app.py

import gradio as gr
import openai
import sqlite3
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os

# OpenRouter API Key (Replace with yours)
OPENROUTER_API_KEY = "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"
OPENROUTER_MODEL = "sophosympatheia/rogue-rose-103b-v0.2:free"

# Hugging Face Space path
DB_PATH = "ecommerce.db"

# Ensure dataset exists
if not os.path.exists(DB_PATH):
    os.system("wget https://your-dataset-link.com/ecommerce.db -O ecommerce.db")  # Replace with actual dataset link

# Initialize OpenAI client
openai_client = openai.OpenAI(api_key=OPENROUTER_API_KEY, base_url="https://openrouter.ai/api/v1")

# Few-shot examples for text-to-SQL
few_shot_examples = [
    {"input": "Show all customers from São Paulo.", "output": "SELECT * FROM customers WHERE customer_state = 'SP';"},
    {"input": "Find the total sales per product.", "output": "SELECT product_id, SUM(price) FROM order_items GROUP BY product_id;"},
    {"input": "List all orders placed in 2017.", "output": "SELECT * FROM orders WHERE order_purchase_timestamp LIKE '2017%';"}
]

# Function: Convert text to SQL
def text_to_sql(query):
    prompt = "Convert the following queries into SQL:\n\n"
    for example in few_shot_examples:
        prompt += f"Input: {example['input']}\nOutput: {example['output']}\n\n"
    prompt += f"Input: {query}\nOutput:"

    try:
        response = openai_client.chat.completions.create(
            model=OPENROUTER_MODEL,
            messages=[{"role": "system", "content": "You are an SQL expert."}, {"role": "user", "content": prompt}]
        )
        return response.choices[0].message.content.strip()
    except Exception as e:
        return f"Error: {e}"

# Function: Execute SQL on SQLite database
def execute_sql(sql_query):
    try:
        conn = sqlite3.connect(DB_PATH)
        df = pd.read_sql_query(sql_query, conn)
        conn.close()
        return df
    except Exception as e:
        return f"SQL Execution Error: {e}"

# Function: Generate Dynamic Visualization
def visualize_data(df):
    if df.empty or df.shape[1] < 2:
        return None

    # Detect numeric columns
    numeric_cols = df.select_dtypes(include=['number']).columns
    if len(numeric_cols) < 1:
        return None

    plt.figure(figsize=(6, 4))
    sns.set_theme(style="darkgrid")

    # Choose visualization type dynamically
    if len(numeric_cols) == 1:  # Single numeric column, assume it's a count metric
        sns.histplot(df[numeric_cols[0]], bins=10, kde=True, color="teal")
        plt.title(f"Distribution of {numeric_cols[0]}")
    elif len(numeric_cols) == 2:  # Two numeric columns, assume X-Y plot
        sns.scatterplot(x=df[numeric_cols[0]], y=df[numeric_cols[1]], color="blue")
        plt.title(f"{numeric_cols[0]} vs {numeric_cols[1]}")
    elif df.shape[0] < 10:  # If rows are few, prefer pie chart
        plt.pie(df[numeric_cols[0]], labels=df.iloc[:, 0], autopct='%1.1f%%', colors=sns.color_palette("pastel"))
        plt.title(f"Proportion of {numeric_cols[0]}")
    else:  # Default: Bar chart for categories + values
        sns.barplot(x=df.iloc[:, 0], y=df[numeric_cols[0]], palette="coolwarm")
        plt.xticks(rotation=45)
        plt.title(f"{df.columns[0]} vs {numeric_cols[0]}")

    plt.tight_layout()
    plt.savefig("chart.png")
    return "chart.png"

# Gradio UI
def gradio_ui(query):
    sql_query = text_to_sql(query)
    results = execute_sql(sql_query)
    visualization = visualize_data(results) if isinstance(results, pd.DataFrame) else None

    return sql_query, results.to_string(index=False) if isinstance(results, pd.DataFrame) else results, visualization

with gr.Blocks() as demo:
    gr.Markdown("## SQL Explorer: Text-to-SQL with Real Execution & Visualization")
    query_input = gr.Textbox(label="Enter your query", placeholder="e.g., Show all products sold in 2018.")
    submit_btn = gr.Button("Convert & Execute")
    sql_output = gr.Textbox(label="Generated SQL Query")
    table_output = gr.Textbox(label="Query Results")
    chart_output = gr.Image(label="Data Visualization")

    submit_btn.click(gradio_ui, inputs=[query_input], outputs=[sql_output, table_output, chart_output])

# Launch
demo.launch()