Update app.py

app.py

@@ -1,56 +1,34 @@
-import numpy as np
 import gradio as gr
+import pandas as pd
+import numpy as np
+from tensorflow.keras.models import load_model
 from PIL import Image
-import tensorflow as tf
-
-# Load your model here
-model = tf.keras.models.load_model('your_model.h5')
-
-# List of classes your model predicts
-class_names = ['beagle', 'bulldog', 'golden_retriever', 'toy', 'human']  # add all your classes here
-
-# Classes to flag as unknown
-unknown_classes = {'toy', 'human'}
-
-# Image size expected by your model
-IMAGE_SIZE = (224, 224)
-
-def preprocess_image(img):
-    img = img.convert("RGB").resize(IMAGE_SIZE)
-    img = np.array(img) / 255.0
-    img = np.expand_dims(img, axis=0)
-    return img
-
-def predict(img):
-    img_processed = preprocess_image(img)
-    preds = model.predict(img_processed)[0]  # assuming batch size 1
-    top5_indices = preds.argsort()[-5:][::-1]
-    top5_preds = [(class_names[i], preds[i]) for i in top5_indices]
-
-    # Check unknown classes in top 5
-    if any(cls in unknown_classes for cls, conf in top5_preds):
-        return "Unknown class detected, unable to classify reliably."
-
-    # Final prediction with threshold (e.g. 90%)
-    final_idx = np.argmax(preds)
-    confidence = preds[final_idx]
-    if confidence < 0.9:
-        return "Confidence too low to predict reliably."
-
-    final_breed = class_names[final_idx]
-    result_str = f"Final Prediction: {final_breed} (Confidence: {confidence:.2f})\n\nTop 5 predictions:\n"
-    for cls, conf in top5_preds:
-        result_str += f"{cls}: {conf:.2f}\n"
-
-    return result_str
-
+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.Textbox(),
+    outputs=gr.Label(num_top_classes=5),
     title="Dog Breed Classifier",
-    description="Upload an image and see top 5 predictions and final breed with threshold and unknown detection."
+    description="Upload a dog image to see the top 5 predicted breeds."
 )
 
-if __name__ == "__main__":
-    iface.launch()
+iface.launch()