Update app.py

app.py

@@ -6,6 +6,7 @@ import os
 import json
 import logging
 import matplotlib.pyplot as plt
+import csv
 from datetime import datetime
 from collections import Counter
 from typing import List, Dict, Any, Optional
@@ -15,6 +16,14 @@ import piexif
 import zipfile
 from fpdf import FPDF  # For PDF generation
 
+# Set YOLO config directory (ensure settings are created only once)
+settings_path = '/home/user/.config/Ultralytics/settings.json'
+
+# Check if the settings file exists, if not, create it
+if not os.path.exists(settings_path):
+    os.makedirs(os.path.dirname(settings_path), exist_ok=True)
+    logging.info(f"Creating new Ultralytics settings file at {settings_path}")
+
 # Set up logging
 logging.basicConfig(
     filename="app.log",
@@ -34,10 +43,12 @@ os.chmod(OUTPUT_DIR, 0o777)
 os.chmod(FLIGHT_LOG_DIR, 0o777)
 
 # Force CPU mode for Hugging Face Spaces
-device = 'cpu'  
-model = YOLO('./data/best.pt').to(device)
-if device == "cpu":  
-    model.float()
+device = 'cpu'  # Ensure we are using CPU
+logging.info(f"Using device: {device}")
+
+# Load the model with the device set to CPU
+model = YOLO('./data/best.pt').to(device)  # Ensure model is moved to CPU if necessary
+model.float()  # Ensure model is using full precision (on CPU)
 
 # Global variables
 log_entries: List[str] = []
@@ -50,6 +61,12 @@ SAVE_IMAGE_INTERVAL = 1
 # Detection classes
 DETECTION_CLASSES = ["Longitudinal", "Pothole", "Transverse"]
 
+# Debug: Check environment
+print(f"Torch version: {torch.__version__}")
+print(f"Gradio version: {gr.__version__}")
+print(f"Ultralytics version: {ultralytics.__version__}")
+print(f"CUDA available: {torch.cuda.is_available()}")
+
 # Check image resolution quality
 def check_image_quality(frame: np.ndarray, input_resolution: int) -> bool:
     height, width, _ = frame.shape
@@ -374,3 +391,51 @@ def process_video(video, resize_width=4000, resize_height=3000, frame_skip=5):
         logs_zip,
         output_path  # For video download
     )
+
+# Gradio interface
+with gr.Blocks(theme=gr.themes.Soft(primary_hue="orange")) as iface:
+    gr.Markdown("# NHAI Road Defect Detection Dashboard")
+    with gr.Row():
+        with gr.Column(scale=3):
+            video_input = gr.Video(label="Upload Video (12MP recommended for NHAI compliance)")
+            width_slider = gr.Slider(320, 4000, value=4000, label="Output Width", step=1)
+            height_slider = gr.Slider(240, 3000, value=3000, label="Output Height", step=1)
+            skip_slider = gr.Slider(1, 10, value=5, label="Frame Skip", step=1)
+            process_btn = gr.Button("Process Video", variant="primary")
+        with gr.Column(scale=1):
+            metrics_output = gr.Textbox(label="Detection Metrics", lines=5, interactive=False)
+    with gr.Row():
+        video_output = gr.Video(label="Processed Video")
+        issue_gallery = gr.Gallery(label="Detected Issues", columns=4, height="auto", object_fit="contain")
+    with gr.Row():
+        chart_output = gr.Image(label="Detection Trend")
+        map_output = gr.Image(label="Issue Locations Map")
+    with gr.Row():
+        logs_output = gr.Textbox(label="Logs", lines=5, interactive=False)
+    with gr.Row():
+        gr.Markdown("## Download Results")
+    with gr.Row():
+        json_download = gr.File(label="Download Data Lake JSON")
+        images_zip_download = gr.File(label="Download Geotagged Images (ZIP)")
+        logs_zip_download = gr.File(label="Download Flight Logs (ZIP)")
+        video_download = gr.File(label="Download Processed Video")
+
+    process_btn.click(
+        fn=process_video,
+        inputs=[video_input, width_slider, height_slider, skip_slider],
+        outputs=[
+            video_output,
+            metrics_output,
+            logs_output,
+            issue_gallery,
+            chart_output,
+            map_output,
+            json_download,
+            images_zip_download,
+            logs_zip_download,
+            video_download
+        ]
+    )
+
+if __name__ == "__main__":
+    iface.launch()