Update app.py

app.py

@@ -1,10 +1,267 @@
+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, Image as PDFImage
+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
+import concurrent.futures
+
+# 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")
+    
+    # Resize image for faster processing (use smaller resolution to speed up inference)
+    image = image.resize((320, 320))  # Reduced size for faster processing
+    
+    # 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, image_paths, captions, 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 (excluding descriptions for images)
+        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))
+        
+        # Add images and captions in the correct order (no need to add description to dpr_text again)
+        for img_path, caption in zip(image_paths, captions):
+            try:
+                # Add image first
+                img = PDFImage(img_path, width=200, height=150)  # Adjust image size if needed
+                flowables.append(img)
+                # Add description below the image
+                description = f"Description: {caption}"
+                flowables.append(Paragraph(description, body_style))
+                flowables.append(Spacer(1, 12))  # Add some space between images
+            except Exception as e:
+                flowables.append(Paragraph(f"Error loading image: {str(e)}", body_style))
+        
+        # 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']
+        
+        # Generate a valid Salesforce URL for the ContentDocument
+        content_document_url = f"https://{sf_connection.sf_instance}/sfc/servlet.shepherd/version/download/{content_version_id}"
+    
+        # Ensure the link is valid
+        return content_document_id, content_document_url, f"File {filename} uploaded successfully"
+    except Exception as e:
+        return None, 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 = []
+    image_paths = []
+    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 images in parallel for faster performance
+    with concurrent.futures.ThreadPoolExecutor() as executor:
+        results = list(executor.map(lambda file: generate_captions_from_image(Image.open(file.name)), files))
+    
+    for i, file in enumerate(files):
+        caption = results[i]
+        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)
+        
+        # Save image path for embedding in the report
+        image_paths.append(file.name)
+    
+    # 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, image_paths, captions, pdf_filename)
+    
+    salesforce_result = ""
+    pdf_content_document_id = None
+    pdf_url = 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({
+                'Detected_Activities__c': report_description  # Store in Detected_Activities__c field
+            })
+            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_url, 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'
+                })
+            
+            # Update the DPR record with the PDF URL
+            if pdf_url:
+                sf.Daily_Progress_Reports__c.update(dpr_record_id, {
+                    'PDF_URL__c': pdf_url  # Storing the PDF URL correctly
+                })
+                salesforce_result += f"Updated PDF URL for record ID {dpr_record_id}\n"
+            
+            # Upload images to Salesforce and link them to DPR record
+            for file in files:
+                image_filename = os.path.basename(file.name)
+                image_content_document_id, image_url, image_upload_result = upload_file_to_salesforce(
+                    file.name, image_filename, sf, "image"
+                )
+                
+                if image_content_document_id:
+                    # Link image to the Daily Progress Report record (DPR) using ContentDocumentLink
+                    sf.ContentDocumentLink.create({
+                        'ContentDocumentId': image_content_document_id,
+                        'LinkedEntityId': dpr_record_id,  # Link image to DPR record
+                        'ShareType': 'V'  # 'V' means Viewer access
+                    })
+                    
+                    # Now, update the DPR record with the ContentDocumentId in the Site_Images field (if it's a text or URL field)
+                    sf.Daily_Progress_Reports__c.update(dpr_record_id, {
+                        'Site_Images__c': image_content_document_id  # Storing the ContentDocumentId directly
+                    })
+                    
+                    salesforce_result += image_upload_result + "\n"
+                        
+        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")],
+    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",
     show_api=False,
     show_tips=False
 )
+
+if __name__ == "__main__":
+    iface.launch()