FINAL FIX: Ultra-minimal gr.Interface version to resolve all JSON schema errors

app.py

@@ -1,7 +1,6 @@
 import gradio as gr
 import pandas as pd
 from simple_salesforce import Salesforce
-import io
 from datetime import datetime
 import logging
 
@@ -9,13 +8,16 @@ import logging
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
-# Global variables to store connection
+# Global connection
 sf_connection = None
-available_objects = []
 
-def connect_to_salesforce(username, password, security_token, sandbox):
-    """Connect to Salesforce"""
-    global sf_connection, available_objects
+def salesforce_data_loader(username, password, security_token, sandbox, operation, csv_file):
+    """Main function that handles all Salesforce operations"""
+    global sf_connection
+    
+    # Step 1: Connect to Salesforce
+    if not username or not password or not security_token:
+        return "❌ Please provide username, password, and security token"
     
     try:
         domain = 'test' if sandbox else None
@@ -26,213 +28,133 @@ def connect_to_salesforce(username, password, security_token, sandbox):
             domain=domain
         )
         
-        # Test connection and get objects
-        common_objects = ['Account', 'Contact', 'Lead', 'Opportunity', 'Case']
-        available_objects = []
+        connection_msg = f"✅ Connected to Salesforce as {username}\n"
         
-        for obj_name in common_objects:
+        # Step 2: Handle file upload if provided
+        if csv_file is not None and operation != "connect_only":
             try:
-                obj = getattr(sf_connection, obj_name)
-                obj.describe()
-                available_objects.append(obj_name)
-            except:
-                continue
+                # Read the file
+                if csv_file.name.endswith('.csv'):
+                    df = pd.read_csv(csv_file.name)
+                elif csv_file.name.endswith(('.xlsx', '.xls')):
+                    df = pd.read_excel(csv_file.name)
+                else:
+                    return connection_msg + "❌ Please upload a CSV or Excel file"
+                
+                if df.empty:
+                    return connection_msg + "❌ The uploaded file is empty"
+                
+                # Clean data
+                records = df.to_dict('records')
+                cleaned_records = []
+                for record in records:
+                    cleaned_record = {k: v for k, v in record.items() if pd.notna(v)}
+                    cleaned_records.append(cleaned_record)
+                
+                # Determine object based on columns (simple heuristic)
+                columns = df.columns.str.lower()
+                if any(col in columns for col in ['firstname', 'lastname', 'email']):
+                    sf_object = sf_connection.Contact
+                    object_name = "Contact"
+                elif any(col in columns for col in ['company', 'name']):
+                    sf_object = sf_connection.Account  
+                    object_name = "Account"
+                else:
+                    sf_object = sf_connection.Lead
+                    object_name = "Lead"
+                
+                # Perform operation
+                if operation == "insert":
+                    result = sf_object.bulk.insert(cleaned_records)
+                elif operation == "update":
+                    result = sf_object.bulk.update(cleaned_records)
+                else:
+                    return connection_msg + "❌ Invalid operation"
+                
+                # Process results
+                success_count = sum(1 for r in result if r.get('success'))
+                error_count = len(result) - success_count
+                
+                upload_msg = f"\n📤 Upload Results:\n"
+                upload_msg += f"Object: {object_name}\n"
+                upload_msg += f"Total records: {len(records)}\n"
+                upload_msg += f"✅ Successful: {success_count}\n"
+                upload_msg += f"❌ Failed: {error_count}\n"
+                
+                return connection_msg + upload_msg
+                
+            except Exception as e:
+                return connection_msg + f"❌ Upload error: {str(e)}"
+        
+        # Step 3: Handle export operation
+        elif operation == "export":
+            try:
+                # Export some Account records as example
+                query = "SELECT Id, Name, Type, Phone, Website FROM Account LIMIT 100"
+                result = sf_connection.query_all(query)
+                records = result['records']
+                
+                if records:
+                    df = pd.DataFrame(records)
+                    if 'attributes' in df.columns:
+                        df = df.drop('attributes', axis=1)
+                    
+                    export_msg = f"\n📥 Export Results:\n"
+                    export_msg += f"Records exported: {len(records)}\n"
+                    export_msg += f"Fields: {', '.join(df.columns)}\n"
+                    export_msg += f"Sample data:\n{df.head().to_string()}"
+                    
+                    return connection_msg + export_msg
+                else:
+                    return connection_msg + "\n❌ No records found to export"
+                    
+            except Exception as e:
+                return connection_msg + f"\n❌ Export error: {str(e)}"
         
-        if not available_objects:
-            available_objects = ['Account', 'Contact', 'Lead']
+        else:
+            return connection_msg + "\n💡 Connection successful! Upload a file to insert/update data, or select 'export' to download data."
             
-        return f"✅ Successfully connected to Salesforce as {username}\nAvailable objects: {', '.join(available_objects)}"
-        
     except Exception as e:
-        sf_connection = None
-        available_objects = []
         error_msg = str(e)
-        
         if "INVALID_LOGIN" in error_msg:
             return "❌ Invalid credentials. Please check your username, password, and security token."
         elif "API_DISABLED_FOR_ORG" in error_msg:
-            return "❌ API access is disabled for your organization. Please contact your Salesforce admin."
+            return "❌ API access is disabled. Contact your Salesforce admin."
         elif "LOGIN_MUST_USE_SECURITY_TOKEN" in error_msg:
-            return "❌ Security token required. Please append your security token to your password."
+            return "❌ Security token required. Append it to your password."
         else:
             return f"❌ Connection failed: {error_msg}"
 
-def upload_data_to_salesforce(file, object_name, operation):
-    """Upload data to Salesforce"""
-    global sf_connection
-    
-    if not sf_connection:
-        return "❌ Please connect to Salesforce first", None
-        
-    if not file or not object_name:
-        return "❌ Please select a file and object", None
-        
-    try:
-        # Read the uploaded file
-        if file.name.endswith('.csv'):
-            df = pd.read_csv(file.name)
-        elif file.name.endswith(('.xlsx', '.xls')):
-            df = pd.read_excel(file.name)
-        else:
-            return "❌ Please upload a CSV or Excel file", None
-        
-        if df.empty:
-            return "❌ The uploaded file is empty", None
-        
-        # Get Salesforce object
-        sf_object = getattr(sf_connection, object_name)
-        
-        # Prepare data for upload
-        records = df.to_dict('records')
-        
-        # Clean data - remove NaN values
-        cleaned_records = []
-        for record in records:
-            cleaned_record = {k: v for k, v in record.items() if pd.notna(v)}
-            cleaned_records.append(cleaned_record)
-        
-        # Perform operation
-        if operation == "Insert":
-            result = sf_object.bulk.insert(cleaned_records)
-        elif operation == "Update":
-            result = sf_object.bulk.update(cleaned_records)
-        else:  # Upsert
-            return "❌ Upsert operation requires additional configuration", None
-        
-        # Process results
-        success_count = sum(1 for r in result if r.get('success'))
-        error_count = len(result) - success_count
-        
-        summary = f"✅ Operation completed!\n"
-        summary += f"📊 Total records: {len(records)}\n"
-        summary += f"✅ Successful: {success_count}\n"
-        summary += f"❌ Failed: {error_count}\n"
-        
-        # Create results file
-        results_df = pd.DataFrame(result)
-        results_file = f"salesforce_upload_results_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv"
-        results_df.to_csv(results_file, index=False)
-        
-        return summary, results_file
-        
-    except Exception as e:
-        logger.error(f"Upload error: {str(e)}")
-        return f"❌ Error: {str(e)}", None
-
-def export_data_from_salesforce(object_name, record_limit):
-    """Export data from Salesforce"""
-    global sf_connection
-    
-    if not sf_connection:
-        return "❌ Please connect to Salesforce first", None
-        
-    if not object_name:
-        return "❌ Please select an object", None
-        
-    try:
-        # Get object metadata to find some fields
-        obj = getattr(sf_connection, object_name)
-        metadata = obj.describe()
-        
-        # Get first 10 fields
-        fields = [field['name'] for field in metadata['fields'][:10]]
-        fields_str = ', '.join(fields)
-        
-        # Build and execute query
-        query = f"SELECT {fields_str} FROM {object_name} LIMIT {record_limit}"
-        result = sf_connection.query_all(query)
-        records = result['records']
-        
-        if not records:
-            return "❌ No records found", None
-        
-        # Convert to DataFrame and clean
-        df = pd.DataFrame(records)
-        if 'attributes' in df.columns:
-            df = df.drop('attributes', axis=1)
-        
-        # Create export file
-        export_file = f"salesforce_export_{object_name}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv"
-        df.to_csv(export_file, index=False)
-        
-        summary = f"✅ Export completed!\n"
-        summary += f"📊 Records exported: {len(records)}\n"
-        summary += f"📋 Fields: {', '.join(fields)}\n"
-        
-        return summary, export_file
-        
-    except Exception as e:
-        logger.error(f"Export error: {str(e)}")
-        return f"❌ Error: {str(e)}", None
-
-# Create the Gradio interface
-with gr.Blocks(title="Salesforce Data Loader", theme=gr.themes.Default()) as demo:
-    gr.Markdown("""
-    # 🚀 Salesforce Data Loader
-    A simple tool to upload and download data from Salesforce.
-    """)
-    
-    with gr.Tab("🔐 Connect"):
-        gr.Markdown("### Connect to Salesforce")
-        username = gr.Textbox(label="Username", placeholder="your.email@company.com")
-        password = gr.Textbox(label="Password", type="password")
-        security_token = gr.Textbox(label="Security Token", type="password")
-        sandbox = gr.Checkbox(label="Sandbox Environment")
-        connect_btn = gr.Button("🔗 Connect", variant="primary")
-        connection_status = gr.Textbox(label="Connection Status", interactive=False)
-        
-        connect_btn.click(
-            fn=connect_to_salesforce,
-            inputs=[username, password, security_token, sandbox],
-            outputs=[connection_status]
-        )
+# Create the interface
+demo = gr.Interface(
+    fn=salesforce_data_loader,
+    inputs=[
+        gr.Textbox(label="Username", placeholder="your.email@company.com"),
+        gr.Textbox(label="Password", type="password"),
+        gr.Textbox(label="Security Token", type="password"),
+        gr.Checkbox(label="Sandbox Environment"),
+        gr.Dropdown(
+            label="Operation", 
+            choices=["connect_only", "insert", "update", "export"],
+            value="connect_only"
+        ),
+        gr.File(label="CSV/Excel File (optional)", file_types=[".csv", ".xlsx", ".xls"])
+    ],
+    outputs=gr.Textbox(label="Results", lines=10),
+    title="🚀 Salesforce Data Loader",
+    description="""
+    **Simple Salesforce Data Management Tool**
     
-    with gr.Tab("📤 Upload"):
-        gr.Markdown("### Upload CSV/Excel data to Salesforce")
-        file_upload = gr.File(label="Upload CSV or Excel file")
-        upload_object = gr.Dropdown(
-            label="Salesforce Object",
-            choices=["Account", "Contact", "Lead", "Opportunity", "Case"],
-            value="Contact"
-        )
-        upload_operation = gr.Dropdown(
-            label="Operation",
-            choices=["Insert", "Update"],
-            value="Insert"
-        )
-        upload_btn = gr.Button("📤 Upload Data", variant="primary")
-        upload_results = gr.Textbox(label="Upload Results", interactive=False)
-        download_results = gr.File(label="Download Results")
-        
-        upload_btn.click(
-            fn=upload_data_to_salesforce,
-            inputs=[file_upload, upload_object, upload_operation],
-            outputs=[upload_results, download_results]
-        )
+    1. **Connect**: Enter your credentials and select 'connect_only'
+    2. **Upload**: Select 'insert' or 'update' and upload a CSV/Excel file  
+    3. **Export**: Select 'export' to download Account data
     
-    with gr.Tab("📥 Export"):
-        gr.Markdown("### Export data from Salesforce")
-        export_object = gr.Dropdown(
-            label="Salesforce Object",
-            choices=["Account", "Contact", "Lead", "Opportunity", "Case"],
-            value="Account"
-        )
-        export_limit = gr.Slider(
-            label="Record Limit",
-            minimum=100,
-            maximum=10000,
-            value=1000,
-            step=100
-        )
-        export_btn = gr.Button("📥 Export Data", variant="primary")
-        export_results = gr.Textbox(label="Export Results", interactive=False)
-        download_export = gr.File(label="Download Export")
-        
-        export_btn.click(
-            fn=export_data_from_salesforce,
-            inputs=[export_object, export_limit],
-            outputs=[export_results, download_export]
-        )
+    **Note**: For uploads, the tool auto-detects object type based on column names.
+    """,
+    examples=[
+        ["user@company.com", "password123", "token123", False, "connect_only", None],
+    ]
+)
 
 if __name__ == "__main__":
     demo.launch(server_name="0.0.0.0", server_port=7860)