Update app.py

app.py

@@ -2,7 +2,6 @@ import gradio as gr
 import torch
 from PIL import Image
 from pathlib import Path
-
 from main import load_and_preprocess_image, genomic_plip_predictions, classify_tiles
 
 def run_load_and_preprocess_image(image_path, clip_processor_path):
@@ -17,20 +16,18 @@ def run_classify_tiles(pred_data, model_path):
     output = classify_tiles(pred_data, model_path)
     return output
 
-
 example_files = list(Path("sample_tiles").glob("*.jpeg"))
 
 with gr.Blocks() as demo:
     gr.Markdown("## Cancer Risk Prediction from Tissue Slide")
 
     image_file = gr.Image(type="filepath", label="Upload Image File")
-
     clip_processor_path = gr.Textbox(label="CLIP Processor Path", value="./genomic_plip_model")
     genomic_plip_model_path = gr.Textbox(label="Genomic PLIP Model Path", value="./genomic_plip_model")
     classifier_model_path = gr.Textbox(label="Classifier Model Path", value="./models/classifier.pth")
 
-    image_tensor_output = gr.Variable()
-    pred_data_output = gr.Variable()
+    image_tensor_output = gr.State()
+    pred_data_output = gr.State()
     result_output = gr.Textbox(label="Result")
 
     preprocess_button = gr.Button("Preprocess Image")
@@ -54,7 +51,6 @@ with gr.Blocks() as demo:
     predict_button.click(update_status, inputs=[predict_status, pred_data_output], outputs=predict_status)
     classify_button.click(update_status, inputs=[classify_status, result_output], outputs=classify_status)
 
-
     gr.Markdown("## Example Images")
     gr.Examples(example_files, inputs=image_file)