app.py

import gradio as gr
import os
import pandas as pd
from huggingface_hub import InferenceClient

# ===============================
# LLM CLIENT SETUP
# ===============================
HF_TOKEN = os.getenv("HF")
client = InferenceClient(model="Qwen/Qwen2.5-7B-Instruct", token=HF_TOKEN)

def analyze_excel(message, history, file):
    """
    Problem Statement:
    1. Add your own HF token in the settings to get the LLM working.
    2. Update requirements.txt, app.py as needed.
    3. Develop a robust "Text-to-Code" analytical workflow.
    
    Requirements:
    a.Code Generation : Transform natural language user queries into executable, sandboxed Python code (specifically using pandas).
    b.Execution : Securely execute the generated code on the Hugging Face Space server against the uploaded dataset.
    c.Synthesis : Capture the raw output of the code execution and feed it back to the LLM to generate a natural language insight.
    """
    if file is None:
        return "Please upload an Excel file to begin."
    
    # The function needs a return here to avoid a NoneType error in Gradio
    return "File received! Candidate: Implement the Planner-Action-Synthesis logic here."

# ===============================
# UI CONFIGURATION
# ===============================
with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown("# 📊 Technical Assessment: Data Analysis Agent")
    gr.Markdown("### Objective: Build a Text-to-Code workflow using Qwen 2.5")
    
    with gr.Row():
        excel_file = gr.File(
            label="1. Upload Dataset (.xlsx)",
            file_types=[".xlsx"]
        )
    
    gr.ChatInterface(
        fn=analyze_excel,
        additional_inputs=[excel_file],
        type="messages",
        description="2. Ask questions about your data (e.g., 'What is the average profit by region?')",
    )

if __name__ == "__main__":
    demo.launch()