Update app.py

app.py

@@ -1,107 +1,161 @@
 import pandas as pd
-import logging
+import requests
 import gradio as gr
+import logging
+from uuid import uuid4
 from simple_salesforce import Salesforce
 
-# Configure logging
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-# Salesforce credentials
+# Salesforce credentials - replace with yours securely
 SALESFORCE_USERNAME = "vaneshdevarapalli866@agentforce.com"
 SALESFORCE_PASSWORD = "vanesh@331"
 SALESFORCE_SECURITY_TOKEN = "VRUVbBOdG0s9Q4xy0W6DB1Y6b"
 
+# Configure logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
 # Connect to Salesforce
 def connect_to_salesforce():
     try:
-        sf = Salesforce(
+        sf_instance = Salesforce(
             username=SALESFORCE_USERNAME,
             password=SALESFORCE_PASSWORD,
             security_token=SALESFORCE_SECURITY_TOKEN,
             domain="login"
         )
-        logger.info("Connected to Salesforce")
-        return sf
+        logger.info("Connected to Salesforce successfully.")
+        return sf_instance
     except Exception as e:
-        logger.error(f"Salesforce connection error: {e}")
+        logger.error(f"Salesforce connection failed: {e}")
         raise
 
 sf = connect_to_salesforce()
 
-# Load your equipment CSV data here
-df = pd.read_csv("equipment_data.csv")
-equipment_types = sorted(df["Equipment_Type__c"].dropna().unique().tolist())
-suggestion_types = ["Move", "Pause Rent", "Repair", "Replace"]
+# Load 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
+
+        # Convert numeric columns properly
+        numeric_cols = [col for col in required_columns + optional_columns if col in df.columns and col not in ["Equipment_ID__c", "Equipment_Type__c"]]
+        for col in numeric_cols:
+            df[col] = pd.to_numeric(df[col], errors='coerce')
+
+        # Fill NaNs with 0 for numeric columns
+        if df[numeric_cols].isnull().any().any():
+            logger.warning("NaN detected in numeric columns, filling with 0")
+            df[numeric_cols] = df[numeric_cols].fillna(0)
+
+        # Add missing optional columns
+        for col in optional_columns:
+            if col not in df.columns:
+                df[col] = 0.0
+        return df
+    except Exception as e:
+        logger.error(f"Failed to load dataset: {e}")
+        return None
 
-# Dummy AI model that returns Move with 85% confidence
-def dummy_ai_model(row):
-    return "Move", 85
+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"]
 
-# Filter equipment and generate details + confidence output
+# Dummy AI suggestion function (Replace with your actual AI integration)
+def call_model(row):
+    # Example logic for AI suggestion (random or based on usage hours)
+    usage = float(row.get("Usage_Hours__c", 0))
+    if usage > 8:
+        return "Pause Rent", 0.9
+    elif usage > 4:
+        return "Move", 0.8
+    else:
+        return "Repair", 0.7
+
+# Filter equipment by type and suggestion
 def filter_equipment(equipment_type, suggestion):
-    if not equipment_type or not suggestion:
-        return "", ""
-    filtered = df[df["Equipment_Type__c"] == equipment_type].copy()
-    if filtered.empty:
-        return "No equipment found.", ""
-
-    filtered["AI_Suggestion__c"] = None
-    filtered["Suggestion_Confidence__c"] = 0
-    for idx, row in filtered.iterrows():
-        s, conf = dummy_ai_model(row)
-        filtered.at[idx, "AI_Suggestion__c"] = s
-        filtered.at[idx, "Suggestion_Confidence__c"] = conf
-    filtered = filtered[filtered["AI_Suggestion__c"] == suggestion]
-    if filtered.empty:
-        return "No equipment matching suggestion.", ""
-
-    details_list = []
-    confidence_list = []
-    for _, row in filtered.iterrows():
-        details_list.append(
-            f"ID: {row['Equipment_ID__c']} | Usage: {row['Usage_Hours__c']} hrs | Idle: {row['Idle_Hours__c']} hrs | AI Suggestion: {row['AI_Suggestion__c']}"
-        )
-        confidence_list.append(f"{row['Equipment_ID__c']}: {row['Suggestion_Confidence__c']}%")
-    return "\n\n".join(details_list), "\n".join(confidence_list)
+    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 dummy AI 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 text for equipment and confidence
+        cards = [
+            f"ID: {row['Equipment_ID__c']} | Usage: {row['Usage_Hours__c']:.2f} hrs | "
+            f"Idle: {row['Idle_Hours__c']:.2f} hrs | AI Suggestion: {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 filtering equipment: {e}")
+        return "An error occurred while filtering equipment.", ""
 
-# Export filtered data as CSV file
+# Export filtered results to CSV file
 def export_csv(equipment_type, suggestion):
-    filtered = df[df["Equipment_Type__c"] == equipment_type].copy()
-    if filtered.empty:
+    if not equipment_type or not suggestion or df is None or df.empty:
         return None
-    filtered["AI_Suggestion__c"] = None
-    filtered["Suggestion_Confidence__c"] = 0
-    for idx, row in filtered.iterrows():
-        s, conf = dummy_ai_model(row)
-        filtered.at[idx, "AI_Suggestion__c"] = s
-        filtered.at[idx, "Suggestion_Confidence__c"] = conf
-    filtered = filtered[filtered["AI_Suggestion__c"] == suggestion]
-    if filtered.empty:
+    try:
+        filtered = df[df["Equipment_Type__c"].str.lower() == equipment_type.lower()].copy()
+        if filtered.empty:
+            return None
+        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
+        filtered = filtered[filtered["AI_Suggestion__c"].str.lower() == suggestion.lower()]
+        if filtered.empty:
+            return None
+        filename = f"filtered_equipment_{uuid4().hex[:8]}.csv"
+        filtered.to_csv(filename, index=False)
+        return filename
+    except Exception as e:
+        logger.error(f"Error exporting CSV: {e}")
         return None
-    csv_path = "filtered_equipment.csv"
-    filtered.to_csv(csv_path, index=False)
-    return csv_path
 
-# Build Gradio UI
-with gr.Blocks(theme=gr.themes.Dark()) as app:
+# Build Gradio UI without theme to avoid errors
+with gr.Blocks() 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")
-        suggestion = gr.Dropdown(choices=suggestion_types, label="Suggestion Type")
+        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])
 
-    details = gr.Textbox(label="Equipment Details", lines=8)
-    confidence = gr.Textbox(label="Confidence Scores", lines=5)
+    details = gr.Textbox(label="Equipment Details", lines=8, placeholder="Select equipment type and suggestion...")
+    confidence = gr.Textbox(label="Confidence Scores", lines=5, placeholder="Confidence scores will appear here...")
 
-    export_btn = gr.Button("Export to CSV")
+    export_btn = gr.Button("Export to CSV", variant="primary")
     csv_file = gr.File(label="Download CSV")
 
-    # Callbacks
+    # Define interactions
     etype.change(fn=filter_equipment, inputs=[etype, suggestion], outputs=[details, confidence])
     suggestion.change(fn=filter_equipment, inputs=[etype, suggestion], outputs=[details, confidence])
     export_btn.click(fn=export_csv, inputs=[etype, suggestion], outputs=csv_file)
 
 if __name__ == "__main__":
-    app.launch()
+    app.launch(share=False)