""" title: Save to Excel author: brunthaler sebastian description: Extract markdown tables from the last assistant message to an Excel file and trigger a download in the browser. icon_url: 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 version: 0.1.0 requirements: fastapi, pandas """ import os import pandas as pd import re import base64 from fastapi import FastAPI, HTTPException from typing import Optional, Callable, Awaitable, Any app = FastAPI() class Action: class UserValves: def __init__(self, show_status=True): self.show_status = show_status def __init__(self): pass async def action( self, body: dict, __user__=None, __event_emitter__=None, __event_call__: Optional[Callable[[Any], Awaitable[None]]] = None, ): print(f"action:{__name__}") user_valves = __user__.get("valves") if __user__ else None if not user_valves: user_valves = self.UserValves() if __event_emitter__: last_assistant_message = body["messages"][-1] if user_valves.show_status: await __event_emitter__( { "type": "status", "data": {"description": "Saving to file", "done": False}, } ) try: # Extract tables from the message message_content = last_assistant_message["content"] tables = self.extract_tables_from_message(message_content) if not tables: raise HTTPException(status_code=400, detail="No tables found") # Generate a unique name for the output file unique_name = f"output_{int(pd.Timestamp.now().timestamp())}.xlsx" # Convert tables into an Excel file excel_file_path = f"app/backend/data/temp/{unique_name}" os.makedirs(os.path.dirname(excel_file_path), exist_ok=True) self.save_tables_to_excel(tables, excel_file_path) # Trigger an event to save the file locally in the browser if __event_call__: # Read the Excel file and encode it in Base64 with open(excel_file_path, "rb") as file: file_content = file.read() base64_blob = base64.b64encode(file_content).decode("utf-8") # Send the blob directly to the frontend await __event_call__( { "type": "execute", "data": { "code": f""" try {{ const base64Data = "{base64_blob}"; const binaryData = atob(base64Data); const arrayBuffer = new Uint8Array(binaryData.length); for (let i = 0; i < binaryData.length; i++) {{ arrayBuffer[i] = binaryData.charCodeAt(i); }} const blob = new Blob([arrayBuffer], {{ type: "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet" }}); const filename = "{unique_name}"; const url = URL.createObjectURL(blob); const a = document.createElement("a"); a.style.display = "none"; a.href = url; a.download = filename; document.body.appendChild(a); a.click(); URL.revokeObjectURL(url); document.body.removeChild(a); }} catch (error) {{ console.error('Error triggering download:', error); }} """ }, } ) if user_valves.show_status: await __event_emitter__( { "type": "status", "data": {"description": "Output Saved", "done": True}, } ) # Remove the temporary file after processing if os.path.exists(excel_file_path): os.remove(excel_file_path) return {"message": "Download event triggered"} except Exception as e: print(f"Error processing tables: {str(e)}") if user_valves.show_status: await __event_emitter__( { "type": "status", "data": { "description": "Error Saving to File", "done": True, }, } ) raise HTTPException( status_code=500, detail=f"Error processing tables: {str(e)}" ) def extract_tables_from_message(self, message: str) -> list: table_pattern = r"(?<=\|)(.*?)(?=\|)" rows = message.split("\n") tables = [] current_table = [] for row in rows: if re.search(table_pattern, row): # Check if the row consists only of dashes (Markdown separator) cells = [cell.strip() for cell in row.strip("|").split("|")] if all(re.fullmatch(r"-+", cell) for cell in cells): continue # Skip separator row current_table.append(cells) elif current_table: tables.append(current_table) current_table = [] if current_table: tables.append(current_table) return tables def save_tables_to_excel(self, tables: list, file_path: str): with pd.ExcelWriter(file_path, engine="xlsxwriter") as writer: for i, table in enumerate(tables): headers = table[0] data = table[1:] # Convert data into a DataFrame df = pd.DataFrame(data, columns=headers) # Try to convert numbers into numeric values for col in df.columns: df[col] = pd.to_numeric(df[col], errors="ignore") # Write the table into an Excel sheet sheet_name = f"Table_{i + 1}" df.to_excel(writer, index=False, sheet_name=sheet_name) # Apply a number format to numeric columns workbook = writer.book worksheet = writer.sheets[sheet_name] for j, column in enumerate(df.columns): if pd.api.types.is_numeric_dtype(df[column]): col_letter = chr(65 + j) worksheet.set_column(f"{col_letter}:{col_letter}", None, workbook.add_format({"num_format": "0.00"}))