SQL-ChatBot / app.py
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import gradio as gr
import pandas as pd
import duckdb
import requests
import re
import io
import os
def get_together_api_key():
"""
Retrieves Together API key from Hugging Face Secrets (hosted) or fallback to local key (dev).
"""
key = os.environ.get("TOGETHER_API_KEY")
if key:
print("βœ… TOGETHER_API_KEY loaded from Hugging Face secret.")
return key
# For local dev fallback
local_key = "your-local-api-key-here" # πŸ‘ˆ REPLACE with your actual key
if local_key:
print("⚠️ Using local fallback API key.")
return local_key
raise RuntimeError("❌ TOGETHER_API_KEY is missing. Set it in Hugging Face Secrets or update the fallback.")
# βœ… READ API KEY from Hugging Face Secret
TOGETHER_API_KEY = get_together_api_key()
if not TOGETHER_API_KEY:
raise RuntimeError("❌ TOGETHER_API_KEY not found. Set it in Hugging Face > Settings > Secrets.")
def generate_sql_from_prompt(prompt, df):
schema = ", ".join([f"{col} ({str(dtype)})" for col, dtype in df.dtypes.items()])
full_prompt = f"""
You are a SQL expert. Here is a table called 'df' with the following schema:
{schema}
User question: "{prompt}"
Write a valid SQL query using the 'df' table. Return only the SQL code.
"""
url = "https://api.together.xyz/v1/chat/completions"
headers = {
"Authorization": f"Bearer {TOGETHER_API_KEY}",
"Content-Type": "application/json"
}
payload = {
"model": "mistralai/Mixtral-8x7B-Instruct-v0.1",
"messages": [{"role": "user", "content": full_prompt}],
"temperature": 0.2,
"max_tokens": 200
}
response = requests.post(url, headers=headers, json=payload)
response.raise_for_status()
result = response.json()
return result['choices'][0]['message']['content'].strip("```sql").strip("```").strip()
def clean_sql_for_duckdb(sql, df_columns):
sql = sql.replace("`", '"')
for col in df_columns:
if " " in col and f'"{col}"' not in sql:
pattern = r'\b' + re.escape(col) + r'\b'
sql = re.sub(pattern, f'"{col}"', sql)
return sql
def chatbot_interface(file, question):
try:
df = pd.read_excel(file)
sql = generate_sql_from_prompt(question, df)
cleaned_sql = clean_sql_for_duckdb(sql, df.columns)
result = duckdb.query(cleaned_sql).to_df()
return f"πŸ“œ SQL Query:\n```sql\n{sql}\n```", result
except Exception as e:
return f"❌ Error: {str(e)}", pd.DataFrame()
with gr.Blocks() as demo:
gr.Markdown("## πŸ“Š Excel SQL Chatbot with Together API")
file_input = gr.File(label="πŸ“‚ Upload Excel File (.xlsx)")
question = gr.Textbox(label="🧠 Ask a question about your data")
submit = gr.Button("πŸš€ Generate & Query")
sql_output = gr.Markdown()
result_table = gr.Dataframe()
submit.click(fn=chatbot_interface, inputs=[file_input, question], outputs=[sql_output, result_table])
if __name__ == "__main__":
demo.launch()