Update app.py

app.py

@@ -48,7 +48,7 @@ feature_names = [
     "SE Radius", "SE Texture", "SE Perimeter", "SE Area", "SE Smoothness",
     "SE Compactness", "SE Concavity", "SE Concave Points", "SE Symmetry", "SE Fractal Dimension",
     "Worst Radius", "Worst Texture", "Worst Perimeter", "Worst Area", "Worst Smoothness",
-    "Worst Compactness", "Worst Concavity", "Worst Concave Points", "Worst Symmetry"
+    "Worst Compactness", "Worst Concavity", "Worst Concave Points", "Worst Symmetry", "Worst Fractal Dimension"
 ]
 
 def classify(model_choice, image=None, file=None, *features):
@@ -73,7 +73,7 @@ def classify(model_choice, image=None, file=None, *features):
             except Exception as e:
                 return f"Error reading file: {e}"
         
-        if any(f is None for f in features):
+        if len(features) != len(feature_names):
             return "Please provide all 30 numerical features."
 
         input_data = np.array(features).reshape(1, -1)
@@ -83,24 +83,33 @@ def classify(model_choice, image=None, file=None, *features):
 
         return class_names[predicted_class]
 
-# Gradio UI
-model_selector = gr.Radio(["ViT", "Neural Network"], label="Choose Model")
-image_input = gr.Image(type="pil", label="Upload Mammogram Image")
-file_input = gr.File(label="Upload Text File (for NN model)")
-feature_inputs = [gr.Number(label=feature) for feature in feature_names]
-
-# Example inputs from Wisconsin Breast Cancer Dataset
-benign_example = ["Neural Network", None, None, 12.32, 12.39, 78.85, 464.1, 0.1028, 0.06981, 0.03987, 0.037, 0.1959, 0.05955, 0.2684, 0.7887, 2.058, 0.02142, 0.01499, 0.01157, 0.01117, 0.01841, 0.003599, 14.34, 14.26, 93.77, 644.8, 0.1288, 0.108, 0.08877, 0.08021, 0.2856, 0.07039]
-malignant_example = ["Neural Network", None, None, 20.57, 17.77, 132.9, 1326.0, 0.08474, 0.07864, 0.0869, 0.07017, 0.1812, 0.05667, 0.5435, 0.7339, 3.398, 0.005225, 0.01308, 0.0186, 0.0134, 0.01389, 0.003532, 24.99, 23.41, 158.8, 1956.0, 0.1238, 0.1866, 0.2416, 0.186, 0.275, 0.08902]
-example_inputs = [benign_example, malignant_example]
-
-iface = gr.Interface(
-    fn=classify,
-    inputs=[model_selector, image_input, file_input] + feature_inputs,
-    outputs="text",
-    title="Breast Cancer Classification",
-    description="Choose between ViT (image-based) and Neural Network (feature-based) classification. You can upload a text file containing feature values instead of entering them manually.",
-    examples=example_inputs
-)
+with gr.Blocks() as iface:
+    gr.Markdown("# Breast Cancer Classification")
+    gr.Markdown("Choose between ViT (image-based) and Neural Network (feature-based) classification.")
+
+    with gr.Row():
+        model_selector = gr.Radio(["ViT", "Neural Network"], label="Choose Model", interactive=True)
+
+    with gr.Row():
+        with gr.Column():
+            image_input = gr.Image(type="pil", label="Upload Mammogram Image")
+            file_input = gr.File(label="Upload Text File (for NN model)")
+
+    with gr.Row():
+        gr.Markdown("### Enter Features (For Neural Network Model)")
+    
+    num_cols = 3
+    feature_inputs = [gr.Number(label=feature) for feature in feature_names]
+    feature_inputs_split = [feature_inputs[i::num_cols] for i in range(num_cols)]
+
+    with gr.Row():
+        for col in feature_inputs_split:
+            with gr.Column():
+                for feature in col:
+                    feature.render()
+
+    output = gr.Textbox(label="Prediction Result")
+    classify_button = gr.Button("Classify")
+    classify_button.click(classify, inputs=[model_selector, image_input, file_input] + feature_inputs, outputs=output)
 
 iface.launch()