Upload app.py

Reverting to pre-definition

app.py

@@ -1,8 +1,10 @@
 import gradio as gr
 import joblib
 import numpy as np
+import pandas as pd
 import tensorflow as tf
 
+# Assuming you have a function that creates the same model architecture
 def create_model(input_shape):
     model = tf.keras.models.Sequential([
         tf.keras.layers.Flatten(input_shape=input_shape),
@@ -20,8 +22,12 @@ def create_model(input_shape):
                   metrics=['accuracy'])
     return model
 
-model = create_model([18])
+# Load model weights
+input_shape = [18]  # Adjust based on your feature count
+model = create_model(input_shape)
 model.load_weights('model_2.h5')
+
+# Load the scaler
 scaler = joblib.load('scaler.joblib')
 
 features = [
@@ -33,27 +39,6 @@ features = [
     'HEP_NEOTHERAPY_140101_Preoperative systemic chemotherapy'
 ]
 
-feature_descriptions = {
-    'PRNCPTX_HEPATECTOMY RESCJ TOTAL RIGHT LOBECTOMY': "Principal Text: Hepatectomy Resection Total Right Lobectomy",
-    'OPTIME': "Operation Time",
-    'HYPERMED_Yes': "Hypertension Medication: Yes/No",
-    'PRNCPTX_HEPATECTOMY RESCJ TRISEGMENTECTOMY': "Principal Text: Hepatectomy Resection Tri-segmentectomy",
-    'PRPLATE': "Preoperative Platelet Count",
-    'PRINR': "Preoperative INR Level",
-    'HEP_APPROACH_Laparoscopic': "Hepatectomy Approach: Laparoscopic",
-    'PRALKPH': "Preoperative Alkaline Phosphatase Level",
-    'HEP_PRINGLE_Yes': "Pringle Maneuver Used: Yes/No",
-    'HEP_SEC_HISTOLOGIC_Colorectal metastasis': "Secondary Histologic Diagnosis: Colorectal Metastasis",
-    'HEP_CON_PARTRES': "Concurrent Partial Resection",
-    'SEX_male': "Patient Gender: Male",
-    'ASACLAS': "ASA Classification",
-    'HEP_SEC_TUMORSIZE': "Secondary Tumor Size",
-    'HEP_SEC_NUMTUMORS': "Number of Secondary Tumors",
-    'PRSGOT': "Preoperative SGOT Levels",
-    'HEP_VIRAL_None': "No Viral Hepatitis",
-    'HEP_NEOTHERAPY_140101_Preoperative systemic chemotherapy': "Preoperative Systemic Chemotherapy"
-}
-
 categorical_features = [
     'HYPERMED_Yes', 'HEP_APPROACH_Laparoscopic', 'HEP_PRINGLE_Yes',
     'SEX_male', 'HEP_VIRAL_None'
@@ -61,25 +46,26 @@ categorical_features = [
 
 def predict_outcome(*args):
     inputs = list(args)
+    # Convert 'Yes'/'No' inputs to 1/0 for the model
     for i, feature in enumerate(features):
         if feature in categorical_features:
             inputs[i] = 1 if inputs[i] == 'Yes' else 0
     input_data = np.array(inputs).reshape(1, -1)
+    # Scale inputs
     inputs_scaled = scaler.transform(input_data)
+    # Predict
     prediction = model.predict(inputs_scaled)
     outcome_probability = prediction.flatten()[0]
-    prediction_text = f'Probability of Post Hepatectomy Liver Failure: {outcome_probability:.2%}'
-    markdown_text = "More details on the variables used in NSQIP can be found in the [NSQIP PUF User Guide 2021]	(https://www.facs.org/media/wd2hlqzv/nsqip_puf_userguide_2021.pdf)."
-    return prediction_text, markdown_text
-
-inputs = [gr.Radio(label=feature_descriptions.get(feature), choices=['Yes', 'No']) if feature in categorical_features else gr.Number(label=feature_descriptions.get(feature)) for feature in features]
+    return f'Probability of Post Hepatectomy Liver Failure: {outcome_probability:.2%}'
 
-output_text = gr.Textbox(label="Probability of Liver Failure")
-link_info = gr.Markdown()
+# Define Gradio inputs
+inputs = []
+for feature in features:
+    if feature in categorical_features:
+        inputs.append(gr.Radio(label=feature, choices=['Yes', 'No']))
+    else:
+        inputs.append(gr.Number(label=feature))
 
-iface = gr.Interface(
-    fn=predict_outcome,
-    inputs=[gr.Radio(label=feature_descriptions.get(feature), choices=['Yes', 'No']) if feature in categorical_features else gr.Number(label=feature_descriptions.get(feature)) for feature in features],
-    outputs=[output_text, link_info],
-    title="NSQIP Post Hepatectomy Liver Failure Calculator"
-)
+# Set up and launch the Gradio interface
+iface = gr.Interface(fn=predict_outcome, inputs=inputs, outputs='text')
+iface.launch()