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from dotenv import load_dotenv
import os
import gradio as gr
from groq import Groq
load_dotenv()
api = os.getenv("groq_api_key")
def create_prompt(user_query, table_metadata):
system_prompt = """
You are a SQL query generator specialized in generating SQL queries for a single table at a time.
Your task is to accurately convert natural language queries into SQL statements based on the user's intent and the provided table metadata.
Rules:
- Single Table Only: Use only the table in the metadata.
- Metadata-Based Validation: Use only columns in the metadata.
- User Intent: Support filters, grouping, sorting, etc.
- SQL Syntax: Use standard SQL (DuckDB compatible).
- Output only valid SQL. No extra commentary.
Input:
User Query: {user_query}
Table Metadata: {table_metadata}
Output:
SQL Query (on a single line, nothing else).
"""
return system_prompt.strip(), f"User Query: {user_query}\nTable Metadata: {table_metadata}"
def generate_output(system_prompt, user_prompt):
client = Groq(api_key=api)
chat_completion = client.chat.completions.create(
messages=[
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_prompt}
],
model="llama3-70b-8192"
)
response = chat_completion.choices[0].message.content.strip()
return response if response.lower().startswith("select") else "Can't perform the task at the moment."
# NEW: accepts user_query and dynamic table_metadata string
def response(payload):
user_query = payload.get("question", "")
table_metadata = payload.get("schema", "")
system_prompt, user_prompt = create_prompt(user_query, table_metadata)
return generate_output(system_prompt, user_prompt)
demo = gr.Interface(
fn=response,
inputs=gr.JSON(label="Input JSON (question, schema)"),
outputs="text",
title="SQL Generator (Groq + LLaMA3)",
description="Input: question & table metadata. Output: SQL using dynamic schema."
)
demo.launch()
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