Update app.py

app.py

@@ -7,10 +7,8 @@ import tempfile
 import logging
 from simple_salesforce import Salesforce
 import plotly.express as px
-import plotly.graph_objects as go
-from plotly.subplots import make_subplots
 
-# Set up logging
+# Logging setup
 logging.basicConfig(level=logging.DEBUG)
 logger = logging.getLogger(__name__)
 
@@ -21,24 +19,19 @@ SALESFORCE_SECURITY_TOKEN = "CaZSEwVmB3EIAiV6G8ukdDp0"
 
 # Connect to Salesforce
 sf = Salesforce(username=SALESFORCE_USERNAME, password=SALESFORCE_PASSWORD, security_token=SALESFORCE_SECURITY_TOKEN)
-logger.debug("Successfully connected to Salesforce.")
+logger.debug("Connected to Salesforce.")
 
 def find_salesforce_project(project_name, sf):
-    """Find an existing Project__c record by name and return its ID."""
     query = f"SELECT Id FROM Project__c WHERE Name = '{project_name}' LIMIT 1"
     result = sf.query(query)
     if result['totalSize'] > 0:
-        project_id = result['records'][0]['Id']
-        logger.debug(f"Found Project__c with Name: {project_name}, ID: {project_id}")
-        return project_id
-    logger.debug(f"No Project__c found with Name: {project_name}")
+        return result['records'][0]['Id']
     return None
 
 def insert_reconciliation_to_salesforce(df, sf):
-    """Inserts reconciliation records into Salesforce, linking to existing Project__c records if possible."""
     inserted_count = 0
     project_cache = {}
-    
+
     for index, row in df.iterrows():
         project_id = None
         if 'Project_ID' in df.columns and pd.notna(row['Project_ID']):
@@ -49,8 +42,8 @@ def insert_reconciliation_to_salesforce(df, sf):
                 project_id = find_salesforce_project(project_name, sf)
                 if project_id:
                     project_cache[project_name] = project_id
-        
-        reconciliation_record = {
+
+        record = {
             'Material_Type__c': row['Material_Type'],
             'Planned_Quantity__c': row['Planned_Quantity'],
             'Received_Quantity__c': row['Received_Quantity'],
@@ -59,191 +52,145 @@ def insert_reconciliation_to_salesforce(df, sf):
             'Reconciliation_Status__c': row['Reconciliation_Status']
         }
         if project_id:
-            reconciliation_record['Project_ID__c'] = project_id
-        
-        logger.debug(f"Inserting record: {reconciliation_record}")
-        sf.Material_Reconciliation_Record__c.create(reconciliation_record)
+            record['Project_ID__c'] = project_id
+
+        sf.Material_Reconciliation_Record__c.create(record)
         inserted_count += 1
-    
-    logger.debug(f"Inserted {inserted_count} of {len(df)} records successfully")
+
     return f"Inserted {inserted_count} records into Salesforce"
 
 def generate_suggestion(row):
-    """Generate AI suggestions based on reconciliation data."""
-    if row['Anomaly'] == -1 or abs(row['Deviation']) > 50:
-        if row['Balance_Quantity'] < 0:
-            return f"Urgent: Reorder {abs(row['Balance_Quantity'])} units of {row['Material_Type']}."
-        elif row['Deviation'] > 50:
-            return f"Excess: Reduce future orders of {row['Material_Type']} by {row['Balance_Quantity'] - row['Planned_Quantity']} units."
-        elif row['Deviation'] < -50:
-            return f"Shortage: Order {abs(row['Used_Quantity'] - row['Planned_Quantity'])} more units of {row['Material_Type']}."
+    if row['Anomaly'] == -1 or abs(row['Deviation']) > 15:
+        if row['Deviation'] < -15:
+            shortage = abs(row['Planned_Quantity'] - row['Used_Quantity'])
+            return f"Shortage: Order {shortage:.0f} more units of {row['Material_Type']}."
+        elif row['Deviation'] > 15:
+            excess = row['Used_Quantity'] - row['Planned_Quantity']
+            return f"Excess: Reduce future orders by {excess:.0f} units of {row['Material_Type']}."
     return "No action needed."
 
 def reconcile_materials(csv_file):
-    """Process CSV and reconcile materials, inserting results into Salesforce."""
-    logger.debug("Starting reconcile_materials function")
-    logger.debug(f"csv_file type: {type(csv_file)}")
-
-    # Read CSV based on input type
     if isinstance(csv_file, str):
-        logger.debug(f"Reading CSV from path: {csv_file}")
         df = pd.read_csv(csv_file)
     elif hasattr(csv_file, 'name'):
-        logger.debug(f"Reading CSV from file object with name: {csv_file.name}")
         df = pd.read_csv(csv_file.name)
     else:
-        logger.debug("Reading CSV from file object directly")
         csv_file.seek(0)
         df = pd.read_csv(csv_file)
 
-    logger.debug(f"CSV read successfully. Columns: {df.columns.tolist()}")
-
-    # Validate CSV columns (Project_ID is optional)
-    column_mapping = {
-        'Material_Type': 'Material_Type',
-        'Planned_Quantity': ['Planned_Quantity', 'Planned_Qty'],
-        'Received_Quantity': ['Received_Quantity', 'Received_Qty'],
-        'Used_Quantity': ['Used_Quantity', 'Used_Qty']
+    # Rename possible variant columns
+    col_map = {
+        'Planned_Qty': 'Planned_Quantity',
+        'Received_Qty': 'Received_Quantity',
+        'Used_Qty': 'Used_Quantity'
     }
-    
-    # Check for required columns and rename if necessary
-    for internal_name, possible_names in column_mapping.items():
-        if isinstance(possible_names, list):
-            found = False
-            for name in possible_names:
-                if name in df.columns:
-                    df.rename(columns={name: internal_name}, inplace=True)
-                    found = True
-                    break
-            if not found:
-                raise ValueError(f"Missing required column: {internal_name}")
-        else:
-            if possible_names not in df.columns:
-                raise ValueError(f"Missing required column: {possible_names}")
-
-    # Validate data types
+    df.rename(columns=col_map, inplace=True)
+
+    # Check required columns
+    required = ['Material_Type', 'Planned_Quantity', 'Received_Quantity', 'Used_Quantity']
+    for col in required:
+        if col not in df.columns:
+            raise ValueError(f"Missing column: {col}")
+
     for col in ['Planned_Quantity', 'Received_Quantity', 'Used_Quantity']:
         df[col] = pd.to_numeric(df[col], errors='coerce')
         if df[col].isna().any():
-            raise ValueError(f"Column '{col}' contains non-numeric values or empty cells.")
+            raise ValueError(f"Non-numeric or missing values in '{col}'")
 
-    # Calculate balance
+    # Compute balance and deviation
     df['Balance_Quantity'] = df['Received_Quantity'] - df['Used_Quantity']
-    logger.debug("Balance_Quantity calculated")
-
-    # Calculate deviation
     df['Deviation'] = df.apply(
-        lambda row: ((row['Balance_Quantity'] - row['Planned_Quantity']) / row['Planned_Quantity'])
-        if row['Planned_Quantity'] != 0 else 0, axis=1
+        lambda row: ((row['Used_Quantity'] - row['Planned_Quantity']) / row['Planned_Quantity']) * 100
+        if row['Planned_Quantity'] != 0 else 0,
+        axis=1
     )
-    logger.debug("Deviation calculated")
 
-    # Anomaly detection with forced anomaly for large deviations
-    iso_forest = IsolationForest(contamination=0.1, random_state=42)
+    # Anomaly detection
     features = df[['Planned_Quantity', 'Received_Quantity', 'Used_Quantity', 'Deviation']]
+    iso_forest = IsolationForest(contamination=0.1, random_state=42)
     df['Anomaly'] = iso_forest.fit_predict(features)
-    df.loc[df['Deviation'].abs() > 50, 'Anomaly'] = -1
-    logger.debug("Anomaly detection completed")
 
-    # Generate suggestions
+    # Enforce anomaly for deviation beyond 15%
+    df.loc[df['Deviation'].abs() > 15, 'Anomaly'] = -1
+
+    # AI Suggestions & Status
     df['AI_Suggestion'] = df.apply(generate_suggestion, axis=1)
     df['Reconciliation_Status'] = df.apply(
-        lambda row: 'Flagged' if row['Anomaly'] == -1 or abs(row['Deviation']) > 50 else 'Complete', axis=1
+        lambda row: 'Flagged' if row['Anomaly'] == -1 or abs(row['Deviation']) > 15 else 'Complete',
+        axis=1
     )
-    logger.debug("Suggestions and status generated")
 
-    # Insert records into Salesforce
+    # Insert into Salesforce
     salesforce_result = insert_reconciliation_to_salesforce(df, sf)
-    logger.debug(f"Salesforce insert result: {salesforce_result}")
 
-    # Generate text output
-    text_output = "Material Reconciliation Results\n"
-    text_output += "=============================\n\n"
-    text_output += salesforce_result + "\n\n"
-    text_output += "Detailed Records:\n"
-    for index, row in df.iterrows():
-        text_output += f"Record {index + 1}:\n"
+    # Output summary
+    output_text = f"Material Reconciliation Results\n=============================\n\n"
+    output_text += f"{salesforce_result}\n\nDetailed Records:\n"
+    for i, row in df.iterrows():
+        output_text += f"Record {i + 1}:\n"
         if 'Project_ID' in df.columns and pd.notna(row['Project_ID']):
-            text_output += f"  Project ID: {row['Project_ID']}\n"
-        text_output += f"  Material Type: {row['Material_Type']}\n"
-        text_output += f"  Planned Quantity: {row['Planned_Quantity']}\n"
-        text_output += f"  Received Quantity: {row['Received_Quantity']}\n"
-        text_output += f"  Used Quantity: {row['Used_Quantity']}\n"
-        text_output += f"  Balance Quantity: {row['Balance_Quantity']}\n"
-        text_output += f"  Deviation: {row['Deviation']:.2f}%\n"
-        text_output += f"  Anomaly: {'Yes' if row['Anomaly'] == -1 else 'No'}\n"
-        text_output += f"  AI Suggestion: {row['AI_Suggestion']}\n"
-        text_output += f"  Reconciliation Status: {row['Reconciliation_Status']}\n"
-        text_output += "-----------------------------\n"
-
-    # Save results to a temporary file
-    with tempfile.NamedTemporaryFile(delete=False, suffix='.csv') as tmp_file:
-        output_file = tmp_file.name
+            output_text += f"  Project ID: {row['Project_ID']}\n"
+        output_text += f"  Material Type: {row['Material_Type']}\n"
+        output_text += f"  Planned Quantity: {row['Planned_Quantity']}\n"
+        output_text += f"  Received Quantity: {row['Received_Quantity']}\n"
+        output_text += f"  Used Quantity: {row['Used_Quantity']}\n"
+        output_text += f"  Balance Quantity: {row['Balance_Quantity']}\n"
+        output_text += f"  Deviation: {row['Deviation']:.2f}%\n"
+        output_text += f"  Anomaly: {'Yes' if row['Anomaly'] == -1 else 'No'}\n"
+        output_text += f"  AI Suggestion: {row['AI_Suggestion']}\n"
+        output_text += f"  Reconciliation Status: {row['Reconciliation_Status']}\n"
+        output_text += "-----------------------------\n"
+
+    # Save file
+    with tempfile.NamedTemporaryFile(delete=False, suffix='.csv') as tmp:
+        output_file = tmp.name
         df.to_csv(output_file, index=False)
-        logger.debug(f"Output CSV saved to: {output_file}")
 
-    # Create visualizations
-    # Bar chart for Deviation by Material_Type
+    # Charts
     bar_fig = px.bar(
-        df,
-        x='Material_Type',
-        y='Deviation',
+        df, x='Material_Type', y='Deviation',
         color='Reconciliation_Status',
         title='Deviation by Material Type',
         labels={'Deviation': 'Deviation (%)'},
         color_discrete_map={'Flagged': '#FF4B4B', 'Complete': '#36A2EB'}
     )
-    bar_fig.update_layout(xaxis_title="Material Type", yaxis_title="Deviation (%)")
 
-    # Pie chart for Reconciliation Status
-    status_counts = df['Reconciliation_Status'].value_counts().reset_index()
-    status_counts.columns = ['Reconciliation_Status', 'Count']
+    pie_data = df['Reconciliation_Status'].value_counts().reset_index()
+    pie_data.columns = ['Reconciliation_Status', 'Count']
     pie_fig = px.pie(
-        status_counts,
-        names='Reconciliation_Status',
-        values='Count',
+        pie_data, names='Reconciliation_Status', values='Count',
         title='Reconciliation Status Distribution',
         color_discrete_map={'Flagged': '#FF4B4B', 'Complete': '#36A2EB'}
     )
 
-    # AI Suggestions summary
     ai_summary = "\n".join([f"{row['Material_Type']}: {row['AI_Suggestion']}" for _, row in df.iterrows()])
+    return output_file, output_text, df, bar_fig, pie_fig, ai_summary
 
-    return output_file, text_output, df, bar_fig, pie_fig, ai_summary
-
-# Gradio interface using Blocks
-logger.debug("Setting up Gradio Blocks interface")
+# Gradio UI
 with gr.Blocks(css='button:has(span:contains("Share via Link")) { display: none !important; }') as interface:
     gr.Markdown("# Material Reconciliation Dashboard")
     with gr.Row():
         with gr.Column(scale=1):
-            gr.Markdown("## Upload CSV File")
             csv_input = gr.File(label="Upload CSV", file_types=[".csv"])
             submit_button = gr.Button("Reconcile Materials")
         with gr.Column(scale=2):
-            gr.Markdown("## Reconciliation Results")
             output_text = gr.Textbox(label="Detailed Results", lines=20)
     with gr.Row():
         with gr.Column(scale=2):
-            gr.Markdown("## Data Table")
             output_table = gr.Dataframe(label="Reconciled Data")
         with gr.Column(scale=1):
-            gr.Markdown("## AI Suggestions")
             ai_summary_output = gr.Textbox(label="AI Suggestions Summary")
     with gr.Row():
         with gr.Column(scale=1):
-            gr.Markdown("## Deviation by Material")
             bar_plot = gr.Plot(label="Deviation Plot")
         with gr.Column(scale=1):
-            gr.Markdown("## Reconciliation Status Distribution")
             pie_plot = gr.Plot(label="Status Distribution")
     output_file = gr.File(label="Download Reconciled CSV")
-    
+
     submit_button.click(
         fn=reconcile_materials,
         inputs=csv_input,
         outputs=[output_file, output_text, output_table, bar_plot, pie_plot, ai_summary_output]
     )
 
-logger.debug("Launching Gradio Blocks interface")
 interface.launch()