Update app.py

app.py

@@ -1,17 +1,13 @@
+import pandas as pd
 import logging
-import sys
 import gradio as gr
 from simple_salesforce import Salesforce
 
 # Configure logging
-logging.basicConfig(
-    level=logging.INFO,
-    format="%(asctime)s - %(levelname)s - %(message)s",
-    handlers=[logging.StreamHandler(sys.stdout)],
-)
+logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
-# Your Salesforce credentials
+# Salesforce credentials
 SALESFORCE_USERNAME = "vaneshdevarapalli866@agentforce.com"
 SALESFORCE_PASSWORD = "vanesh@331"
 SALESFORCE_SECURITY_TOKEN = "VRUVbBOdG0s9Q4xy0W6DB1Y6b"
@@ -23,126 +19,89 @@ def connect_to_salesforce():
             username=SALESFORCE_USERNAME,
             password=SALESFORCE_PASSWORD,
             security_token=SALESFORCE_SECURITY_TOKEN,
-            domain="login",
+            domain="login"
         )
-        logger.info("Connected to Salesforce successfully.")
+        logger.info("Connected to Salesforce")
         return sf
     except Exception as e:
-        logger.error(f"Failed to connect to Salesforce: {e}")
+        logger.error(f"Salesforce connection error: {e}")
         raise
 
 sf = connect_to_salesforce()
 
-# Lookup Salesforce record ID by object API name and record Name (string)
-def lookup_record_id(object_api_name, record_name):
-    try:
-        query = f"SELECT Id FROM {object_api_name} WHERE Name = '{record_name}' LIMIT 1"
-        result = sf.query(query)
-        records = result.get("records", [])
-        if records:
-            record_id = records[0]["Id"]
-            logger.info(f"Found {object_api_name} ID for '{record_name}': {record_id}")
-            return record_id
-        else:
-            logger.warning(f"No {object_api_name} record found with Name '{record_name}'")
-            return None
-    except Exception as e:
-        logger.error(f"Error looking up {object_api_name} ID: {e}")
+# 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"]
+
+# Dummy AI model that returns Move with 85% confidence
+def dummy_ai_model(row):
+    return "Move", 85
+
+# Filter equipment and generate details + confidence output
+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)
+
+# Export filtered data as CSV file
+def export_csv(equipment_type, suggestion):
+    filtered = df[df["Equipment_Type__c"] == equipment_type].copy()
+    if filtered.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:
         return None
+    csv_path = "filtered_equipment.csv"
+    filtered.to_csv(csv_path, index=False)
+    return csv_path
 
-# Dummy AI model (replace with your real AI logic or API call)
-def dummy_ai_model(usage_hours, idle_hours):
-    if usage_hours > 5:
-        suggestion = "Move"
-        confidence = 90.0
-        utilization_score = 85.0
-    else:
-        suggestion = "Pause Rent"
-        confidence = 75.0
-        utilization_score = 60.0
-    return suggestion, confidence, utilization_score
-
-def process_equipment_utilization(
-    equipment_name,
-    project_name,
-    usage_hours,
-    idle_hours,
-    report_link,
-    last_maintenance_date,
-):
-    equipment_id = lookup_record_id("Equipment__c", equipment_name)
-    project_id = lookup_record_id("Project__c", project_name)
-
-    if not equipment_id or not project_id:
-        return {
-            "status": "Error",
-            "message": "Could not find Salesforce record IDs for Equipment or Project. Please check names.",
-        }
-
-    ai_suggestion, suggestion_confidence, utilization_score = dummy_ai_model(
-        usage_hours, idle_hours
-    )
-
-    record = {
-        "Equipment_ID__c": equipment_id,
-        "Project_ID__c": project_id,
-        "Usage_Hours__c": usage_hours,
-        "Idle_Hours__c": idle_hours,
-        "AI_Suggestion__c": ai_suggestion,
-        "Suggestion_Confidence__c": suggestion_confidence,
-        "Utilization_Score__c": utilization_score,
-        "Report_Link__c": report_link,
-        "Last_Maintenance__c": last_maintenance_date,
-        "Dashboard_Flag__c": False,
-    }
+# Build Gradio UI
+with gr.Blocks(theme=gr.themes.Dark()) as app:
+    gr.Markdown("# Equipment Utilization Dashboard")
+    gr.Markdown("Filter equipment by type and AI suggestion to optimize utilization.")
 
-    try:
-        response = sf.Equipment_Utilization_Record__c.create(record)
-        logger.info(f"Created Salesforce record: {response}")
-        return {
-            "status": "Success",
-            "Salesforce_Record_ID": response.get("id"),
-            "AI_Suggestion": ai_suggestion,
-            "Suggestion_Confidence": suggestion_confidence,
-            "Utilization_Score": utilization_score,
-        }
-    except Exception as e:
-        logger.error(f"Failed to create Salesforce record: {e}")
-        return {"status": "Error", "message": str(e)}
-
-# Gradio UI function
-def gradio_submit(
-    equipment_name,
-    project_name,
-    usage_hours,
-    idle_hours,
-    report_link,
-    last_maintenance_date,
-):
-    return process_equipment_utilization(
-        equipment_name,
-        project_name,
-        usage_hours,
-        idle_hours,
-        report_link,
-        last_maintenance_date,
-    )
-
-
-iface = gr.Interface(
-    fn=gradio_submit,
-    inputs=[
-        gr.Textbox(label="Equipment Name (Salesforce Name field)"),
-        gr.Textbox(label="Project Name (Salesforce Name field)"),
-        gr.Number(label="Usage Hours", value=0, minimum=0),
-        gr.Number(label="Idle Hours", value=0, minimum=0),
-        gr.Textbox(label="Report Link (URL)", value=""),
-        gr.Textbox(label="Last Maintenance Date (YYYY-MM-DD)", value=""),
-    ],
-    outputs=gr.JSON(label="Salesforce Insert Result"),
-    title="Equipment Utilization Record Creator with Name Lookup",
-    description="Input Equipment and Project names; the app looks up Salesforce IDs and creates the record.",
-)
+    with gr.Row():
+        etype = gr.Dropdown(choices=equipment_types, label="Equipment Type")
+        suggestion = gr.Dropdown(choices=suggestion_types, label="Suggestion Type")
+
+    details = gr.Textbox(label="Equipment Details", lines=8)
+    confidence = gr.Textbox(label="Confidence Scores", lines=5)
+
+    export_btn = gr.Button("Export to CSV")
+    csv_file = gr.File(label="Download CSV")
+
+    # Callbacks
+    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__":
-    iface.launch(share=False)
+    app.launch()