Update app.py

app.py

@@ -1,24 +1,12 @@
 import gradio as gr
+import shap
 from fastai.vision.all import *
 from efficientnet_pytorch import EfficientNet 
 
-#learn = load_learner('model/predictcovidfastaifinal18102023.pkl')
-learn = load_learner('model/final_20102023_eb7_model.pkl')
-
-categories = learn.dls.vocab
 
-def predict_image(get_image):
-   pred, idx, probs = learn.predict(get_image)
-   return dict(zip(categories, map(float, probs)))
 
 title = "COVID_19 Infection Detectation App!"
 head = (
-  "<h1>"
-    "<centre>"
-      #"<img src='covid/Xray_Image_SuperImposed_Gradcam.png'>"
-      "<img src='Xray_Image_SuperImposed_Gradcam.png' alt='Xray GradCam' width='400' height='400'>"
-    "</centre>"
-  "</h1>"
   "<div>"
     "This Space demonstrates model based on efficientnet base model. I has been trained to classify chest xray image." 
     " "
@@ -36,6 +24,45 @@ 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')
+
+categories = learn.dls.vocab
+
+def predict_image(get_image):
+   pred, idx, probs = learn.predict(get_image)
+   return dict(zip(categories, map(float, probs)))
+
 interpretation="default"
 enable_queue=True