Update app.py

app.py

@@ -3,8 +3,6 @@ from PIL import Image
 import gradio as gr
 import torch
 from datetime import datetime
-from reportlab.lib.pagesizes import letter
-from reportlab.pdfgen import canvas
 
 # Load BLIP model and processor
 processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
@@ -13,7 +11,7 @@ model.eval()
 device = "cuda" if torch.cuda.is_available() else "cpu"
 model.to(device)
 
-# Inference function to generate captions from images dynamically
+# Inference function to generate captions dynamically based on image content
 def generate_captions_from_image(image):
     if image.mode != "RGB":
         image = image.convert("RGB")
@@ -25,12 +23,12 @@ def generate_captions_from_image(image):
     
     return caption
 
-# Function to generate the daily progress report
+# Function to generate the daily progress report (DPR) text
 def generate_dpr(files):
     dpr_text = []
     current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
     
-    # Add header to the PDF
+    # Add header to the DPR
     dpr_text.append(f"Daily Progress Report\nGenerated on: {current_time}\n")
     
     # Process each uploaded file (image)
@@ -48,32 +46,16 @@ def generate_dpr(files):
         dpr_section = f"\nImage: {file.name}\nDescription: {caption}\n"
         dpr_text.append(dpr_section)
 
-    # Generate a PDF report
-    pdf_path = "dpr_report.pdf"
-    c = canvas.Canvas(pdf_path, pagesize=letter)
-    c.drawString(100, 750, "Daily Progress Report")
-    c.drawString(100, 730, f"Generated on: {current_time}")
+    # Return the generated DPR as a text output
+    return "\n".join(dpr_text)
 
-    # Add the detailed captions for each image to the PDF (in text format)
-    y_position = 700
-    for section in dpr_text:
-        c.drawString(100, y_position, section)
-        y_position -= 100  # Move down for the next section
-        if y_position < 100:
-            c.showPage()
-            y_position = 750
-
-    c.save()
-
-    return pdf_path
-
-# Gradio interface for uploading multiple files
+# Gradio interface for uploading multiple files and displaying the text-based DPR
 iface = gr.Interface(
     fn=generate_dpr,
     inputs=gr.Files(type="filepath", label="Upload Site Photos"),  # Handle batch upload of images
-    outputs="file",
+    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 PDF report.",
+    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).",
     allow_flagging="never"  # Optional: Disable flagging
 )