Update app.py

app.py

@@ -1,10 +1,8 @@
 import gradio as gr
-import shap
 from fastai.vision.all import *
 from efficientnet_pytorch import EfficientNet 
 
 
-
 title = "COVID_19 Infection Detectation App!"
 head = (
   "<div>"
@@ -24,36 +22,6 @@ examples = [
     ['covid/covid_1031.png']
 ]
 
-# Load the SHAP explainer for your model
-explainer = shap.Explainer(model, data=None)
-
-# Define a function to compute Shapley values for an image
-def compute_shapley(image):
-    # Preprocess the input image (e.g., resize, normalize) to match the model's input requirements
-    # You should replace this with your specific preprocessing code
-    image = preprocess_image(image)
-
-    # Compute Shapley values for the input image
-    shap_values = explainer(image)
-
-    # Convert the Shapley values to a format that can be displayed (e.g., a heatmap)
-    shap_values_image = shap.image_plot(shap_values[0], image)
-
-    return shap_values_image
-
-# Define the Gradio interface
-image_input = gr.inputs.Image(shape=(224, 224))  # Adjust the shape as needed
-shapley_output = gr.outputs.Image()
-
-def interpreter_func(input_text):
-    if input_text == "Compute Shapley":
-        return compute_shapley(image_input)
-    else:
-        return None
-
-interpreter_input = gr.inputs.Textbox(default="Type 'Compute Shapley' and click the button")
-interpreter_button = "Generate Shapley"
-
 #learn = load_learner('model/predictcovidfastaifinal18102023.pkl')
 learn = load_learner('model/final_20102023_eb7_model.pkl')