Create app.py

app.py

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+import tensorflow as tf
+import gradio as gr
+import numpy as np
+from PIL import Image
+
+# Load the model
+model = tf.saved_model.load('./saved_model')
+
+# Define the prediction function
+def predict(image):
+    # Preprocess the image to the required input format
+    img = np.array(image).astype(np.float32)
+    img = np.expand_dims(img, axis=0)  # Add batch dimension
+    img = tf.image.resize(img, (640, 640))  # Resize if needed
+
+    # Perform inference
+    predictions = model(img)
+    return predictions.numpy()  # Adjust output processing as needed
+
+# Set up the Gradio interface
+interface = gr.Interface(fn=predict, inputs=gr.inputs.Image(type="pil"), outputs="label")
+interface.launch()