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| import requests | |
| import pandas as pd | |
| import io | |
| from fpdf import FPDF | |
| import json | |
| def convert_xlsx_to_pdf(file): | |
| """Converts an XLSX file to a PDF and returns a BytesIO object with a filename.""" | |
| excel_data = pd.ExcelFile(file) | |
| pdf = FPDF() | |
| pdf.set_auto_page_break(auto=True, margin=15) | |
| pdf.add_page() | |
| pdf.set_font("Arial", size=12) | |
| for sheet_name in excel_data.sheet_names: | |
| pdf.cell(200, 10, txt=f"Sheet: {sheet_name}", ln=True, align='C') | |
| pdf.ln(10) | |
| df = excel_data.parse(sheet_name) | |
| for i in range(min(10, len(df))): # Limiting rows for readability | |
| row_data = " | ".join(str(x) for x in df.iloc[i]) | |
| pdf.multi_cell(0, 10, row_data) | |
| pdf.ln(5) | |
| pdf_output = io.BytesIO() | |
| pdf_output.write(pdf.output(dest='S').encode('latin1')) # Convert to bytes | |
| pdf_output.seek(0) | |
| # Manually add a 'name' attribute to mimic a file | |
| pdf_output.name = file.name.replace(".xlsx", ".pdf") | |
| return pdf_output | |
| def upload_file_to_vectara(file, customer_id, api_key, corpus_key): | |
| """Uploads a file to Vectara API v2.""" | |
| url = f"https://api.vectara.io/v2/corpora/{corpus_key}/upload_file" | |
| headers = { | |
| "customer-id": customer_id, | |
| "x-api-key": api_key, | |
| "Accept": "application/json" | |
| } | |
| metadata = {"type_file": "excel"} if file.name.endswith('.xlsx') else {} | |
| if file.name.endswith('.xlsx'): | |
| file = convert_xlsx_to_pdf(file) # Convert XLSX to PDF | |
| files = { | |
| 'metadata': (None, json.dumps(metadata), 'application/json'), | |
| "file": (file.name, file.getvalue())} # Now file.name exists | |
| response = requests.post(url, headers=headers, files=files) | |
| return response.json() | |