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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("β TOGETHER_API_KEY loaded from Hugging Face secret.") | |
| return key | |
| # For local dev fallback | |
| local_key = "your-local-api-key-here" # π REPLACE with your actual key | |
| if local_key: | |
| print("β οΈ Using local fallback API key.") | |
| return local_key | |
| raise RuntimeError("β TOGETHER_API_KEY is missing. Set it in Hugging Face Secrets or update the fallback.") | |
| # β READ API KEY from Hugging Face Secret | |
| TOGETHER_API_KEY = get_together_api_key() | |
| if not TOGETHER_API_KEY: | |
| raise RuntimeError("β 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"π SQL Query:\n```sql\n{sql}\n```", result | |
| except Exception as e: | |
| return f"β Error: {str(e)}", pd.DataFrame() | |
| with gr.Blocks() as demo: | |
| gr.Markdown("## π Excel SQL Chatbot with Together API") | |
| file_input = gr.File(label="π Upload Excel File (.xlsx)") | |
| question = gr.Textbox(label="π§ Ask a question about your data") | |
| submit = gr.Button("π 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() |