Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+import numpy as np
+from tensorflow.keras.models import load_model
+from PIL import Image
+from sklearn.preprocessing import LabelEncoder
+
+# Load the trained model
+model = load_model("best_dog_breed_model.keras")  # ✅ match the actual file name
+
+# Load and process labels
+df = pd.read_csv("labels.csv")
+le = LabelEncoder()
+le.fit(df['breed'])
+breed_list = list(le.classes_)
+
+# Define prediction function
+def predict(image):
+    image = image.resize((224, 224))
+    img_array = np.expand_dims(np.array(image) / 255.0, axis=0)
+    preds = model.predict(img_array)[0]
+    top5_idx = preds.argsort()[-5:][::-1]
+    return {breed_list[i]: float(preds[i]) for i in top5_idx}
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(num_top_classes=5),
+    title="Dog Breed Classifier",
+    description="Upload a dog image to see the top 5 predicted breeds."
+)
+
+iface.launch()