Create app.py

app.py

@@ -1,265 +1,10 @@
-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"
+    allow_flagging="never",
+    show_api=False,
+    show_tips=False
 )
-
-if __name__ == "__main__":
-    iface.launch()