Update app.py

app.py

@@ -1,129 +1,54 @@
+# Remove unnecessary OpenCV imports and conversions
 import gradio as gr
 from ultralyticsplus import YOLO, render_result
-import cv2
+import numpy as np
 import time
 import torch
 
-# --------------------------
-# System Checks & Optimization
-# --------------------------
+# System checks
 print("\n" + "="*40)
-print("System Configuration Check:")
-print(f"PyTorch Version: {torch.__version__}")
-print(f"CUDA Available: {torch.cuda.is_available()}")
-print(f"CUDA Device Count: {torch.cuda.device_count()}")
-if torch.cuda.is_available():
-    print(f"Using GPU: {torch.cuda.get_device_name(0)}")
-else:
-    print("Using CPU - For better performance, consider using a GPU environment")
+print(f"PyTorch: {torch.__version__}")
+print(f"CUDA: {torch.cuda.is_available()}")
 print("="*40 + "\n")
 
-# --------------------------
-# Model Configuration
-# --------------------------
-# Load model with performance optimizations
+# Load model
 model = YOLO('foduucom/plant-leaf-detection-and-classification')
-
-# Configure model parameters
-model_params = {
+model.overrides.update({
     'conf': 0.25,
     'iou': 0.45,
     'imgsz': 640,
     'device': 'cuda' if torch.cuda.is_available() else 'cpu',
-    'half': True if torch.cuda.is_available() else False  # FP16 acceleration
-}
-model.overrides.update(model_params)
-
-# Warmup model with dummy input
-print("Performing model warmup...")
-dummy_input = torch.randn(1, 3, 640, 640).to(model_params['device'])
-if model_params['half']:
-    dummy_input = dummy_input.half()
-model.predict(dummy_input, verbose=False)
-print("Model warmup complete!\n")
+    'half': torch.cuda.is_available()
+})
 
-# --------------------------
-# Image Processing Pipeline
-# --------------------------
-def preprocess_image(image):
-    """Optimized image preprocessing"""
-    # Convert RGB to BGR
-    img = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
-    
-    # Resize maintaining aspect ratio
-    max_size = 1280
-    h, w = img.shape[:2]
-    scale = min(max_size/h, max_size/w)
-    img = cv2.resize(img, (int(w*scale), int(h*scale)), 
-                    interpolation=cv2.INTER_LINEAR)
-    return img
-
-# --------------------------
-# Detection Function
-# --------------------------
 def detect_leaves(image):
     try:
         start_time = time.time()
         
-        # Step 1: Preprocessing
-        preprocess_start = time.time()
-        img = preprocess_image(image)
-        print(f"Preprocessing time: {time.time() - preprocess_start:.2f}s")
-        
-        # Step 2: Prediction
-        predict_start = time.time()
+        # Directly use numpy array
         results = model.predict(
-            source=img,
+            source=image,
             verbose=False,
-            stream=False,  # Disable streaming mode
-            augment=False   # Disable TTA for speed
+            stream=False
         )
-        print(f"Prediction time: {time.time() - predict_start:.2f}s")
         
-        # Step 3: Postprocessing
-        postprocess_start = time.time()
         num_leaves = len(results[0].boxes)
-        rendered_img = render_result(model=model, image=img, result=results[0])
-        rendered_img = cv2.cvtColor(rendered_img, cv2.COLOR_BGR2RGB)
-        print(f"Postprocessing time: {time.time() - postprocess_start:.2f}s")
-        
-        total_time = time.time() - start_time
-        print(f"\nTotal processing time: {total_time:.2f}s")
-        print(f"Detected leaves: {num_leaves}")
-        print("-"*50)
+        rendered_img = render_result(model=model, image=image, result=results[0])
         
+        print(f"Processing time: {time.time()-start_time:.2f}s")
         return rendered_img, num_leaves
     
     except Exception as e:
-        print(f"Error processing image: {str(e)}")
+        print(f"Error: {str(e)}")
         return None, 0
 
-# --------------------------
-# Gradio Interface
-# --------------------------
-with gr.Blocks(title="Leaf Detection", theme=gr.themes.Soft()) as demo:
-    gr.Markdown("# 🍃 Real-Time Plant Leaf Detection")
-    gr.Markdown("Upload a plant image to analyze leaf count and health")
-    
-    with gr.Row():
-        input_image = gr.Image(label="Input Image", type="numpy")
-        output_image = gr.Image(label="Detection Results", width=600)
-    
-    with gr.Row():
-        leaf_count = gr.Number(label="Detected Leaves", precision=0)
-        process_btn = gr.Button("Analyze Image", variant="primary")
-    
-    progress = gr.Textbox(label="Processing Status", visible=True)
-    
-    process_btn.click(
-        fn=detect_leaves,
-        inputs=[input_image],
-        outputs=[output_image, leaf_count]
-    )
+# Simplified interface
+interface = gr.Interface(
+    fn=detect_leaves,
+    inputs=gr.Image(),
+    outputs=[gr.Image(), gr.Number()],
+    title="🍃 Leaf Detector"
+)
 
 if __name__ == "__main__":
-    demo.launch(
-        server_port=7860,
-        show_error=True,
-        share=False
-    )
+    interface.launch(server_port=7860)