from transformers import BlipProcessor, BlipForConditionalGeneration from PIL import Image import gradio as gr import torch from datetime import datetime from reportlab.lib.pagesizes import letter from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle from reportlab.lib import colors from simple_salesforce import Salesforce import os from dotenv import load_dotenv import base64 import io # Load environment variables from .env file load_dotenv() # Salesforce credentials SF_USERNAME = os.getenv('SF_USERNAME') SF_PASSWORD = os.getenv('SF_PASSWORD') SF_SECURITY_TOKEN = os.getenv('SF_SECURITY_TOKEN') # Initialize Salesforce connection try: sf = Salesforce(username=SF_USERNAME, password=SF_PASSWORD, security_token=SF_SECURITY_TOKEN) except Exception as e: sf = None print(f"Failed to connect to Salesforce: {str(e)}") # Load BLIP model and processor processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base") model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base") model.eval() device = "cuda" if torch.cuda.is_available() else "cpu" model.to(device) # Inference function to generate captions dynamically based on image content def generate_captions_from_image(image): if image.mode != "RGB": image = image.convert("RGB") # Preprocess the image and generate a caption inputs = processor(image, return_tensors="pt").to(device, torch.float16) output = model.generate(**inputs, max_new_tokens=50) caption = processor.decode(output[0], skip_special_tokens=True) return caption # Function to save DPR text to a PDF file def save_dpr_to_pdf(dpr_text, filename): try: # Create a PDF document doc = SimpleDocTemplate(filename, pagesize=letter) styles = getSampleStyleSheet() # Define custom styles title_style = ParagraphStyle( name='Title', fontSize=16, leading=20, alignment=1, # Center spaceAfter=20, textColor=colors.black, fontName='Helvetica-Bold' ) body_style = ParagraphStyle( name='Body', fontSize=12, leading=14, spaceAfter=10, textColor=colors.black, fontName='Helvetica' ) # Build the PDF content flowables = [] # Add title flowables.append(Paragraph("Daily Progress Report", title_style)) # Split DPR text into lines and add as paragraphs for line in dpr_text.split('\n'): # Replace problematic characters for PDF line = line.replace('\u2019', "'").replace('\u2018', "'") if line.strip(): flowables.append(Paragraph(line, body_style)) else: flowables.append(Spacer(1, 12)) # Build the PDF doc.build(flowables) return f"PDF saved successfully as {filename}", filename except Exception as e: return f"Error saving PDF: {str(e)}", None # Function to upload a file to Salesforce as ContentVersion def upload_file_to_salesforce(file_path, filename, sf_connection, file_type): try: # Read file content and encode in base64 with open(file_path, 'rb') as f: file_content = f.read() file_content_b64 = base64.b64encode(file_content).decode('utf-8') # Set description based on file type description = "Daily Progress Report PDF" if file_type == "pdf" else "Site Image" # Create ContentVersion content_version = sf_connection.ContentVersion.create({ 'Title': filename, 'PathOnClient': filename, 'VersionData': file_content_b64, 'Description': description }) # Get ContentDocumentId content_version_id = content_version['id'] content_document = sf_connection.query( f"SELECT ContentDocumentId FROM ContentVersion WHERE Id = '{content_version_id}'" ) content_document_id = content_document['records'][0]['ContentDocumentId'] return content_document_id, f"File {filename} uploaded successfully" except Exception as e: return None, f"Error uploading {filename} to Salesforce: {str(e)}" # Function to generate the daily progress report (DPR), save as PDF, and upload to Salesforce def generate_dpr(files): dpr_text = [] captions = [] current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S") # Add header to the DPR dpr_text.append(f"Daily Progress Report\nGenerated on: {current_time}\n") # Process each uploaded file (image) for file in files: # Open the image from file path image = Image.open(file.name) if image.mode != "RGB": image = image.convert("RGB") # Dynamically generate a caption based on the image caption = generate_captions_from_image(image) captions.append(caption) # Generate DPR section for this image with dynamic caption dpr_section = f"\nImage: {file.name}\nDescription: {caption}\n" dpr_text.append(dpr_section) # Combine DPR text dpr_output = "\n".join(dpr_text) # Generate PDF filename with timestamp pdf_filename = f"DPR_{datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.pdf" # Save DPR text to PDF pdf_result, pdf_filepath = save_dpr_to_pdf(dpr_output, pdf_filename) # Salesforce upload salesforce_result = "" pdf_content_document_id = None image_content_document_ids = [] if sf and pdf_filepath: try: # Create Daily_Progress_Reports__c record report_description = "; ".join(captions)[:255] # Concatenate captions, limit to 255 chars dpr_record = sf.Daily_Progress_Reports__c.create({ 'Report_Description__c': report_description }) dpr_record_id = dpr_record['id'] salesforce_result += f"Created Daily_Progress_Reports__c record with ID: {dpr_record_id}\n" # Upload PDF to Salesforce pdf_content_document_id, pdf_upload_result = upload_file_to_salesforce( pdf_filepath, pdf_filename, sf, "pdf" ) salesforce_result += pdf_upload_result + "\n" # Link PDF to DPR record if pdf_content_document_id: sf.ContentDocumentLink.create({ 'ContentDocumentId': pdf_content_document_id, 'LinkedEntityId': dpr_record_id, 'ShareType': 'V' }) # Upload images to Salesforce for file in files: image_filename = os.path.basename(file.name) image_content_document_id, image_upload_result = upload_file_to_salesforce( file.name, image_filename, sf, "image" ) if image_content_document_id: image_content_document_ids.append(image_content_document_id) salesforce_result += image_upload_result + "\n" # Link image to DPR record if image_content_document_id: sf.ContentDocumentLink.create({ 'ContentDocumentId': image_content_document_id, 'LinkedEntityId': dpr_record_id, 'ShareType': 'V' }) except Exception as e: salesforce_result += f"Error interacting with Salesforce: {str(e)}\n" else: salesforce_result = "Salesforce connection not available or PDF generation failed.\n" # Return DPR text, PDF file, and Salesforce upload status return ( dpr_output + f"\n\n{pdf_result}\n\nSalesforce Upload Status:\n{salesforce_result}", pdf_filepath ) # Gradio interface for uploading multiple files, displaying DPR, and downloading PDF iface = gr.Interface( fn=generate_dpr, inputs=gr.Files(type="filepath", label="Upload Site Photos"), outputs=[ gr.Textbox(label="Daily Progress Report"), gr.File(label="Download PDF") ], title="Daily Progress Report Generator", description="Upload up to 10 site photos. The AI model will generate a text-based Daily Progress Report (DPR), save it as a PDF, and upload the PDF and images to Salesforce under Daily_Progress_Reports__c in the Files related list. Download the PDF locally if needed.", allow_flagging="never" ) if __name__ == "__main__": iface.launch()