Update app.py

app.py

@@ -3,6 +3,10 @@ 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
 
 # Load BLIP model and processor
 processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
@@ -23,7 +27,54 @@ def generate_captions_from_image(image):
     
     return caption
 
-# Function to generate the daily progress report (DPR) text
+# 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}"
+    except Exception as e:
+        return f"Error saving PDF: {str(e)}"
+
+# Function to generate the daily progress report (DPR) text and save as PDF
 def generate_dpr(files):
     dpr_text = []
     current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
@@ -45,9 +96,18 @@ def generate_dpr(files):
         # Generate DPR section for this image with dynamic caption
         dpr_section = f"\nImage: {file.name}\nDescription: {caption}\n"
         dpr_text.append(dpr_section)
-
-    # Return the generated DPR as a text output
-    return "\n".join(dpr_text)
+    
+    # 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 = save_dpr_to_pdf(dpr_output, pdf_filename)
+    
+    # Return the DPR text and PDF save status
+    return f"{dpr_output}\n\n{pdf_result}"
 
 # Gradio interface for uploading multiple files and displaying the text-based DPR
 iface = gr.Interface(
@@ -55,8 +115,9 @@ iface = gr.Interface(
     inputs=gr.Files(type="filepath", label="Upload Site Photos"),  # Handle batch upload of images
     outputs="text",  # Display the DPR as text in the output section
     title="Daily Progress Report Generator",
-    description="Upload up to 10 site photos. The AI model will dynamically detect construction activities, materials, and progress and generate a text-based Daily Progress Report (DPR).",
+    description="Upload up to 10 site photos. The AI model will dynamically detect construction activities, materials, and progress and generate a text-based Daily Progress Report (DPR). The DPR will also be saved as a PDF.",
     allow_flagging="never"  # Optional: Disable flagging
 )
 
-iface.launch()
+if __name__ == "__main__":
+    iface.launch()