Upload dogbreaddetection.py

dogbreaddetection.py

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+# -*- coding: utf-8 -*-
+"""DogBreadDetection.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1asSnC5KEvsnOmOzCEdX839PgCTd_zWfU
+"""
+
+'''
+The code is designed to identify dog breeds from uploaded images by leveraging a pretrained image classification model, such as VGG16 or ResNet, fine-tuned specifically for dog breed classification. This is achieved by using a Convolutional Neural Network (CNN) within TensorFlow or PyTorch frameworks. Additionally, Gradio is used to build a user-friendly web-based interface for easy image uploads and breed predictions.
+'''
+
+!pip install torch torchvision
+!pip install matplotlib
+!pip install gradio
+
+import numpy as np
+import torch
+import torchvision.models as models
+from PIL import Image
+import torchvision.transforms as transforms
+import requests
+import gradio as gr
+import os
+
+# Load pretrained VGG16 model
+VGG16 = models.vgg16(weights="IMAGENET1K_V1")
+use_cuda = torch.cuda.is_available()
+if use_cuda:
+    VGG16 = VGG16.cuda()
+
+# Global cache for labels
+LABELS_CACHE = None
+
+def prefetch_labels():
+    global LABELS_CACHE
+    LABELS_MAP_URL = "https://raw.githubusercontent.com/anishathalye/imagenet-simple-labels/master/imagenet-simple-labels.json"
+    try:
+        LABELS_CACHE = requests.get(LABELS_MAP_URL, timeout=5).json()
+    except requests.exceptions.RequestException as e:
+        LABELS_CACHE = None
+        print(f"Error fetching labels: {e}")
+
+# Fetch labels on startup
+prefetch_labels()
+
+def load_convert_image_to_tensor(image):
+    if isinstance(image, np.ndarray):
+        image = Image.fromarray(image.astype('uint8'), 'RGB')
+    elif isinstance(image, str):
+        image = Image.open(image).convert('RGB')
+
+    in_transform = transforms.Compose([
+        transforms.Resize(size=(224, 224)),
+        transforms.ToTensor()
+    ])
+    image = in_transform(image)[:3, :, :].unsqueeze(0)
+    return image
+
+def get_human_readable_label_for_class_id(class_id, labels_cache=None):
+    if labels_cache is None or class_id >= len(labels_cache):
+        return f"Unknown class ID: {class_id}"
+    return labels_cache[class_id]
+
+def classify_image(image, confidence_threshold=0.0):
+    global LABELS_CACHE
+    if LABELS_CACHE is None:
+        return "Error: Labels not loaded"
+
+    try:
+        image_tensor = load_convert_image_to_tensor(image)
+        if use_cuda:
+            image_tensor = image_tensor.cuda()
+
+        output = VGG16(image_tensor)
+        softmax_output = torch.nn.functional.softmax(output, dim=1)
+        top_probs, top_classes = torch.topk(softmax_output, 3)
+        top_probs = top_probs.cpu().detach().numpy() if use_cuda else top_probs.detach().numpy()
+        top_classes = top_classes.cpu().detach().numpy() if use_cuda else top_classes.detach().numpy()
+
+        result = {}
+        for prob, cls_id in zip(top_probs[0], top_classes[0]):
+            if prob >= confidence_threshold:
+                label = get_human_readable_label_for_class_id(int(cls_id), LABELS_CACHE)
+                result[label] = prob
+        return result if result else "No predictions above the confidence threshold."
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+# Gradio Interface
+image_input = gr.Image()
+confidence_slider = gr.Slider(0, 1, 0.0, label="Confidence Threshold (Optional)")  # Changed this line
+label_output = gr.Label(num_top_classes=3)
+
+interface = gr.Interface(fn=classify_image, inputs=[image_input, confidence_slider], outputs=label_output)
+
+# Launch Gradio with shareable link
+interface.launch(share=True)