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Cervical_Cancer_Risk_Prediction
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Parthebhan commited on
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1 Parent(s): c1b7912

Update app.py

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  1. app.py +35 -35
app.py CHANGED
@@ -11,9 +11,9 @@ def cerviccancer(Age, Num_sexual_partners, First_sexual_intercourse, Num_pregnan
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  STDs_vaginal_condylomatosis, STDs_vulvoperineal_condylomatosis, STDs_syphilis, STDs_pelvic_inflammatory_disease, STDs_genital_herpes,
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  STDs_molluscum_contagiosum, STDs_AIDS, STDs_HIV, STDs_Hepatitis_B, STDs_HPV, STDs_Num_of_diagnosis, Dx_Cancer, Dx_CIN, Dx, Hinselmann, Schiller, Citology):
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  inputs = [[Age, Num_sexual_partners, First_sexual_intercourse, Num_pregnancies, Smokes, Smokes_years, Smokes_packs_year,
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- Hormonal_Contraceptives, Hormonal_Contraceptives_years, IUD, IUD_years, STDs, STDs_condylomatosis, STDs_cervical_condylomatosis,
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- STDs_vaginal_condylomatosis, STDs_vulvoperineal_condylomatosis, STDs_syphilis, STDs_pelvic_inflammatory_disease, STDs_genital_herpes,
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- STDs_molluscum_contagiosum, STDs_AIDS, STDs_HIV, STDs_Hepatitis_B, STDs_HPV, STDs_Num_of_diagnosis, Dx_Cancer, Dx_CIN, Dx, Hinselmann, Schiller, Citology]]
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  prediction = model.predict(inputs)
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  prediction_value = prediction[0]
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  return f"Predicted probability of Biopsy: {prediction_value}"
@@ -22,38 +22,38 @@ def cerviccancer(Age, Num_sexual_partners, First_sexual_intercourse, Num_pregnan
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  # Create the Gradio interface
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  automatidata_ga = gr.Interface(fn=automatidata,
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  inputs = [
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- gr.Number(13.0, 84.0, label="Age"),
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- gr.Number(1.0, 28.0, label="Number of sexual partners"),
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- gr.Number(10.0, 32.0, label="First sexual intercourse"),
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- gr.Number(0.0, 11.0, label="Num of pregnancies"),
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- gr.Number(0.0, 1.0, label="Smokes"),
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- gr.Number(0.0, 37.0, label="Smokes (years)"),
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- gr.Number(0.0, 37.0, label="Smokes (packs/year)"),
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- gr.Number(0.0, 1.0, label="Hormonal Contraceptives"),
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- gr.Number(0.0, 30.0, label="Hormonal Contraceptives (years)"),
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- gr.Number(0.0, 1.0, label="IUD"),
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- gr.Number(0.0, 19.0, label="IUD (years)"),
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- gr.Number(0.0, 1.0, label="STDs"),
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- gr.Number(0.0, 1.0, label="STDs:condylomatosis"),
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- gr.Number(0.0, 0.0, label="STDs:cervical condylomatosis"),
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- gr.Number(0.0, 1.0, label="STDs:vaginal condylomatosis"),
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- gr.Number(0.0, 1.0, label="STDs:vulvo-perineal condylomatosis"),
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- gr.Number(0.0, 1.0, label="STDs:syphilis"),
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- gr.Number(0.0, 1.0, label="STDs:pelvic inflammatory disease"),
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- gr.Number(0.0, 1.0, label="STDs:genital herpes"),
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- gr.Number(0.0, 1.0, label="STDs:molluscum contagiosum"),
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- gr.Number(0.0, 0.0, label="STDs:AIDS"),
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- gr.Number(0.0, 1.0, label="STDs:HIV"),
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- gr.Number(0.0, 1.0, label="STDs:Hepatitis B"),
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- gr.Number(0.0, 1.0, label="STDs:HPV"),
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- gr.Number(0.0, 3.0, label="STDs: Number of diagnosis"),
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- gr.Number(0.0, 1.0, label="Dx:Cancer"),
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- gr.Number(0.0, 1.0, label="Dx:CIN"),
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- gr.Number(0.0, 1.0, label="Dx"),
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- gr.Number(0.0, 1.0, label="Hinselmann"),
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- gr.Number(0.0, 1.0, label="Schiller"),
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- gr.Number(0.0, 1.0, label="Citology"),
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- gr.Number(0.0, 1.0, label="Biopsy")
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  ]
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  outputs="text", title="Cervical Cancer Risk Prediction",
 
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  STDs_vaginal_condylomatosis, STDs_vulvoperineal_condylomatosis, STDs_syphilis, STDs_pelvic_inflammatory_disease, STDs_genital_herpes,
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  STDs_molluscum_contagiosum, STDs_AIDS, STDs_HIV, STDs_Hepatitis_B, STDs_HPV, STDs_Num_of_diagnosis, Dx_Cancer, Dx_CIN, Dx, Hinselmann, Schiller, Citology):
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  inputs = [[Age, Num_sexual_partners, First_sexual_intercourse, Num_pregnancies, Smokes, Smokes_years, Smokes_packs_year,
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+ Hormonal_Contraceptives, Hormonal_Contraceptives_years, IUD, IUD_years, STDs, STDs_condylomatosis, STDs_cervical_condylomatosis,
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+ STDs_vaginal_condylomatosis, STDs_vulvoperineal_condylomatosis, STDs_syphilis, STDs_pelvic_inflammatory_disease, STDs_genital_herpes,
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+ STDs_molluscum_contagiosum, STDs_AIDS, STDs_HIV, STDs_Hepatitis_B, STDs_HPV, STDs_Num_of_diagnosis, Dx_Cancer, Dx_CIN, Dx, Hinselmann, Schiller, Citology]]
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  prediction = model.predict(inputs)
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  prediction_value = prediction[0]
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  return f"Predicted probability of Biopsy: {prediction_value}"
 
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  # Create the Gradio interface
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  automatidata_ga = gr.Interface(fn=automatidata,
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  inputs = [
25
+ gr.Number(13.0, 84.0, label="Age: [13.0 84.0]"),
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+ gr.Number(1.0, 28.0, label="Number of sexual partners: [1.0 28.0]"),
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+ gr.Number(10.0, 32.0, label="First sexual intercourse: [10.0 32.0]"),
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+ gr.Number(0.0, 11.0, label="Num of pregnancies: [0.0 11.0]"),
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+ gr.Number(0.0, 1.0, label="Smokes: [0.0 1.0]"),
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+ gr.Number(0.0, 37.0, label="Smokes (years): [0.0 37.0]"),
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+ gr.Number(0.0, 37.0, label="Smokes (packs/year): [0.0 37.0]"),
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+ gr.Number(0.0, 1.0, label="Hormonal Contraceptives: [0.0 1.0]"),
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+ gr.Number(0.0, 30.0, label="Hormonal Contraceptives (years): [0.0 30.0]"),
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+ gr.Number(0.0, 1.0, label="IUD: [0.0 1.0]"),
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+ gr.Number(0.0, 19.0, label="IUD (years): [0.0 19.0]"),
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+ gr.Number(0.0, 1.0, label="STDs: [0.0 1.0]"),
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+ gr.Number(0.0, 1.0, label="STDs:condylomatosis: [0.0 1.0]"),
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+ gr.Number(0.0, 0.0, label="STDs:cervical condylomatosis: [0.0 0.0]"),
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+ gr.Number(0.0, 1.0, label="STDs:vaginal condylomatosis: [0.0 1.0]"),
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+ gr.Number(0.0, 1.0, label="STDs:vulvo-perineal condylomatosis: [0.0 1.0]"),
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+ gr.Number(0.0, 1.0, label="STDs:syphilis: [0.0 1.0]"),
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+ gr.Number(0.0, 1.0, label="STDs:pelvic inflammatory disease: [0.0 1.0]"),
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+ gr.Number(0.0, 1.0, label="STDs:genital herpes: [0.0 1.0]"),
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+ gr.Number(0.0, 1.0, label="STDs:molluscum contagiosum: [0.0 1.0]"),
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+ gr.Number(0.0, 0.0, label="STDs:AIDS: [0.0 0.0]"),
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+ gr.Number(0.0, 1.0, label="STDs:HIV: [0.0 1.0]"),
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+ gr.Number(0.0, 1.0, label="STDs:Hepatitis B: [0.0 1.0]"),
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+ gr.Number(0.0, 1.0, label="STDs:HPV: [0.0 1.0]"),
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+ gr.Number(0.0, 3.0, label="STDs: Number of diagnosis: [0.0 3.0]"),
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+ gr.Number(0.0, 1.0, label="Dx:Cancer: [0.0 1.0]"),
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+ gr.Number(0.0, 1.0, label="Dx:CIN: [0.0 1.0]"),
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+ gr.Number(0.0, 1.0, label="Dx: [0.0 1.0]"),
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+ gr.Number(0.0, 1.0, label="Hinselmann: [0.0 1.0]"),
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+ gr.Number(0.0, 1.0, label="Schiller: [0.0 1.0]"),
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+ gr.Number(0.0, 1.0, label="Citology: [0.0 1.0]"),
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+ gr.Number(0.0, 1.0, label="Biopsy: [0.0 1.0]")
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  ]
58
 
59
  outputs="text", title="Cervical Cancer Risk Prediction",