init commit

app.py

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+import gradio as gr
+from joblib import load
+import pandas as pd
+
+rf = load('random_forest_model.joblib')
+
+def predict(df):
+    pred = rf.predict(df)
+    return {'LUNG_CANCER': pred[0]}
+
+def process_inputs(AGE, GENDER, SMOKING, YELLOW_FINGERS, ANXIETY, PEER_PRESSURE, CHRONIC_DISEASE, WHEEZING, ALCOHOL_CONSUMING, COUGHING, SHORTNESS_OF_BREATH, SWALLOWING_DIFFICULTY, CHEST_PAIN):
+    GENDER=GENDER.map({'Female': 0, 'Male': 1})
+    SMOKING=SMOKING.map({'Yes': 1, 'No': 0})
+    YELLOW_FINGERS=YELLOW_FINGERS.map({'Yes': 1, 'No': 0})
+    ANXIETY=ANXIETY.map({'Yes': 1, 'No': 0})
+    PEER_PRESSURE=PEER_PRESSURE.map({'Yes': 1, 'No': 0})
+    CHRONIC_DISEASE=CHRONIC_DISEASE.map({'Yes': 1, 'No': 0})
+    WHEEZING=WHEEZING.map({'Yes': 1, 'No': 0})
+    ALCOHOL_CONSUMING=ALCOHOL_CONSUMING.map({'Yes': 1, 'No': 0})
+    COUGHING=COUGHING.map({'Yes': 1, 'No': 0})
+    SHORTNESS_OF_BREATH=SHORTNESS_OF_BREATH.map({'Yes': 1, 'No': 0})
+    SWALLOWING_DIFFICULTY=SWALLOWING_DIFFICULTY.map({'Yes': 1, 'No': 0})
+    CHEST_PAIN=CHEST_PAIN.map({'Yes': 1, 'No': 0})
+    df = pd.DataFrame(columns=["GENDER", "SMOKING", "YELLOW_FINGERS", "ANXIETY", "PEER_PRESSURE", "CHRONIC_DISEASE", "WHEEZING", "ALCOHOL_CONSUMING", "COUGHING", "SHORTNESS_OF_BREATH", "SWALLOWING_DIFFICULTY", "CHEST_PAIN", "AGE"])
+    df.loc[0] = [GENDER, SMOKING, YELLOW_FINGERS, ANXIETY, PEER_PRESSURE, CHRONIC_DISEASE, WHEEZING, ALCOHOL_CONSUMING, COUGHING, SHORTNESS_OF_BREATH, SWALLOWING_DIFFICULTY, CHEST_PAIN, AGE];
+    return predict(df)
+    
+inputs = [
+    gr.Number(label="Age"),
+    gr.Radio(choices=["Female", "Male"], label="Gender"),
+    gr.Radio(choices=["Yes", "No"], label="Smoking"),
+    gr.Radio(choices=["Yes", "No"], label="Yellow_fingers"),
+    gr.Radio(choices=["Yes", "No"], label="Anxiety"),
+    gr.Radio(choices=["Yes", "No"], label="Peer pressure"),
+    gr.Radio(choices=["Yes", "No"], label="Chronic_disease"),
+    gr.Radio(choices=["Yes", "No"], label="Wheezing"),
+    gr.Radio(choices=["Yes", "No"], label="Alcohol_consuming"),
+    gr.Radio(choices=["Yes", "No"], label="Coughing"),
+    gr.Radio(choices=["Yes", "No"], label="shortness_of_breath"),
+    gr.Radio(choices=["Yes", "No"], label="swallowing_difficulty"),
+    gr.Radio(choices=["Yes", "No"], label="chest_pain")
+]
+
+output = gr.Label(num_top_classes=1)
+
+iface = gr.Interface(fn=process_inputs, inputs=inputs, outputs=output,
+                     description="O modelo em questão classifica 1 se tiver cancer de pulmão e 0 se não tiver.")
+
+iface.launch()

requirements.txt

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+gradio
+pandas
+numpy
+joblib
+scikit-learn==1.2.2