import os
import gradio as gr
import pandas as pd
import tempfile
from agent import run_agent

def _write_tmp(content, name):
    with tempfile.NamedTemporaryFile(mode="w", suffix=name, delete=False) as f:
        f.write(content)
        return f.name

def run_pipeline(file_obj):
    if not file_obj: return "Upload a file", None, None, None, None, None, None
    
    df = pd.read_csv(file_obj.name)
    rows = df.to_dict('records')
    
    state = None
    for log in run_agent(rows):
        if isinstance(log, str):
            yield log, None, None, None, None, None
        else:
            state = log

    yield (
        "Done", 
        pd.DataFrame(state.title_summary), 
        pd.DataFrame(state.abstract_summary),
        pd.DataFrame(state.pajais_map.get("MAPPED", [])),
        _write_tmp(state.comparison_csv, "comparison.csv"),
        _write_tmp(state.taxonomy_json, "taxonomy.json")
    )

with gr.Blocks(title="Agentic Topic Modeller") as demo:
    gr.Markdown("# ◈ AGENTIC TOPIC MODELLER (Mistral Version)")
    
    with gr.Row():
        file_input = gr.File(label="Upload CSV")
        run_btn = gr.Button("Run Pipeline", variant="primary")
    
    logs = gr.Textbox(label="Agent Logs", lines=5)
    
    with gr.Tabs():
        with gr.Tab("Title Topics"):
            t_out = gr.Dataframe()
        with gr.Tab("Abstract Topics"):
            a_out = gr.Dataframe()
        with gr.Tab("PAJAIS Map"):
            p_out = gr.Dataframe()
    
    with gr.Row():
        dl_csv = gr.File(label="Download Comparison")
        dl_json = gr.File(label="Download Taxonomy")

    run_btn.click(run_pipeline, inputs=[file_input], outputs=[logs, t_out, a_out, p_out, dl_csv, dl_json])

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