Update app.py

app.py

@@ -1,25 +1,11 @@
-import tensorflow as tf
 import gradio as gr
-import numpy as np
 import os
 
-# Load the model
-model = tf.saved_model.load('.')
+def echo_image(image):
+    return image
 
-# 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().tolist()  # Adjust output processing as needed
-
-# Set up the Gradio interface
 image_input = gr.Image(type="pil")
-label_output = gr.Label(num_top_classes=3)
+image_output = gr.Image(type="pil")
 
-interface = gr.Interface(fn=predict, inputs=image_input, outputs=label_output)
-interface.launch(server_port=os.getenv('GRADIO_SERVER_PORT', 7860))  # Use environment variable for port
+interface = gr.Interface(fn=echo_image, inputs=image_input, outputs=image_output)
+interface.launch(server_port=os.getenv('GRADIO_SERVER_PORT', 7860))