Update app.py

app.py

@@ -12,7 +12,7 @@ loaded_feature_extractor = load_model("feature_extractor_model")
 
 # Load the SVM model
 import pickle
-with open("svm_model.pkl", 'rb') as file:
+with open("svm_model_probablity.pkl", 'rb') as file:
     loaded_svm_model = pickle.load(file)
 
 # Load the mineral detection model
@@ -71,28 +71,31 @@ def classify_image(image):
     is_mineral = detect_mineral(image)
     if not is_mineral:
         return "Input is not a Microscopic mineral thin section, Please Insert a thin section.", "", ""
-    
+
     # Preprocess the image for classification
     image = preprocess_image_classification(np.array(image))
     if image is None:
         return "Error preprocessing image.", "", ""
-    
+
     # Extract features using the loaded CNN feature extractor
     image_features = loaded_feature_extractor.predict(image)
-    
+
     # Make prediction using the loaded SVM model
     predicted_label = loaded_svm_model.predict(image_features)
     class_idx = predicted_label[0]
     predicted_class_name = class_labels[class_idx]
-    
-    # Get prediction scores for all classes
-    decision_values = loaded_svm_model.decision_function(image_features)
-    prediction_scores = [f"{label}: {score:.4f}" for label, score in zip(class_labels, decision_values[0])]
+
+    # Get probabilities for all classes
+    probabilities = loaded_svm_model.predict_proba(image_features)[0]
+    prediction_scores = [
+        f"{label}: {prob:.4f}"
+        for label, prob in zip(class_labels, probabilities)
+    ]
     predicted_scores = "\n".join(prediction_scores)
-    
-    # Get the mineral key facts
+
+    # Get key facts about the predicted mineral
     mineral_key_facts = mineral_facts.get(predicted_class_name, "No key facts available for this mineral.")
-    
+
     return predicted_class_name, predicted_scores, mineral_key_facts
 
 
@@ -176,6 +179,7 @@ custom_css = """
 """
 
 
+# Gradio Blocks interface
 with gr.Blocks(
     title=app_title,
     css=custom_css,
@@ -183,7 +187,7 @@ with gr.Blocks(
     js=js,
 ) as demo:
     gr.Markdown(DESCRIPTION)
-    
+
     # Create interface components
     with gr.Row():
         image_input = gr.Image(elem_id="image_input", type="pil")
@@ -193,10 +197,16 @@ with gr.Blocks(
             gr.Textbox(label="Key Facts About Mineral", elem_id="mineral_key_facts", lines=8),
         ]
         image_button = gr.Button("Classify Mineral")
-        image_button.click(classify_image, inputs=image_input, outputs=output_components)
+        image_button.click(
+            classify_image,
+            inputs=image_input,
+            outputs=output_components
+        )
 
     gr.Examples(
-        examples=["Gradio examples/Biotite1.jpg", "Gradio examples/Biotite2.jpg", "Gradio examples/Olivine1.jpg", "Gradio examples/Plagioclase1.jpg"],
+        examples=[
+            r"C:\Users\nanom\OneDrive\Desktop\Gradio examples\Olivine2.jpg",
+        ],
         inputs=image_input,
     )