Delete app.py

app.py

@@ -1,100 +0,0 @@
-# -*- coding: utf-8 -*-
-"""app.ipynb
-
-Automatically generated by Colab.
-
-Original file is located at
-    https://colab.research.google.com/drive/1oJP09Coya1D16dQ_7fpVGlbuLnCWxHTN
-"""
-
-import gradio as gr
-import joblib
-import numpy as np
-from tensorflow.keras.models import load_model
-
-# Load models
-svr_model = joblib.load('SVR_model.joblib')
-rf_model = joblib.load('RandomForestRegressor_model.joblib')
-dt_model = joblib.load('best_DecisionTreeRegressor_model.joblib')
-dl_model = load_model('best_DeepLearning_model.h5')
-
-# Load preprocessing objects
-label_encoder = joblib.load('label_encoder.joblib')
-column_transformer = joblib.load('column_transformer.joblib')
-
-def predict_warfarin_dose(gender, race, age, height, weight, diabetes,
-                         simvastatin, amiodarone, inr_reported,
-                         cyp2c9, vkorc1, model_choice):
-    try:
-        # Encode Age
-        age_encoded = label_encoder.transform([age])
-
-        # Create input list
-        inputs = [
-            str(gender),
-            str(race),
-            str(age),
-            float(height) if height is not None else 0.0,
-            float(weight) if weight is not None else 0.0,
-            float(diabetes),
-            float(simvastatin),
-            float(amiodarone),
-            float(inr_reported) if inr_reported is not None else 0.0,
-            str(cyp2c9),
-            str(vkorc1)
-        ]
-
-        # Transform inputs
-        inputs_transformed = column_transformer.transform([inputs])
-        inputs_transformed[0][-7] = age_encoded[0]
-        input_data = np.array(inputs_transformed, dtype=np.float32)
-
-        # Make prediction based on model choice
-        if model_choice == 'Deep Learning':
-            prediction = dl_model.predict(input_data)[0][0]
-        elif model_choice == 'Support Vector Regression':
-            prediction = svr_model.predict(input_data)[0]
-        elif model_choice == 'Random Forest':
-            prediction = rf_model.predict(input_data)[0]
-        else:
-            prediction = dt_model.predict(input_data)[0]
-
-        return f"Predicted Warfarin Dose: {prediction:.2f} mg/week"
-
-    except Exception as e:
-        return f"Error in prediction: {str(e)}"
-
-# Create the Gradio interface
-iface = gr.Interface(
-    fn=predict_warfarin_dose,
-    inputs=[
-        gr.Radio(["male", "female"], label="Gender"),
-        gr.Dropdown(["Asian", "Black", "White", "Unknown", "Mixed or Missing"], label="Race"),
-        gr.Dropdown(["0-9", "10-19", "20-29", "30-39", "40-49", "50-59", "60-69",
-                    "70-79", "80-89", "90+"], label="Age"),
-        gr.Number(label="Height (cm)"),
-        gr.Number(label="Weight (kg)"),
-        gr.Radio([0.0, 1.0], label="Diabetes"),
-        gr.Radio([0.0, 1.0], label="Simvastatin (Zocor)"),
-        gr.Radio([0.0, 1.0], label="Amiodarone (Cordarone)"),
-        gr.Number(label="INR on Reported Therapeutic Dose of Warfarin"),
-        gr.Dropdown(["*1/*1", "*1/*2", "*1/*3", "*2/*2", "*2/*3", "*3/*3"],
-                   label="Cyp2C9 genotypes"),
-        gr.Radio(["A/A", "A/G", "G/G"], label="VKORC1 genotypes"),
-        gr.Dropdown(['Decision Tree', 'Support Vector Regression',
-                    'Random Forest', 'Deep Learning'], label="Model Selection")
-    ],
-    outputs=gr.Textbox(label="Prediction Result"),
-    title="Warfarin Dosage Prediction System",
-    description="""This system predicts the optimal warfarin dosage based on patient characteristics.
-                   Enter the required information below and select a model for prediction.""",
-    examples=[
-        ["male", "Asian", "50-59", 170, 70, 0.0, 0.0, 0.0, 2.5, "*1/*1", "A/G", "Random Forest"],
-        ["female", "White", "60-69", 165, 65, 1.0, 1.0, 0.0, 2.8, "*1/*2", "G/G", "Deep Learning"]
-    ],
-    theme="default"
-)
-
-# Launch the interface
-if __name__ == "__main__":
-    iface.launch()