Create app.py

app.py

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+import gradio as gr
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+from PIL import Image
+import torch
+
+# Load your model and processor (replace with your model’s Hugging Face ID or local path)
+model_name = "your-username/your-bird-classifier"  # e.g., "dennisjooo/Birds-Classifier-EfficientNetB2"
+processor = AutoImageProcessor.from_pretrained(model_name)
+model = AutoModelForImageClassification.from_pretrained(model_name)
+
+# Prediction function
+def classify_bird(image):
+    # Process the image
+    inputs = processor(image, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**inputs).logits
+    # Get the predicted label
+    predicted_idx = outputs.argmax(-1).item()
+    label = model.config.id2label[predicted_idx]
+    return f"Predicted bird species: {label}"
+
+# Create the Gradio interface
+interface = gr.Interface(
+    fn=classify_bird,
+    inputs=gr.Image(type="pil", label="Upload a bird image"),
+    outputs=gr.Textbox(label="Prediction"),
+    title="Bird Species Classifier",
+    description="Upload an image of a bird to identify its species!"
+)
+
+# Launch the app
+interface.launch()