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| import pandas as pd | |
| import requests | |
| import gradio as gr | |
| import logging | |
| from uuid import uuid4 | |
| from simple_salesforce import Salesforce | |
| # Configure logging AT THE TOP before usage | |
| logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') | |
| logger = logging.getLogger(__name__) | |
| SALESFORCE_USERNAME = "vaneshdevarapalli866@agentforce.com" | |
| SALESFORCE_PASSWORD = "vanesh@331" | |
| SALESFORCE_SECURITY_TOKEN = "VRUVbBOdG0s9Q4xy0W6DB1Y6b" | |
| def connect_to_salesforce(): | |
| try: | |
| sf_instance = Salesforce( | |
| username=SALESFORCE_USERNAME, | |
| password=SALESFORCE_PASSWORD, | |
| security_token=SALESFORCE_SECURITY_TOKEN, | |
| domain="login" | |
| ) | |
| logger.info("Connected to Salesforce successfully.") | |
| return sf_instance | |
| except Exception as e: | |
| logger.error(f"Salesforce connection failed: {e}") | |
| raise | |
| sf = connect_to_salesforce() | |
| # FastAPI endpoint | |
| FASTAPI_ENDPOINT = "http://localhost:8000/predict" | |
| # Load and validate dataset | |
| def load_dataset(file_path="equipment_data.csv"): | |
| try: | |
| df = pd.read_csv(file_path) | |
| required_columns = ["Equipment_ID__c", "Equipment_Type__c", "Usage_Hours__c", "Idle_Hours__c"] | |
| optional_columns = ["Movement_Frequency__c", "Cost_Per_Hour__c"] | |
| missing_required = [col for col in required_columns if col not in df.columns] | |
| if missing_required: | |
| logger.error(f"Missing required columns: {missing_required}") | |
| return None | |
| # Ensure numeric columns are properly typed | |
| numeric_cols = [col for col in required_columns + optional_columns if col in df.columns and col != "Equipment_ID__c" and col != "Equipment_Type__c"] | |
| for col in numeric_cols: | |
| df[col] = pd.to_numeric(df[col], errors='coerce') | |
| # Fill NaN values in numeric columns with 0 | |
| if df[numeric_cols].isnull().any().any(): | |
| logger.warning("NaN values detected in numeric columns, filling with 0") | |
| df[numeric_cols] = df[numeric_cols].fillna(0) | |
| # Add missing optional columns with default value 0 | |
| for col in optional_columns: | |
| if col not in df.columns: | |
| logger.warning(f"Optional column {col} missing, adding with default value 0") | |
| df[col] = 0.0 | |
| return df | |
| except FileNotFoundError: | |
| logger.error(f"Dataset file {file_path} not found") | |
| return None | |
| except Exception as e: | |
| logger.error(f"Failed to load dataset: {e}") | |
| return None | |
| # Load dataset | |
| df = load_dataset() | |
| equipment_types = sorted(df["Equipment_Type__c"].dropna().unique().tolist()) if df is not None and not df.empty else ["No Equipment Types"] | |
| suggestion_types = ["Move", "Pause Rent", "Repair", "Replace"] | |
| # Call FastAPI endpoint | |
| def call_model(row): | |
| try: | |
| # Prepare inputs | |
| inputs = { | |
| "usage_hours": float(row["Usage_Hours__c"]), | |
| "idle_hours": float(row["Idle_Hours__c"]), | |
| "movement_frequency": float(row.get("Movement_Frequency__c", 0.0)), | |
| "cost_per_hour": float(row.get("Cost_Per_Hour__c", 0.0)) | |
| } | |
| logger.info(f"Sending request with inputs: {inputs}") # Log inputs | |
| response = requests.post(FASTAPI_ENDPOINT, json=inputs, timeout=10) | |
| response.raise_for_status() | |
| result = response.json() | |
| # Log the API response | |
| logger.info(f"Received response: {result}") | |
| suggestion = result.get("suggestion", "Replace") | |
| confidence = float(result.get("confidence", 0.5)) | |
| if suggestion not in suggestion_types: | |
| logger.warning(f"Invalid suggestion: {suggestion}, defaulting to Replace") | |
| suggestion = "Replace" | |
| if not (0 <= confidence <= 1): | |
| logger.warning(f"Invalid confidence: {confidence}, defaulting to 0.5") | |
| confidence = 0.5 | |
| return suggestion, confidence | |
| except (requests.RequestException, ValueError, KeyError) as e: | |
| logger.error(f"API call failed: {e}") | |
| return "Replace", 0.5 | |
| # Filter equipment and generate display | |
| def filter_equipment(equipment_type, suggestion): | |
| if not equipment_type or not suggestion or df is None or df.empty: | |
| return "No data available or invalid filters selected.", "" | |
| try: | |
| filtered = df[df["Equipment_Type__c"].str.lower() == equipment_type.lower()].copy() | |
| if filtered.empty: | |
| return f"No equipment found for type: {equipment_type}.", "" | |
| # Apply model predictions | |
| filtered["AI_Suggestion__c"] = None | |
| filtered["Suggestion_Confidence__c"] = 0.0 | |
| for idx, row in filtered.iterrows(): | |
| s, conf = call_model(row) | |
| filtered.at[idx, "AI_Suggestion__c"] = s | |
| filtered.at[idx, "Suggestion_Confidence__c"] = conf | |
| # Filter by suggestion | |
| filtered = filtered[filtered["AI_Suggestion__c"].str.lower() == suggestion.lower()] | |
| if filtered.empty: | |
| return f"No equipment with suggestion '{suggestion}' for type '{equipment_type}'.", "" | |
| # Generate display cards | |
| cards = [ | |
| f"ID: {row['Equipment_ID__c']} | Usage: {row['Usage_Hours__c']:.2f} hrs | " | |
| f"Idle: {row['Idle_Hours__c']:.2f} hrs | Move Freq: {row['Movement_Frequency__c']:.2f} | " | |
| f"Cost/hr: ${row['Cost_Per_Hour__c']:.2f} | AI: {row['AI_Suggestion__c']} " | |
| f"({row['Suggestion_Confidence__c']:.2%})" | |
| for _, row in filtered.iterrows() | |
| ] | |
| confidences = [f"{row['Equipment_ID__c']}: {row['Suggestion_Confidence__c']:.2%}" | |
| for _, row in filtered.iterrows()] | |
| return "\n\n".join(cards), "\n".join(confidences) | |
| except Exception as e: | |
| logger.error(f"Error in filter_equipment: {e}") | |
| return "An error occurred while filtering equipment.", "" | |
| # Export filtered data to CSV | |
| def export_csv(equipment_type, suggestion): | |
| if not equipment_type or not suggestion or df is None or df.empty: | |
| return None | |
| try: | |
| filtered = df[df["Equipment_Type__c"].str.lower() == equipment_type.lower()].copy() | |
| if filtered.empty: | |
| return None | |
| # Apply model predictions | |
| filtered["AI_Suggestion__c"] = None | |
| filtered["Suggestion_Confidence__c"] = 0.0 | |
| for idx, row in filtered.iterrows(): | |
| s, conf = call_model(row) | |
| filtered.at[idx, "AI_Suggestion__c"] = s | |
| filtered.at[idx, "Suggestion_Confidence__c"] = conf | |
| # Filter by suggestion | |
| filtered = filtered[filtered["AI_Suggestion__c"].str.lower() == suggestion.lower()] | |
| if filtered.empty: | |
| return None | |
| # Export to CSV | |
| filename = f"filtered_equipment_{uuid4().hex[:8]}.csv" | |
| filtered.to_csv(filename, index=False) | |
| return filename | |
| except Exception as e: | |
| logger.error(f"Error in export_csv: {e}") | |
| return None | |
| # Build Gradio UI | |
| with gr.Blocks(theme=gr.themes.Soft()) as app: | |
| gr.Markdown("# Equipment Utilization Dashboard") | |
| gr.Markdown("Filter equipment by type and AI suggestion to optimize utilization.") | |
| with gr.Row(): | |
| etype = gr.Dropdown(choices=equipment_types, label="Equipment Type", | |
| value=equipment_types[0] if equipment_types else None) | |
| suggestion = gr.Dropdown(choices=suggestion_types, label="Suggestion Type", value=suggestion_types[0]) | |
| results = gr.Textbox(label="Equipment Details", lines=10, placeholder="Select equipment type and suggestion...") | |
| confidences = gr.Textbox(label="Confidence Scores", lines=5, placeholder="Confidence scores will appear here...") | |
| with gr.Row(): | |
| export_button = gr.Button("Export to CSV", variant="primary") | |
| file_output = gr.File(label="Download CSV") | |
| # Define interactions | |
| etype.change(fn=filter_equipment, inputs=[etype, suggestion], outputs=[results, confidences]) | |
| suggestion.change(fn=filter_equipment, inputs=[etype, suggestion], outputs=[results, confidences]) | |
| export_button.click( | |
| fn=export_csv, | |
| inputs=[etype, suggestion], | |
| outputs=file_output | |
| ) | |
| # Launch the app | |
| if __name__ == "__main__": | |
| try: | |
| app.launch(debug=False, share=False) | |
| except Exception as e: | |
| logger.error(f"Failed to launch Gradio app: {e}") | |