Update app.py
Browse files
app.py
CHANGED
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@@ -7,7 +7,7 @@ import pickle
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import gradio as gr
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from math import sqrt
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df= pd.read_csv("heart.csv")
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df
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df.info()
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@@ -118,13 +118,13 @@ def prd(model, age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpe
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if model == "Random Forest":
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filename = 'randomforest.pkl'
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X_test = np.array([[age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca, thal],[52, 1, 0, 125, 212, 0, 1, 168, 0, 1.0, 2, 2, 3]])
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loaded_model = pickle.load(open(filename, 'rb'))
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result = loaded_model.predict(X_test)[0]
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else:
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filename = 'scaling.pkl'
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X_test = np.array([[age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca, thal],[52, 1, 0, 125, 212, 0, 1, 168, 0, 1.0, 2, 2, 3]])
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loaded_model = pickle.load(open(filename, 'rb'))
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result = loaded_model.predict(X_test)[0]
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import gradio as gr
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from math import sqrt
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df= pd.read_csv("DACVV/heart.csv")
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df
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df.info()
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if model == "Random Forest":
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filename = 'DACVV/randomforest.pkl'
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X_test = np.array([[age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca, thal],[52, 1, 0, 125, 212, 0, 1, 168, 0, 1.0, 2, 2, 3]])
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loaded_model = pickle.load(open(filename, 'rb'))
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result = loaded_model.predict(X_test)[0]
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else:
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filename = 'DACVV/scaling.pkl'
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X_test = np.array([[age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca, thal],[52, 1, 0, 125, 212, 0, 1, 168, 0, 1.0, 2, 2, 3]])
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loaded_model = pickle.load(open(filename, 'rb'))
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result = loaded_model.predict(X_test)[0]
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