Update app.py

app.py

@@ -1,6 +1,5 @@
 import gradio as gr
 import tensorflow as tf
-import numpy as np
 import gdown
 from PIL import Image
 
@@ -36,41 +35,22 @@ model = tf.keras.models.load_model(model_file)
 def predict_class(image):
     img = tf.cast(image, tf.float32)
     img = tf.image.resize(img, [input_shape[0], input_shape[1]])
-    img = np.expand_dims(img, axis=0)
+    img = tf.expand_dims(img, axis=0)
     prediction = model.predict(img)
-    return prediction[0]
+    class_index = tf.argmax(prediction[0]).numpy()
+    predicted_class = labels[class_index]
+    return predicted_class
 
 # UI Design
 def classify_image(image):
-    pred = predict_class(image)
-    class_names = [
-        "plane",
-        "car",
-        "bird",
-        "cat",
-        "deer",
-        "dog",
-        "frog",
-        "horse",
-        "ship",
-        "truck",
-    ]
-
-    probabilities = tf.nn.softmax(pred)
-    top_indices = tf.argsort(probabilities, direction='DESCENDING')
-    top_classes = [class_names[idx] for idx in top_indices]
-    top_probs = [probabilities[idx] for idx in top_indices]
-
-    output = "<h3>Top 3 Predictions:</h3>"
-    for i in range(3):
-        output += f"<p>{top_classes[i]}: {top_probs[i]*100:.2f}%</p>"
-
+    predicted_class = predict_class(image)
+    output = f"<h2>Predicted Class:</h2><p>{predicted_class}</p>"
     return output
 
 inputs = gr.inputs.Image(label="Upload an image")
 outputs = gr.outputs.HTML()
 
 title = "<h1 style='text-align: center;'>Image Classifier</h1>"
-description = "Upload an image and get the top 3 predictions."
+description = "Upload an image and get the predicted class."
 
-gr.Interface(fn=classify_image, inputs=inputs, outputs=outputs, title=title, description=description).launch()
+gr.Interface(fn=classify_image, inputs=inputs, outputs=outputs, title=title, description=description).launch()