Update app.py

app.py

@@ -2,8 +2,6 @@ import cv2
 import torch
 import gradio as gr
 import numpy as np
-from ultralytics import YOLO
-import time
 import os
 import json
 import logging
@@ -11,6 +9,12 @@ import matplotlib.pyplot as plt
 from datetime import datetime
 from collections import Counter
 from typing import List, Dict, Any, Optional
+from ultralytics import YOLO
+import ultralytics
+import time
+
+# Set YOLO config directory early to avoid warning
+os.environ["YOLO_CONFIG_DIR"] = "/tmp/Ultralytics"
 
 # Set up logging
 logging.basicConfig(
@@ -38,7 +42,7 @@ frame_count: int = 0
 # Debug: Check environment
 print(f"Torch version: {torch.__version__}")
 print(f"Gradio version: {gr.__version__}")
-print(f"Ultralytics version: {YOLO.__version__}")
+print(f"Ultralytics version: {ultralytics.__version__}")  # Fixed: Use ultralytics.__version__
 print(f"CUDA available: {torch.cuda.is_available()}")
 
 # Load custom YOLO model
@@ -49,9 +53,8 @@ if device == "cuda":
     model.half()  # Use half-precision (FP16)
 print(f"Model classes: {model.names}")
 
-# Mock service functions (replace with actual implementations if available)
+# Mock service functions
 def generate_map(gps_coords: List[List[float]], items: List[Dict[str, Any]]) -> str:
-    """Mock map generation: returns a placeholder image path."""
     map_path = "map_temp.png"
     plt.figure(figsize=(4, 4))
     plt.scatter([x[1] for x in gps_coords], [x[0] for x in gps_coords], c='blue', label='GPS Points')
@@ -64,11 +67,9 @@ def generate_map(gps_coords: List[List[float]], items: List[Dict[str, Any]]) ->
     return map_path
 
 def send_to_salesforce(data: Dict[str, Any]) -> None:
-    """Mock Salesforce dispatch: logs data."""
     logging.info(f"Mock Salesforce dispatch: {json.dumps(data, indent=2)}")
 
 def update_metrics(detections: List[Dict[str, Any]]) -> Dict[str, Any]:
-    """Compute detection metrics."""
     counts = Counter([det["label"] for det in detections])
     return {
         "items": [{"type": k, "count": v} for k, v in counts.items()],
@@ -77,7 +78,6 @@ def update_metrics(detections: List[Dict[str, Any]]) -> Dict[str, Any]:
     }
 
 def generate_line_chart() -> Optional[str]:
-    """Generate detection trend chart."""
     if not detected_counts:
         return None
     plt.figure(figsize=(4, 2))
@@ -148,7 +148,7 @@ def process_video(video, resize_width=320, resize_height=240, frame_skip=5):
         for detection in results[0].boxes:
             cls = int(detection.cls)
             conf = float(detection.conf)
-            box = detection.xyxy[0].cpu().numpy().astype(int).tolist()  # [x_min, y_min, x_max, y_max]
+            box = detection.xyxy[0].cpu().numpy().astype(int).tolist()
             label = model.names[cls]
             frame_detections.append({"label": label, "box": box, "conf": conf})
             log_entries.append(f"Frame {frame_count}: Detected {label} with confidence {conf:.2f}")
@@ -164,7 +164,7 @@ def process_video(video, resize_width=320, resize_height=240, frame_skip=5):
         frame_path = os.path.join(OUTPUT_DIR, f"frame_{frame_count:04d}.jpg")
         cv2.imwrite(frame_path, annotated_frame)
 
-        gps_coord = [17.385044 + (frame_count * 0.0001), 78.486671 + (frame_count * 0.0001)]  # Simulated GPS
+        gps_coord = [17.385044 + (frame_count * 0.0001), 78.486671 + (frame_count * 0.0001)]
         gps_coordinates.append(gps_coord)
         for det in frame_detections:
             det["gps"] = gps_coord