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| from PIL import Image | |
| import gradio as gr | |
| import torch | |
| from datetime import datetime | |
| from ultralytics import YOLO | |
| torch.serialization.add_safe_globals([torch.nn.Module, 'ultralytics.nn.tasks.DetectionModel']) | |
| # Load YOLOv8 model (trained on construction dataset) | |
| model = YOLO('yolov8n.pt') # Path to pre-trained model on construction dataset | |
| # Function to generate DPR text based on detections | |
| def generate_dpr(files): | |
| dpr_text = [] | |
| 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 each uploaded file (image) | |
| for file in files: | |
| # Open the image from file path | |
| image = Image.open(file.name) | |
| # Perform object detection with YOLOv8 | |
| results = model(image) # Perform detection | |
| # Parse detections (activities, materials, etc.) | |
| detected_objects = results.names # Object names detected by the model | |
| detections = results.pandas().xywh # Get the dataframe with detection results | |
| detected_activities = [] | |
| detected_materials = [] | |
| # Define construction activity and material categories | |
| construction_activities = ['scaffolding', 'concrete pouring', 'welding', 'excavation'] | |
| construction_materials = ['concrete', 'steel', 'bricks', 'cement', 'sand'] | |
| # Check the detected objects and categorize them | |
| for obj in detected_objects: | |
| if obj.lower() in construction_activities: | |
| detected_activities.append(obj) | |
| elif obj.lower() in construction_materials: | |
| detected_materials.append(obj) | |
| # Build a detailed report for this image | |
| dpr_section = f"\nImage: {file.name}\n" | |
| if detected_activities: | |
| dpr_section += f"Detected Activities: {', '.join(detected_activities)}\n" | |
| else: | |
| dpr_section += "No construction activities detected.\n" | |
| if detected_materials: | |
| dpr_section += f"Detected Materials: {', '.join(detected_materials)}\n" | |
| else: | |
| dpr_section += "No materials detected.\n" | |
| dpr_text.append(dpr_section) | |
| # Return the generated DPR as a text output | |
| return "\n".join(dpr_text) | |
| # 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="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 detect construction activities, materials, and progress and generate a text-based Daily Progress Report (DPR).", | |
| allow_flagging="never" # Optional: Disable flagging | |
| ) | |
| iface.launch() | |