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import pandas as pd |
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import numpy as np |
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import sklearn as sn |
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from datasets import Dataset |
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df=pd.read_csv("https://huggingface.co/spaces/Ralmao/Anemia/raw/main/Flujo_anemia.csv",encoding='latin-1', on_bad_lines='skip') |
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dataset= Dataset.from_pandas(df) |
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X = df.drop(['Flujo_Type'], axis = 1) |
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y = df['Flujo_Type'] |
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from sklearn.model_selection import train_test_split |
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X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.30,random_state=42) |
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from sklearn.ensemble import RandomForestClassifier |
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clf = RandomForestClassifier(n_estimators=10) |
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clf = clf.fit(X_train, y_train) |
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clf |
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import gradio as gr |
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def predict_Flujotype(Hemoglobina): |
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x = np.array([Hemoglobina]) |
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pred = clf.predict(x.reshape(1, -1)) |
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if pred == 1: |
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return "Paciente con anemia empezar con un flujo de 220 e ir incrementando poco a poco hasta llegar a 300" |
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else: |
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return "Paciente sin anemia empezar con un flujo de 250 e ir incrementando poco a poco hasta llegar a 300" |
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Hemoglobina = gr.Number(label='Hemoglobina') |
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output = gr.Textbox(label='Flujo_Type') |
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app = gr.Interface(predict_Flujotype,inputs = [Hemoglobina],outputs=output, description= 'This is a Flujo Type Predictor') |
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app.launch(share=True) |