init

README.md

@@ -1,8 +1,8 @@
 ---
 title: Large Dog Breed Classifier
-emoji: 🔥
-colorFrom: blue
-colorTo: green
+emoji: 🐶
+colorFrom: indigo
+colorTo: blue
 sdk: gradio
 sdk_version: 4.1.2
 app_file: app.py
@@ -11,3 +11,4 @@ license: apache-2.0
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+

app.py

@@ -0,0 +1,45 @@
+import spaces
+import gradio as gr
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+import torch
+from PIL import Image
+
+# Load the fine-tuned model
+model = AutoModelForImageClassification.from_pretrained("Pavarissy/ConvNextV2-large-DogBreed")
+
+# Initialize the image processor
+preprocessor = AutoImageProcessor.from_pretrained("Pavarissy/ConvNextV2-large-DogBreed")
+
+def classify_image(image):
+    # Preprocess the image
+    inputs = preprocessor(images=image, return_tensors="pt")
+
+    # Model prediction
+    with torch.no_grad():
+        logits = model(**inputs).logits
+
+    # Convert logits to probabilities
+    probs = logits.softmax(dim=-1)
+
+    # Extract top 5 predictions
+    top_5_probs, top_5_labels = torch.topk(probs, 5)
+    top_5_probs = top_5_probs.squeeze().tolist()
+    top_5_labels = top_5_labels.squeeze().tolist()
+
+    # Map labels to their names
+    labels = model.config.id2label
+    predicted_labels = [labels[label] for label in top_5_labels]
+
+    return dict(zip(predicted_labels, top_5_probs))
+
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=classify_image,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(num_top_classes=5),
+    title="Dog Breed Classifier",
+    description="Upload an image of a dog, and the model will predict the breed."
+)
+
+# Launch the interface
+iface.launch()

requirements.txt

@@ -0,0 +1,7 @@
+git+https://github.com/huggingface/transformers.git
+--extra-index-url https://download.pytorch.org/whl/cu113
+torch
+datasets
+accelerate
+numpy
+ochat