create app.py

app.py

@@ -0,0 +1,143 @@
+import torch 
+import torchvision 
+from torchvision.models.detection import FasterRCNN_ResNet50_FPN_Weights 
+from PIL import Image 
+import numpy as np 
+import matplotlib.pyplot as plt 
+import gradio as gr 
+import os 
+import sys
+ 
+# Load the pre-trained model once 
+model = torchvision.models.detection.fasterrcnn_resnet50_fpn(weights=FasterRCNN_ResNet50_FPN_Weights.DEFAULT) 
+model.eval() 
+ 
+# COCO class names 
+COCO_INSTANCE_CATEGORY_NAMES = [ 
+    '__background__', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 
+    'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'N/A', 'stop sign', 
+    'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 
+    'elephant', 'bear', 'zebra', 'giraffe', 'N/A', 'backpack', 'umbrella', 'N/A', 'N/A', 
+    'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 
+    'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 
+    'bottle', 'N/A', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 
+    'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 
+    'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'N/A', 'dining table', 
+    'N/A', 'N/A', 'toilet', 'N/A', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 
+    'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'N/A', 'book', 
+    'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush' 
+] 
+
+# Gradio-compatible detection function 
+def detect_objects(image, threshold=0.5): 
+    if image is None:
+        print("Image is None, returning empty output", file=sys.stderr)
+        # Create a blank image as output
+        blank_img = Image.new('RGB', (400, 400), color='white')
+        plt.figure(figsize=(10, 10))
+        plt.imshow(blank_img)
+        plt.text(0.5, 0.5, "No image provided", 
+                 horizontalalignment='center', verticalalignment='center',
+                 transform=plt.gca().transAxes, fontsize=20)
+        plt.axis('off')
+        output_path = "blank_output.png"
+        plt.savefig(output_path)
+        plt.close()
+        return output_path
+        
+    try:
+        print(f"Processing image of type {type(image)} and threshold {threshold}", file=sys.stderr)
+        # Make sure threshold is a valid number
+        if threshold is None:
+            threshold = 0.5
+            print("Threshold was None, using default 0.5", file=sys.stderr)
+        
+        # Convert threshold to float if it's not already
+        threshold = float(threshold)
+        
+        transform = FasterRCNN_ResNet50_FPN_Weights.DEFAULT.transforms() 
+        image_tensor = transform(image).unsqueeze(0) 
+     
+        with torch.no_grad(): 
+            prediction = model(image_tensor)[0] 
+     
+        boxes = prediction['boxes'].cpu().numpy() 
+        labels = prediction['labels'].cpu().numpy() 
+        scores = prediction['scores'].cpu().numpy() 
+     
+        image_np = np.array(image) 
+        plt.figure(figsize=(10, 10)) 
+        plt.imshow(image_np) 
+        ax = plt.gca() 
+     
+        for box, label, score in zip(boxes, labels, scores): 
+            # Explicit debug prints to trace the comparison issue
+            print(f"Score: {score}, Threshold: {threshold}, Type: {type(score)}/{type(threshold)}", file=sys.stderr)
+            
+            if score >= threshold: 
+                x1, y1, x2, y2 = box 
+                ax.add_patch(plt.Rectangle((x1, y1), x2 - x1, y2 - y1, 
+                             fill=False, color='red', linewidth=2)) 
+                class_name = COCO_INSTANCE_CATEGORY_NAMES[label] 
+                ax.text(x1, y1, f'{class_name}: {score:.2f}', bbox=dict(facecolor='yellow', alpha=0.5), 
+                        fontsize=12, color='black') 
+     
+        plt.axis('off') 
+        plt.tight_layout() 
+         
+        # Save the figure to return 
+        output_path = "output.png"
+        plt.savefig(output_path) 
+        plt.close() 
+        return output_path
+    except Exception as e:
+        print(f"Error in detect_objects: {e}", file=sys.stderr)
+        import traceback
+        traceback.print_exc(file=sys.stderr)
+        
+        # Create an error image
+        error_img = Image.new('RGB', (400, 400), color='white')
+        plt.figure(figsize=(10, 10))
+        plt.imshow(error_img)
+        plt.text(0.5, 0.5, f"Error: {str(e)}", 
+                 horizontalalignment='center', verticalalignment='center',
+                 transform=plt.gca().transAxes, fontsize=12, wrap=True)
+        plt.axis('off')
+        error_path = "error_output.png"
+        plt.savefig(error_path)
+        plt.close()
+        return error_path
+
+# Create direct file paths for examples
+# These exact filenames match what's visible in your repository
+examples = [
+    os.path.join("/home/user/app", "TEST_IMG_1.jpg"),
+    os.path.join("/home/user/app", "TEST_IMG_2.JPG"),
+    os.path.join("/home/user/app", "TEST_IMG_3.jpg"),
+    os.path.join("/home/user/app", "TEST_IMG_4.jpg")
+]
+
+# Create Gradio interface
+# Important: For Gradio examples, we need to create a list of lists
+example_list = [[path] for path in examples if os.path.exists(path)]
+
+print(f"Found {len(example_list)} valid examples: {example_list}", file=sys.stderr)
+
+# Create Gradio interface with a simplified approach
+interface = gr.Interface( 
+    fn=detect_objects, 
+    inputs=[
+        gr.Image(type="pil", label="Input Image"),
+        gr.Slider(minimum=0.0, maximum=1.0, value=0.5, step=0.05, label="Confidence Threshold")
+    ],
+    outputs=gr.Image(type="filepath", label="Detected Objects"),
+    title="Faster R-CNN Object Detection",
+    description="Upload an image to detect objects using a pretrained Faster R-CNN model.",
+    examples=example_list,
+    cache_examples=False  # Disable caching to avoid potential issues
+)
+
+# Launch with specific configuration for Hugging Face
+if __name__ == "__main__":
+    # Launch with debug mode enabled
+    interface.launch(debug=True)