import base64
import os
import tempfile
from io import BytesIO

import gradio as gr
import pandas as pd
import requests


API_BASE_URL = os.getenv("MULTIMODAL_API_BASE_URL", "http://127.0.0.1:7861")
EXTRACTION_API_URL = os.getenv(
    "MULTIMODAL_API_URL",
    f"{API_BASE_URL.rstrip('/')}/information_extraction/",
)
MAPPING_API_URL = os.getenv(
    "MULTIMODAL_MAPPING_API_URL",
    f"{API_BASE_URL.rstrip('/')}/mapping/",
)


def process_zip(zip_file, employee_code, debug=False):
    if zip_file is None:
        raise gr.Error("Please upload a ZIP file.")
    if not employee_code or not employee_code.strip():
        raise gr.Error("Please enter an employee code.")

    with open(zip_file, "rb") as file_obj:
        response = requests.post(
            EXTRACTION_API_URL,
            files={
                "file": (
                    os.path.basename(zip_file),
                    file_obj,
                    "application/zip",
                )
            },
            data={
                "employee_code": employee_code.strip(),
                "debug": str(debug).lower(),
            },
            timeout=300,
        )

    try:
        payload = response.json()
    except ValueError as exc:
        raise gr.Error("The API returned an invalid response.") from exc

    if response.status_code != 200:
        error_message = payload.get("detail") or payload.get("message") or "Request failed."
        raise gr.Error(error_message)

    excel_base64 = payload.get("excel_data_base64")
    if not excel_base64:
        raise gr.Error("The API response did not include an Excel file.")

    excel_bytes = base64.b64decode(excel_base64)
    dataframe = pd.read_excel(BytesIO(excel_bytes))

    output_path = os.path.join(
        tempfile.gettempdir(),
        f"extraction_result_{employee_code.strip()}.xlsx",
    )
    with open(output_path, "wb") as excel_file:
        excel_file.write(excel_bytes)

    status = (
        f"{payload.get('message', 'Processing completed.')} "
        f"Time: {payload.get('duration', 'N/A')}s."
    )

    return status, dataframe, output_path


def process_mapping(product_list, dense_weight=0.7, sparse_weight=0.3, normalize=True):
    if not product_list or not product_list.strip():
        raise gr.Error("Please enter at least one product name.")

    response = requests.post(
        MAPPING_API_URL,
        data={
            "product_list": product_list.strip(),
            "dense_weight": dense_weight,
            "sparse_weight": sparse_weight,
            "normalize": str(normalize).lower(),
        },
        timeout=300,
    )

    try:
        payload = response.json()
    except ValueError as exc:
        raise gr.Error("The API returned an invalid response.") from exc

    if response.status_code != 200:
        error_message = payload.get("detail") or payload.get("message") or "Request failed."
        raise gr.Error(error_message)

    results = payload.get("results")
    if not isinstance(results, list):
        raise gr.Error("The API response did not include mapping results.")

    dataframe = pd.DataFrame(results)
    if dataframe.empty:
        dataframe = pd.DataFrame(
            columns=["original_product_name", "top_1", "top_2", "top_3", "top_4", "top_5"]
        )
    else:
        preferred_columns = [
            "original_product_name",
            "top_1",
            "top_2",
            "top_3",
            "top_4",
            "top_5",
        ]
        dataframe = dataframe.reindex(columns=preferred_columns)

    total_products = payload.get("total_products", len(dataframe))
    duration = payload.get("api_duration", payload.get("duration", "N/A"))
    status = f"Mapped {total_products} products. Time: {duration}s."

    return status, dataframe


with gr.Blocks(title="Multimodal OCR and Product Mapping") as demo:
    gr.Markdown(
        """
        # Multimodal OCR and Product Mapping Interface
        ## Run information extraction or product mapping from a single interface.
        """ 
    )

    with gr.Tabs():
        with gr.Tab("Information Extraction"):
            with gr.Row():
                zip_input = gr.File(
                    label="ZIP File",
                    file_types=[".zip"],
                    type="filepath",
                )
                employee_code_input = gr.Textbox(
                    label="Employee Code",
                    placeholder="e.g. admin",
                )

            debug_input = gr.Checkbox(label="Debug mode", value=False)
            submit_button = gr.Button("Process", variant="primary")

            status_output = gr.Textbox(label="Status", interactive=False)
            excel_preview = gr.Dataframe(label="Excel Preview", interactive=False)
            excel_download = gr.File(label="Download Excel", interactive=False)

            submit_button.click(
                fn=process_zip,
                inputs=[zip_input, employee_code_input, debug_input],
                outputs=[status_output, excel_preview, excel_download],
            )

        with gr.Tab("Mapping"):
            product_input = gr.Textbox(
                label="Product List",
                placeholder="One product per line",
                lines=10,
            )

            with gr.Row():
                dense_weight_input = gr.Slider(
                    minimum=0,
                    maximum=1,
                    value=0.7,
                    step=0.1,
                    label="Dense Weight",
                )
                sparse_weight_input = gr.Slider(
                    minimum=0,
                    maximum=1,
                    value=0.3,
                    step=0.1,
                    label="Sparse Weight",
                )
            gr.Markdown("---")
            gr.Markdown("#### 💡 Ví dụ")
            gr.Examples(
                examples=[
                    ["Đèn LED ốp trần 18W\nBóng LED E27 9W\nĐèn downlight âm trần"],
                    ["Bóng Trụ 30W\nPhích nước 2L\nVợt bắt muỗi"],
                    ["Đèn năng lượng mặt trời\nĐèn sạc tích điện\nBóng compact 20W"],
                ],
                inputs=[product_input],
                label="Click để thử các ví dụ"
            )
            normalize_input = gr.Checkbox(label="Normalize Query", value=True)
            mapping_button = gr.Button("Run Mapping", variant="primary")

            mapping_status = gr.Textbox(label="Status", interactive=False)
            mapping_preview = gr.Dataframe(label="Mapping Preview", interactive=False)

            mapping_button.click(
                fn=process_mapping,
                inputs=[
                    product_input,
                    dense_weight_input,
                    sparse_weight_input,
                    normalize_input,
                ],
                outputs=[mapping_status, mapping_preview],
            )


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