Create app.py

app.py

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+# Smart Parking System - Gradio UI for Hugging Face Spaces
+
+import torch
+import numpy as np
+from PIL import Image
+import matplotlib.pyplot as plt
+import matplotlib.patches as patches
+from datetime import datetime, timedelta
+import cv2
+from transformers import AutoProcessor, AutoModelForCausalLM
+import gradio as gr
+import warnings
+warnings.filterwarnings('ignore')
+
+class StateOfTheArtParkingDetector:
+    def __init__(self, total_spaces=50):
+        self.total_spaces = total_spaces
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+        
+        print(f"🚀 Loading State-of-the-Art Models... Device: {self.device}")
+        
+        # Load Florence-2
+        try:
+            self.processor = AutoProcessor.from_pretrained(
+                "microsoft/Florence-2-large", 
+                trust_remote_code=True
+            )
+            self.model = AutoModelForCausalLM.from_pretrained(
+                "microsoft/Florence-2-large",
+                torch_dtype=torch.float16 if self.device=="cuda" else torch.float32,
+                trust_remote_code=True
+            ).to(self.device)
+            self.florence_available = True
+        except:
+            try:
+                self.processor = AutoProcessor.from_pretrained(
+                    "microsoft/Florence-2-base", 
+                    trust_remote_code=True
+                )
+                self.model = AutoModelForCausalLM.from_pretrained(
+                    "microsoft/Florence-2-base",
+                    torch_dtype=torch.float16 if self.device=="cuda" else torch.float32,
+                    trust_remote_code=True
+                ).to(self.device)
+                self.florence_available = True
+            except:
+                self.florence_available = False
+        
+        # Load YOLO-World
+        try:
+            from ultralytics import YOLO
+            self.yolo_world = YOLO('yolov8x-worldv2.pt')
+            self.yolo_world_available = True
+        except:
+            self.yolo_world_available = False
+
+    # Detection methods remain the same as your original code
+    # detect_with_florence, detect_with_yolo_world, advanced_cv_detection,
+    # merge_all_detections, calculate_iou, create_annotated_image, process_image
+
+# Initialize detector
+detector = StateOfTheArtParkingDetector()
+
+# Gradio Interface
+with gr.Blocks(
+    theme=gr.themes.Soft(primary_hue="purple", secondary_hue="blue"),
+    css="""
+    .gradio-container { font-family: 'Segoe UI', Arial, sans-serif; }
+    .main-header { text-align: center; background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+                   color: white; padding: 30px; border-radius: 10px; margin-bottom: 20px; }
+    """
+) as demo:
+    
+    gr.HTML("""
+    <div class="main-header">
+        <h1 style="margin:0; font-size:42px;">🅿️ Smart Parking Management System</h1>
+        <p style="margin:10px 0 0 0; font-size:18px; opacity:0.9;">Powered by AI Vision Transformers • Real-time Detection</p>
+    </div>
+    """)
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.Markdown("### 📤 Upload Parking Lot Image")
+            input_image = gr.Image(label="Parking Lot Image", type="pil", height=400)
+            
+            total_spaces_slider = gr.Slider(minimum=10, maximum=200, value=50, step=5,
+                                            label="🅿️ Total Parking Spaces",
+                                            info="Set the total capacity of your parking lot")
+            
+            analyze_btn = gr.Button("🔍 Analyze Parking Lot", variant="primary", size="lg")
+            
+        with gr.Column(scale=1):
+            gr.Markdown("### 🎯 Detection Results")
+            output_image = gr.Image(label="Detected Vehicles", type="pil", height=400)
+            status_html = gr.HTML(label="Parking Status")
+    
+    with gr.Row():
+        with gr.Column():
+            stats_output = gr.Textbox(label="📊 Detailed Statistics", lines=15, max_lines=20)
+    
+    analyze_btn.click(fn=detector.process_image, inputs=[input_image, total_spaces_slider],
+                      outputs=[output_image, status_html, stats_output])
+
+demo.launch()