Add workbook-native XLSX table workflow

Add XLSX upload, workbook summary, sheet explorer, formula-preserving table preview, and table inference output. Fix unsized read-only worksheets by forcing dimension calculation.

app.py

@@ -1,13 +1,22 @@
+from io import StringIO
 from pathlib import Path
+import tomllib
 
 import gradio as gr
 import pandas as pd
 import spaces
 from huggingface_hub import hf_hub_download
 from llama_cpp import Llama
+from openpyxl import load_workbook
+from openpyxl.utils import get_column_letter
 
 
+PROJECT_VERSION = tomllib.loads(
+    Path(__file__).with_name("pyproject.toml").read_text(encoding="utf-8")
+)["project"]["version"]
 MODEL_REPO = "mradermacher/Spreadsheet-RL-4B-GGUF"
+XLSX_PREVIEW_ROWS = 100
+XLSX_PREVIEW_COLUMNS = 50
 QUANT_FILES = {
     "Q2_K · 1.9 GB": "Spreadsheet-RL-4B.Q2_K.gguf",
     "Q3_K_S · 2.2 GB": "Spreadsheet-RL-4B.Q3_K_S.gguf",
@@ -27,6 +36,58 @@ model = None
 active_quant = None
 
 
+def build_workbook_overview(file_path: str | Path) -> pd.DataFrame:
+    workbook = load_workbook(file_path, read_only=True, data_only=False)
+    active_sheet = workbook.active.title
+    overview = pd.DataFrame(
+        [
+            {
+                "Sheet": worksheet.title,
+                "Active": worksheet.title == active_sheet,
+                "Used range": worksheet.calculate_dimension(force=True),
+                "Rows": worksheet.max_row,
+                "Columns": worksheet.max_column,
+            }
+            for worksheet in workbook.worksheets
+        ]
+    )
+    workbook.close()
+    return overview
+
+
+def preview_xlsx_sheet(file_path: str | Path, sheet_name: str) -> pd.DataFrame:
+    workbook = load_workbook(file_path, read_only=True, data_only=False)
+    worksheet = workbook[sheet_name]
+    worksheet.calculate_dimension(force=True)
+    column_count = min(worksheet.max_column, XLSX_PREVIEW_COLUMNS)
+    row_count = min(worksheet.max_row, XLSX_PREVIEW_ROWS)
+    preview = pd.DataFrame(
+        worksheet.iter_rows(
+            min_row=1,
+            max_row=row_count,
+            min_col=1,
+            max_col=column_count,
+            values_only=True,
+        ),
+        columns=[get_column_letter(index) for index in range(1, column_count + 1)],
+    )
+    workbook.close()
+    return preview
+
+
+def load_xlsx_workflow(
+    file_path: str | Path,
+) -> tuple[pd.DataFrame, gr.Dropdown, pd.DataFrame]:
+    overview = build_workbook_overview(file_path)
+    sheet_names = overview["Sheet"].tolist()
+    selected_sheet = sheet_names[0]
+    return (
+        overview,
+        gr.Dropdown(choices=sheet_names, value=selected_sheet),
+        preview_xlsx_sheet(file_path, selected_sheet),
+    )
+
+
 def download_quant(quantization: str) -> None:
     hf_hub_download(repo_id=MODEL_REPO, filename=QUANT_FILES[quantization])
 
@@ -101,16 +162,72 @@ def generate(
     return completion["choices"][0]["message"]["content"].rsplit("</think>", 1)[-1].strip()
 
 
+@spaces.GPU(duration=120)
+def generate_xlsx_table(
+    system_prompt: str,
+    user_prompt: str,
+    attachment: str,
+    sheet_name: str,
+    quantization: str,
+) -> pd.DataFrame:
+    global active_quant, model
+
+    quant_file = QUANT_FILES[quantization]
+    if active_quant != quantization:
+        model = None
+        active_quant = None
+        model = Llama(
+            model_path=hf_hub_download(repo_id=MODEL_REPO, filename=quant_file),
+            n_ctx=4096,
+            n_gpu_layers=-1,
+            verbose=True,
+        )
+        active_quant = quantization
+
+    overview = build_workbook_overview(attachment).to_csv(index=False)
+    preview = preview_xlsx_sheet(attachment, sheet_name).to_csv(index=False)
+    messages = [
+        {
+            "role": "system",
+            "content": (
+                f"{system_prompt}\n\nReturn only valid CSV with one header row. "
+                "Do not wrap the CSV in a code fence or add prose."
+            ),
+        },
+        {
+            "role": "user",
+            "content": (
+                f"Task:\n{user_prompt}\n\nWorkbook overview:\n{overview}\n"
+                f"Selected sheet preview ({sheet_name}, first {XLSX_PREVIEW_ROWS} rows "
+                f"and {XLSX_PREVIEW_COLUMNS} columns):\n{preview}"
+            ),
+        },
+    ]
+    completion = model.create_chat_completion(
+        messages=messages,
+        max_tokens=1024,
+        temperature=0.6,
+        top_p=0.95,
+        top_k=20,
+    )
+    response = completion["choices"][0]["message"]["content"].rsplit("</think>", 1)[-1].strip()
+    return pd.read_csv(StringIO(response))
+
+
 CSS = """
-.gradio-container { max-width: 1180px !important; }
+.gradio-container {
+    width: min(calc(100% - 32px), 1600px) !important;
+    max-width: 1600px !important;
+    margin-inline: auto !important;
+}
 .agent-panel { border: 2px dashed #79b5ce; border-radius: 18px; padding: 8px; }
 .output-panel { border: 2px dashed #f0aeb7; border-radius: 18px; padding: 8px; }
 """
 
 with gr.Blocks(css=CSS, title="Spreadsheet Data Agent") as demo:
     gr.Markdown(
-        """
-        # Spreadsheet Data Agent
+        f"""
+        # Spreadsheet Data Agent · v{PROJECT_VERSION}
 
         [Code](https://github.com/electblake/Spreadsheet-RL-Data-Agent) | [Demo](https://huggingface.co/spaces/electblake/spreadsheet-data-agent) | [Paper](https://arxiv.org/abs/2605.22642) | [Spreadsheet-RL Model](https://huggingface.co/Spreadsheet-RL/Spreadsheet-RL-4B)
 
@@ -119,43 +236,125 @@ with gr.Blocks(css=CSS, title="Spreadsheet Data Agent") as demo:
         """
     )
 
-    with gr.Row():
-        with gr.Column(scale=1, elem_classes="agent-panel"):
-            gr.Markdown("### RL data input")
-            system_prompt = gr.Textbox(
-                label="System prompt",
-                value=(
-                    "You are a spreadsheet reasoning assistant. Inspect the supplied "
-                    "spreadsheet or text context and answer the user's request precisely."
-                ),
-                lines=5,
+    with gr.Tabs(selected="basic-data"):
+        with gr.Tab("Basic data only", id="basic-data"):
+            gr.Markdown(
+                "Uses the original inference workflow: uploaded files are converted to "
+                "plain data context and sent to the model with the prompt."
             )
-            user_prompt = gr.Textbox(
-                label="User prompt",
-                placeholder="Describe the spreadsheet task or ask a question…",
-                lines=8,
+
+            with gr.Row():
+                with gr.Column(scale=1, elem_classes="agent-panel"):
+                    gr.Markdown("### RL data input")
+                    system_prompt = gr.Textbox(
+                        label="System prompt",
+                        value=(
+                            "You are a spreadsheet reasoning assistant. Inspect the supplied "
+                            "spreadsheet or text context and answer the user's request precisely."
+                        ),
+                        lines=5,
+                    )
+                    user_prompt = gr.Textbox(
+                        label="User prompt",
+                        placeholder="Describe the spreadsheet task or ask a question…",
+                        lines=8,
+                    )
+                    attachment = gr.File(
+                        label="Optional file context",
+                        file_types=[".txt", ".md", ".json", ".csv", ".tsv", ".xlsx", ".xls"],
+                        type="filepath",
+                    )
+                    quantization = gr.Dropdown(
+                        choices=list(QUANT_FILES),
+                        value="Q4_K_M · 2.8 GB · recommended",
+                        label="Spreadsheet-RL-4B quantization",
+                        info="Static GGUF quants published by mradermacher; Q4_K_M is the reference recommendation.",
+                    )
+                    run = gr.Button("Run inference", variant="primary")
+
+                with gr.Column(scale=1, elem_classes="output-panel"):
+                    gr.Markdown("### Agent response")
+                    response = gr.Textbox(
+                        label="Generated text",
+                        lines=28,
+                        buttons=["copy"],
+                    )
+
+        with gr.Tab("XLSX workflow", id="xlsx-workflow"):
+            gr.Markdown(
+                """
+                ## XLSX workbook workflow
+
+                Upload an Excel workbook to inspect its sheets and preview its table data
+                before running table-focused inference.
+                """
             )
-            attachment = gr.File(
-                label="Optional file context",
-                file_types=[".txt", ".md", ".json", ".csv", ".tsv", ".xlsx", ".xls"],
+
+            xlsx_attachment = gr.File(
+                label="XLSX workbook",
+                file_types=[".xlsx"],
                 type="filepath",
             )
-            quantization = gr.Dropdown(
-                choices=list(QUANT_FILES),
-                value="Q4_K_M · 2.8 GB · recommended",
-                label="Spreadsheet-RL-4B quantization",
-                info="Static GGUF quants published by mradermacher; Q4_K_M is the reference recommendation.",
+            workbook_overview = gr.Dataframe(
+                headers=["Sheet", "Active", "Used range", "Rows", "Columns"],
+                datatype=["str", "bool", "str", "number", "number"],
+                label="Workbook summary",
+                interactive=False,
+                buttons=["fullscreen", "copy"],
+                show_search="filter",
+            )
+            xlsx_sheet = gr.Dropdown(
+                label="Preview sheet",
+                choices=[],
             )
-            run = gr.Button("Run inference", variant="primary")
-
-        with gr.Column(scale=1, elem_classes="output-panel"):
-            gr.Markdown("### Agent response")
-            response = gr.Textbox(
-                label="Generated text",
-                lines=28,
-                buttons=["copy"],
+            xlsx_preview = gr.Dataframe(
+                label=(
+                    f"Selected sheet preview (first {XLSX_PREVIEW_ROWS} rows and "
+                    f"{XLSX_PREVIEW_COLUMNS} columns)"
+                ),
+                interactive=False,
+                max_height=520,
+                buttons=["fullscreen", "copy"],
+                show_row_numbers=True,
+                show_search="filter",
             )
 
+            with gr.Row():
+                with gr.Column(scale=1, elem_classes="agent-panel"):
+                    gr.Markdown("### Table inference input")
+                    xlsx_system_prompt = gr.Textbox(
+                        label="System prompt",
+                        value=(
+                            "You are a spreadsheet data assistant. Analyze the workbook "
+                            "summary and selected sheet preview, then return the requested "
+                            "result as a table."
+                        ),
+                        lines=5,
+                    )
+                    xlsx_user_prompt = gr.Textbox(
+                        label="User prompt",
+                        placeholder="Describe the table to derive from this workbook…",
+                        lines=8,
+                    )
+                    xlsx_quantization = gr.Dropdown(
+                        choices=list(QUANT_FILES),
+                        value="Q4_K_M · 2.8 GB · recommended",
+                        label="Spreadsheet-RL-4B quantization",
+                        info="Static GGUF quants published by mradermacher; Q4_K_M is the reference recommendation.",
+                    )
+                    xlsx_run = gr.Button("Run table inference", variant="primary")
+
+                with gr.Column(scale=1, elem_classes="output-panel"):
+                    gr.Markdown("### Table response")
+                    xlsx_response = gr.Dataframe(
+                        label="Generated table",
+                        interactive=False,
+                        max_height=720,
+                        buttons=["fullscreen", "copy"],
+                        show_row_numbers=True,
+                        show_search="filter",
+                    )
+
     gr.Markdown(
         """
         ---
@@ -194,4 +393,35 @@ with gr.Blocks(css=CSS, title="Spreadsheet Data Agent") as demo:
         show_progress="full",
     )
 
+    xlsx_attachment.upload(
+        fn=load_xlsx_workflow,
+        inputs=xlsx_attachment,
+        outputs=[workbook_overview, xlsx_sheet, xlsx_preview],
+        show_progress="full",
+    )
+    xlsx_sheet.change(
+        fn=preview_xlsx_sheet,
+        inputs=[xlsx_attachment, xlsx_sheet],
+        outputs=xlsx_preview,
+        show_progress="full",
+    )
+    xlsx_run.click(
+        fn=download_quant,
+        inputs=xlsx_quantization,
+        outputs=None,
+        show_progress="full",
+    ).then(
+        fn=generate_xlsx_table,
+        inputs=[
+            xlsx_system_prompt,
+            xlsx_user_prompt,
+            xlsx_attachment,
+            xlsx_sheet,
+            xlsx_quantization,
+        ],
+        outputs=xlsx_response,
+        api_name="generate_xlsx_table",
+        show_progress="full",
+    )
+
 demo.queue().launch(mcp_server=True)