initial commit

README.md

@@ -1,6 +1,6 @@
 ---
 title: Warfarin Dosage
-emoji: 📚
+emoji: 💉
 colorFrom: yellow
 colorTo: red
 sdk: gradio

ann_model_regression.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:70a2c3d2d149e8985ff03632fe35315837ea47b87b3f842466bceebcef4c336c
+size 102630

app.py

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+import gradio as gr
+import pickle
+
+gender_categories = ["Male", "Female"]
+gender = gr.inputs.Dropdown(gender_categories, label="Gender")
+
+race_categories = ["White", "Black", "Other"]
+race = gr.inputs.Dropdown(race_categories, label="Race")
+
+age_categories = ["80 - 89", "70 - 79", "50 - 59", "60 - 69",  "0 - 9", "40 - 49",  "30 - 39", "20 - 29", "10 - 19"]
+age =  gr.inputs.Dropdown(age_categories, label="Age")
+
+height = gr.inputs.Textbox(label = "Height (cm)")
+weight =  gr.inputs.Textbox(label = "Weight (kg)")
+
+diabetes_categories = ["Yes", "No"]
+diabetes = gr.inputs.Dropdown(diabetes_categories, label="Diabetes")
+
+simvastatin_categories = ["Yes", "No"]
+simvastatin = gr.inputs.Dropdown(simvastatin_categories, label="Simvastatin (Zocor)")
+
+amiodarone_categories = ["Yes", "No"]
+amiodarone  = gr.inputs.Dropdown(amiodarone_categories, label="Amiodarone (Cordarone)")
+
+target_inr = gr.inputs.Textbox(label = "Target INR (mg/week)")
+inr = gr.inputs.Textbox(label = "INR on Reported Therapeutic Dose of Warfarin")
+
+cyp2c9_genotypes_categories = ["*1/*1", "*1/*3", "*1/*2", "*2/*2", "*1/*5", "*2/*3", "*3/*3"]
+cyp2c9_genotypes = gr.inputs.Dropdown(cyp2c9_genotypes_categories, label = "Cyp2C9 genotypes")
+
+vk0rc1_genotype_categories = ["A/G", "G/G", "A/A"]
+vk0rc1_genotype = gr.inputs.Dropdown(vk0rc1_genotype_categories, label = "VKORC1 genotype: -1639 G>A (3673); chr16:31015190; rs9923231; C/T")
+
+model_selection = gr.inputs.Dropdown(["Linear Regression (Regression)", "Ridge Regression (Regression)", "Random Forest (Regression)", "Neural Network (Regression)", "K-Nearest Neighbors (Classification)", "Logistic Regression (Classification)", "Random Forest (Classification)"], label="Machine Learning Model")
+
+warfarin_dose = gr.outputs.Textbox(label = "Warfarin Dose")
+
+linear_model = pickle.load(open('linear_model_regression.pkl', 'rb'))
+ridge_model = pickle.load(open('ridge_model_regression.pkl', 'rb'))
+forest_mode = pickle.load(open('forest_model_regression.pkl', 'rb'))
+ann_model = pickle.load(open('ann_model_regression.pkl', 'rb'))
+knn_model_clf = pickle.load(open('knn_model_classification.pkl', 'rb'))
+logistic_model_clf = pickle.load(open('logistic_model_classification.pkl', 'rb'))
+random_forest_model_clf = pickle.load(open('random_forest_model_classification.pkl', 'rb'))
+
+def predict_warfarin_dose(gender, race, age, height, weight, diabetes, simvastatin, amiodarone, target_inr, inr, cyp2c9_genotypes, vk0rc1_genotyp, model_selection):
+    new_instance = [gender_categories.index(gender), race_categories.index(race), age_categories.index(age), height, weight, diabetes_categories.index(diabetes), simvastatin_categories.index(simvastatin), amiodarone_categories.index(amiodarone), target_inr, inr, cyp2c9_genotypes_categories.index(cyp2c9_genotypes), vk0rc1_genotype_categories.index(vk0rc1_genotype)]
+    if model_selection == "Linear Regression (Regression)":
+        prediction = linear_model.predict(new_instance)
+    elif model_selection == "Ridge Regression (Regression)":
+        prediction = ridge_model.predict(new_instance)
+    elif model_selection == "Random Forest (Regression)":
+        prediction = forest_mode.predict(new_instance)
+    elif model_selection == "Neural Network (Regression)":
+        prediction = ann_model.predict(new_instance)
+    elif model_selection == "K-Nearest Neighbors (Classification)":
+        prediction = knn_model_clf.predict(new_instance)
+    elif model_selection == "Logistic Regression (Classification)":
+        prediction = logistic_model_clf.predict(new_instance)
+    else:
+        prediction = random_forest_model_clf.predict(new_instance)
+    return prediction
+
+interface = gr.Interface(fn=predict_warfarin_dose, inputs=[gender, race, age, height, weight, diabetes, simvastatin, amiodarone, target_inr, inr, cyp2c9_genotypes, vk0rc1_genotype, model_selection], outputs=warfarin_dose)
+interface.launch()

forest_model_regression.pkl

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+size 2156240

knn_model_classification.pkl

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+oid sha256:49397b66ebe3533e97e212f8a705c4f6a87843d7124eaa2c95052c93f897e006
+size 65667

linear_model_regression.pkl

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+oid sha256:05e975a4d81074768a09ff467be92a03b6b21d233849f213fd191c5e64755f9d
+size 651

logistic_model_classification.pkl

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+oid sha256:5c2f67d257015e2c115b24df5f93087c34de7f0e63f77ad72951e04429ee33ad
+size 3262482

random_forest_model_classification.pkl

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+oid sha256:5c2f67d257015e2c115b24df5f93087c34de7f0e63f77ad72951e04429ee33ad
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requirements.txt

@@ -0,0 +1 @@
+pickle5

ridge_model_regression.pkl

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+size 553