app.py

import json
import openai
import gradio as gr
import duckdb
from functools import lru_cache
import pandas as pd
import plotly.express as px
import os

# Set OpenAI API key
openai.api_key = os.getenv("OPENAI_API_KEY")

# =========================
# Configuration and Setup
# =========================

# Load the Parquet dataset path
dataset_path = 'sample_contract_df.parquet'  # Update with your Parquet file path

# Provided schema
schema = [
    {"column_name": "department_ind_agency", "column_type": "VARCHAR"},
    {"column_name": "cgac", "column_type": "BIGINT"},
    {"column_name": "sub_tier", "column_type": "VARCHAR"},
    {"column_name": "fpds_code", "column_type": "VARCHAR"},
    {"column_name": "office", "column_type": "VARCHAR"},
    {"column_name": "aac_code", "column_type": "VARCHAR"},
    {"column_name": "posteddate", "column_type": "VARCHAR"},
    {"column_name": "type", "column_type": "VARCHAR"},
    {"column_name": "basetype", "column_type": "VARCHAR"},
    {"column_name": "popstreetaddress", "column_type": "VARCHAR"},
    {"column_name": "popcity", "column_type": "VARCHAR"},
    {"column_name": "popstate", "column_type": "VARCHAR"},
    {"column_name": "popzip", "column_type": "VARCHAR"},
    {"column_name": "popcountry", "column_type": "VARCHAR"},
    {"column_name": "active", "column_type": "VARCHAR"},
    {"column_name": "awardnumber", "column_type": "VARCHAR"},
    {"column_name": "awarddate", "column_type": "VARCHAR"},
    {"column_name": "award", "column_type": "DOUBLE"},
    {"column_name": "awardee", "column_type": "VARCHAR"},
    {"column_name": "state", "column_type": "VARCHAR"},
    {"column_name": "city", "column_type": "VARCHAR"},
    {"column_name": "zipcode", "column_type": "VARCHAR"},
    {"column_name": "countrycode", "column_type": "VARCHAR"}
]

@lru_cache(maxsize=1)
def get_schema():
    return schema

COLUMN_TYPES = {col['column_name']: col['column_type'] for col in get_schema()}

# =========================
# Database Interaction
# =========================

def load_dataset_schema():
    """
    Loads the dataset schema into DuckDB by creating a view.
    """
    con = duckdb.connect()
    try:
        con.execute("DROP VIEW IF EXISTS contract_data")
        con.execute(f"CREATE VIEW contract_data AS SELECT * FROM '{dataset_path}'")
        return True
    except Exception as e:
        print(f"Error loading dataset schema: {e}")
        return False
    finally:
        con.close()

# =========================
# OpenAI API Integration
# =========================

def parse_query(nl_query):
    """
    Converts a natural language query into a SQL query using OpenAI's API.
    """
    messages = [
        {"role": "system", "content": "Convert natural language queries to SQL queries for 'contract_data'."},
        {"role": "user", "content": f"Schema:\n{json.dumps(schema, indent=2)}\n\nQuery:\n\"{nl_query}\"\n\nSQL:"}
    ]

    try:
        response = openai.chat.completions.create(
            model="gpt-4o-mini",
            messages=messages,
            temperature=0,
            max_tokens=150,
        )
        sql_query = response.choices[0].message.content.strip()
        return sql_query
    except Exception as e:
        return f"Error generating SQL query: {e}"

# =========================
# Plotting Utilities
# =========================

def detect_plot_intent(nl_query):
    """
    Detects if the user's query involves plotting.
    """
    plot_keywords = ['plot', 'graph', 'chart', 'distribution', 'visualize', 'trend', 'histogram', 'bar', 'line']
    return any(keyword in nl_query.lower() for keyword in plot_keywords)

def generate_plot_code(sql_query, result_df):
    """
    Generates plotting code based on the SQL query and result DataFrame.
    """
    if not detect_plot_intent(sql_query):
        return None

    columns = result_df.columns.tolist()
    if len(columns) >= 2:
        fig = px.bar(result_df, x=columns[0], y=columns[1], title='Generated Plot')
        fig.update_layout(title_x=0.5)
        return fig
    else:
        return None

# =========================
# Gradio Application UI
# =========================

with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue")) as demo:
    gr.Markdown("""
    ## Parquet Data Explorer

    **Query and visualize data effortlessly.**

    """, elem_id="main-title")

    with gr.Row():
        with gr.Column(scale=1):
            query = gr.Textbox(
                label="Ask a question about the data",
                placeholder='e.g., "What are the total awards over 1M in California?"',
                lines=1
            )
            # Display schema next to the input
            schema_display = gr.JSON(value=json.loads(json.dumps(get_schema(), indent=2)), visible=False)

            error_out = gr.Alert(variant="error", visible=False)

        with gr.Column(scale=2):
            results_out = gr.DataFrame(label="Results")
            plot_out = gr.Plot()

    def on_query_submit(nl_query):
        sql_query = parse_query(nl_query)
        if sql_query.startswith("Error"):
            return gr.update(visible=True, value=sql_query), None, None
        result_df, error_msg = execute_query(sql_query)
        if error_msg:
            return gr.update(visible=True, value=error_msg), None, None
        fig = generate_plot_code(nl_query, result_df)
        return gr.update(visible=False), result_df, fig

    def on_focus():
        return gr.update(visible=True)

    query.submit(
        fn=on_query_submit,
        inputs=query,
        outputs=[error_out, results_out, plot_out]
    )
    query.focus(
        fn=on_focus,
        outputs=schema_display
    )

# =========================
# Helper Functions
# =========================

def execute_query(sql_query):
    """
    Executes the SQL query and returns the results.
    """
    if sql_query.startswith("Error"):
        return None, sql_query

    try:
        con = duckdb.connect()
        con.execute(f"CREATE OR REPLACE VIEW contract_data AS SELECT * FROM '{dataset_path}'")
        result_df = con.execute(sql_query).fetchdf()
        con.close()
        return result_df, ""
    except Exception as e:
        return None, f"Error executing query: {e}"

# =========================
# Launch the Gradio App
# =========================

demo.launch()