app.py

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.pdfgen import canvas

# 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)

# Define categories for construction activities and materials
construction_terms = {
    "activities": ["pouring", "scaffolding", "building", "excavation", "piling", "digging", "cementing", "welding", "cutting", "assembling", "drilling"],
    "materials": ["concrete", "steel", "wood", "bricks", "cement", "sand", "mortar", "rebar", "plaster", "tiles"],
    "progress": ["completed", "ongoing", "in-progress", "starting", "finished", "under construction"]
}

# Function to detect activities and materials
def detect_construction_info(caption):
    activity_found = []
    material_found = []
    progress_found = []

    # Split the caption into words and check for the terms
    for word in caption.split():
        word_lower = word.lower()
        if word_lower in construction_terms["activities"]:
            activity_found.append(word)
        elif word_lower in construction_terms["materials"]:
            material_found.append(word)
        elif word_lower in construction_terms["progress"]:
            progress_found.append(word)
    
    # Build the informative output
    activity_str = ", ".join(activity_found) if activity_found else "No specific activities detected."
    material_str = ", ".join(material_found) if material_found else "No materials detected."
    progress_str = ", ".join(progress_found) if progress_found else "No progress information available."

    return f"Activities: {activity_str}\nMaterials: {material_str}\nProgress: {progress_str}"

# Function to generate the daily progress report
def generate_dpr(files):
    dpr_text = []
    current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
    
    # Add header to the PDF
    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)  # Using file.name for filepath
        
        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)

        # Get detailed construction information based on the caption
        detailed_caption = detect_construction_info(caption)
        
        # Generate DPR section for this image
        dpr_section = f"\nImage: {file.name}\n{detailed_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}")

    # Add the detailed captions for each image to the PDF
    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
iface = gr.Interface(
    fn=generate_dpr,
    inputs=gr.Files(type="filepath", label="Upload Site Photos"),  # Handle batch upload of images
    outputs="file",
    title="Daily Progress Report Generator",
    description="Upload up to 10 site photos. The AI model will detect construction activities, materials, and progress and generate a PDF report.",
    allow_flagging="never"  # Optional: Disable flagging
)

iface.launch()