result_txt, report_file]
+                    outputs=[result_txt, report_file, csv_file]
                 )
-                clear_btn.click(lambda: ("", None), None, [result_txt, report_file])
+                clear_btn.click(lambda: ("", None, None), None, [result_txt, report_file, csv_file])
         with gr.TabItem("CSV Upload"):
             with gr.Group():
                 csv_file   = gr.File(label="📂 Upload CSV file", file_types=[".csv"])
                 csv_output = gr.JSON(label="📄 Batch Upload Results")
-                csv_file.change(fn=batch_upload, inputs=csv_file, outputs=csv_output)
+                csv_downloads = gr.File(label="📥 Download Generated CSV Reports")
+                csv_file.change(fn=batch_upload, inputs=csv_file, outputs=[csv_output, csv_downloads])
     app.css = """
 .gradio-container { background-color: #ffffff !important; }
 #app-title { text-align: center !important; }