app.py

import gradio as gr
import pandas as pd
import duckdb
from datasets import load_dataset
import openai
import os
from typing import Dict, List, Any
import json

class SALTAnalytics:
    def __init__(self):
        """Initialize SALT Analytics"""
        self.con = duckdb.connect(':memory:')
        self.data_loaded = False
        self.schema_info = ""
        self.openai_client = None
        
    def setup_openai(self, api_key: str):
        """Setup OpenAI client with API key"""
        try:
            self.openai_client = openai.OpenAI(api_key=api_key)
            return True
        except Exception as e:
            return False
        
    def load_salt_dataset(self):
        """Load SAP SALT dataset from Hugging Face into DuckDB"""
        if self.data_loaded:
            return "Dataset already loaded!"
            
        try:
            # Load dataset with error handling for HF Spaces
            dataset = load_dataset("SAP/SALT", "joined_table", split="train", streaming=False)
            df = dataset.to_pandas()
            
            # Sample data for demo if dataset is too large
            if len(df) > 100000:  # Limit for HF Spaces memory
                df = df.sample(n=50000, random_state=42)
            
            # Load into DuckDB
            self.con.execute("CREATE TABLE salt_data AS SELECT * FROM df")
            
            # Get schema information
            schema_result = self.con.execute("DESCRIBE salt_data").fetchall()
            self.schema_info = "\n".join([f"{col}: {col}" for col in schema_result])[1]
            
            self.data_loaded = True
            return f"✅ Successfully loaded {len(df)} records into DuckDB"
            
        except Exception as e:
            return f"❌ Error loading dataset: {str(e)}"
    
    def get_predefined_insights(self):
        """Generate predefined analytical insights"""
        if not self.data_loaded:
            return "Please load the dataset first"
            
        try:
            insights = {}
            
            # Sales Office Performance
            insights['Sales Office Performance'] = self.con.execute("""
                SELECT SALESOFFICE, 
                       COUNT(*) as total_orders,
                       COUNT(DISTINCT CUSTOMERID) as unique_customers
                FROM salt_data 
                GROUP BY SALESOFFICE
                ORDER BY total_orders DESC
                LIMIT 10
            """).fetchdf()
            
            # Payment Terms Distribution
            insights['Payment Terms Distribution'] = self.con.execute("""
                SELECT CUSTOMERPAYMENTTERMS,
                       COUNT(*) as frequency,
                       ROUND(COUNT(*) * 100.0 / SUM(COUNT(*)) OVER(), 2) as percentage
                FROM salt_data 
                GROUP BY CUSTOMERPAYMENTTERMS
                ORDER BY frequency DESC
            """).fetchdf()
            
            # Shipping Conditions Analysis
            insights['Shipping Conditions'] = self.con.execute("""
                SELECT SHIPPINGCONDITION,
                       COUNT(*) as order_count,
                       COUNT(DISTINCT PLANT) as plants_served
                FROM salt_data
                GROUP BY SHIPPINGCONDITION
                ORDER BY order_count DESC
            """).fetchdf()
            
            return insights
            
        except Exception as e:
            return f"Error generating insights: {str(e)}"
    
    def natural_language_query(self, question: str, api_key: str):
        """Convert natural language to SQL and execute"""
        if not self.data_loaded:
            return "Please load the dataset first"
            
        if not api_key:
            return "Please provide OpenAI API key"
            
        try:
            # Setup OpenAI client
            client = openai.OpenAI(api_key=api_key)
            
            prompt = f"""
            You are a SQL expert analyzing SAP SALT dataset. The database has a table called 'salt_data' with this schema:
            
            {self.schema_info}
            
            The SALT dataset contains SAP ERP sales order data. Key fields:
            - SALESOFFICE, SALESGROUP: Sales organization
            - CUSTOMERID: Customer identifier  
            - CUSTOMERPAYMENTTERMS: Payment terms (Net30, Net45, etc.)
            - SHIPPINGCONDITION, SHIPPINGPOINT: Shipping logistics
            - PLANT: Manufacturing location
            - HEADERINCOTERMSCLASSIFICATION, ITEMINCOTERMSCLASSIFICATION: Trade terms
            
            Convert this question to a DuckDB SQL query: "{question}"
            
            Return ONLY the SQL query, no explanation. Limit results to 20 rows.
            """
            
            response = client.chat.completions.create(
                model="gpt-4",
                messages=[{"role": "user", "content": prompt}],
                temperature=0.1
            )
            
            sql_query = response.choices.message.content.strip()
            
            # Clean SQL query - FIXED: Use single quotes to avoid syntax error
            if sql_query.startswith('```sql'):
                sql_query = sql_query[6:-3]
            elif sql_query.startswith('```
                sql_query = sql_query[3:-3]
            
            # Execute query
            result_df = self.con.execute(sql_query).fetchdf()
            
            # Get explanation
            explanation_prompt = f"""
            Question: {question}
            Results: {result_df.head(10).to_string()}
            
            Provide a clear business explanation of these SAP ERP results in 2-3 sentences.
            """
            
            explanation_response = client.chat.completions.create(
                model="gpt-4",
                messages=[{"role": "user", "content": explanation_prompt}],
                temperature=0.3
            )
            
            explanation = explanation_response.choices.message.content
            
            return f"**SQL Query:**\n```sql\n{sql_query}\n```
            
        except Exception as e:
            return f"Error: {str(e)}"

# Initialize analytics
analytics = SALTAnalytics()

def load_dataset_interface():
    """Interface for loading dataset"""
    result = analytics.load_salt_dataset()
    return result

def show_insights_interface():
    """Interface for showing insights"""
    insights = analytics.get_predefined_insights()
    
    if isinstance(insights, str):
        return insights
    
    output = "# 📊 SAP SALT Dataset Insights\n\n"
    
    for title, df in insights.items():
        output += f"## {title}\n\n"
        output += df.to_markdown(index=False)
        output += "\n\n---\n\n"
    
    return output

def qa_interface(question: str, api_key: str):
    """Interface for Q&A functionality"""
    if not question.strip():
        return "Please enter a question"
    
    result = analytics.natural_language_query(question, api_key)
    return result

# Sample questions for the interface
sample_questions = [
    "Which sales office has the most customers?",
    "What are the most common payment terms?",
    "Show me shipping conditions by plant",
    "Which customers have the highest number of orders?",
    "What's the distribution of sales groups?"
]

# Create Gradio interface
with gr.Blocks(title="SAP SALT Analytics Demo", theme=gr.themes.Soft()) as demo:
    
    gr.Markdown("""
    # 🚀 SAP SALT Dataset Analytics Demo
    ## Open Source Analytics + AI for SAP ERP
    
    This demo showcases how open source tools (DuckDB + OpenAI) can generate massive value for enterprises running SAP ERP systems.
    """)
    
    with gr.Tab("📥 Load Dataset"):
        gr.Markdown("### Load SAP SALT Dataset from Hugging Face")
        
        load_btn = gr.Button("Load SALT Dataset", variant="primary")
        load_output = gr.Textbox(label="Status", lines=3)
        
        load_btn.click(
            fn=load_dataset_interface,
            outputs=load_output
        )
    
    with gr.Tab("📈 Insights"):
        gr.Markdown("### Pre-built Analytics Insights")
        
        insights_btn = gr.Button("Generate Insights", variant="primary")
        insights_output = gr.Markdown()
        
        insights_btn.click(
            fn=show_insights_interface,
            outputs=insights_output
        )
    
    with gr.Tab("🤖 AI Q&A"):
        gr.Markdown("### Ask Questions in Natural Language")
        
        with gr.Row():
            with gr.Column(scale=3):
                api_key_input = gr.Textbox(
                    label="OpenAI API Key",
                    type="password",
                    placeholder="Enter your OpenAI API key"
                )
                
                question_input = gr.Textbox(
                    label="Your Question",
                    placeholder="e.g., Which sales office handles the most customers?",
                    lines=2
                )
                
                sample_dropdown = gr.Dropdown(
                    choices=sample_questions,
                    label="Or choose a sample question",
                    value=None
                )
                
                ask_btn = gr.Button("Get Answer", variant="primary")
            
            with gr.Column(scale=4):
                qa_output = gr.Markdown()
        
        # Update question input when sample is selected
        sample_dropdown.change(
            fn=lambda x: x if x else "",
            inputs=sample_dropdown,
            outputs=question_input
        )
        
        ask_btn.click(
            fn=qa_interface,
            inputs=[question_input, api_key_input],
            outputs=qa_output
        )
    
    with gr.Tab("ℹ️ About"):
        gr.Markdown("""
        ### About This Demo
        
        **Dataset**: SAP SALT (Sales Autocompletion Linked Business Tables)
        - Real SAP S/4HANA sales order data
        - 4 linked tables: Sales Documents, Items, Customers, Addresses
        - 8 classification targets for ML models
        
        **Technology Stack**:
        - **DuckDB**: High-performance analytics database
        - **OpenAI GPT-4**: Natural language to SQL conversion
        - **Hugging Face**: Dataset hosting and deployment
        - **Gradio**: Interactive web interface
        
        **Business Value**:
        - Automate sales order completion (70-80% accuracy)
        - Optimize customer-to-sales office assignments
        - Predict shipping and payment preferences
        - Generate actionable business insights
        
        **Open Source Benefits**:
        - Zero licensing costs vs. proprietary SAP analytics
        - Full customization and control
        - Community-driven improvements
        - Easy integration with existing systems
        """)

if __name__ == "__main__":
    demo.launch()