Upload 3 files

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+
+
+model_path = "iris_mlp.weights.h5"
+model = tf.keras.Sequential([
+    tf.keras.layers.InputLayer(input_shape=[4]),
+    tf.keras.layers.BatchNormalization(),
+    tf.keras.layers.Dense(32, activation="relu"),
+    tf.keras.layers.Dense(16, activation="relu"),
+    tf.keras.layers.Dense(3, activation="softmax")
+])
+model.load_weights(model_path)
+
+labels = ['Setosa', 'Versicolour', 'Virginica']
+
+# Define the core prediction function
+def predict_iris(sepal_length, sepal_width, petal_length, petal_width):
+    features = [sepal_length, sepal_width, petal_length, petal_width]
+    features = np.array(features)[None, ...]
+    prediction = model.predict(features)
+    print(prediction)
+    confidences = {labels[i]: np.round(float(prediction[0][i]), 2) for i in range(len(labels))}
+    return confidences
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=predict_iris,
+    inputs=["number", "number", "number", "number"],
+    outputs=gr.Label(),
+    examples=[[7.7, 2.6, 6.9, 2.3]]
+)
+
+iface.launch()

iris_mlp.weights.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a4dd6c3813f72c9a26f660bfd898100dd26d4ed68a803cad7063361c5a5dc726
+size 33792

requirements.txt

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+tensorflow