app.py

# app.py

import gradio as gr
import pandas as pd
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline

# 1) Load your synthetic profitability dataset
df = pd.read_csv('synthetic_profit.csv')

# 2) Choose the publicly available TAPEX WikiSQL model
MODEL_ID = "microsoft/tapex-base-finetuned-wikisql"

# 3) Set device: GPU if available, else CPU
device = 0 if torch.cuda.is_available() else -1

# 4) Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model     = AutoModelForSeq2SeqLM.from_pretrained(MODEL_ID)

# 5) Build the table-question-answering pipeline
table_qa = pipeline(
    "table-question-answering",
    model=model,
    tokenizer=tokenizer,
    framework="pt",
    device=device,
)

# 6) Define the QA function, casting all cells to strings to avoid float issues
def answer_profitability(question: str) -> str:
    # Cast entire DataFrame to string
    df_str = df.astype(str)
    table  = df_str.to_dict(orient="records")
    try:
        out = table_qa(table=table, query=question)
        return out.get("answer", "No answer found.")
    except Exception as e:
        return f"Error: {e}"

# 7) Define Gradio interface
iface = gr.Interface(
    fn=answer_profitability,
    inputs=gr.Textbox(lines=2, placeholder="Ask a question about profitability…"),
    outputs="text",
    title="SAP Profitability Q&A (TAPEX-Base)",
    description=(
        "Free-form questions on the synthetic profitability dataset, "
        "powered end-to-end by microsoft/tapex-base-finetuned-wikisql."
    )
)

# 8) Launch the app
if __name__ == "__main__":
    iface.launch(server_name="0.0.0.0", server_port=7860)