Update app.py
Browse files
app.py
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@@ -5,6 +5,18 @@ import numpy as np
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# Load the pickled model
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model = tf.keras.models.load_model("census.h5")
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# Define the function for making predictions
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def salarybracket(age, workclass, education, education_num, marital_status, occupation, relationship, race, gender, capital_gain, capital_loss, hours_per_week, native_country):
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inputs = np.array([[age, workclass, education, education_num, marital_status, occupation, relationship, race, gender, capital_gain, capital_loss, hours_per_week, native_country]])
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@@ -13,27 +25,33 @@ def salarybracket(age, workclass, education, education_num, marital_status, occu
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result = "Income_bracket lesser than or equal to 50K ⬇️" if prediction_value <= 0.5 else "Income_bracket greater than 50K ⬆️"
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return f"{result}"
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# Create the Gradio interface
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salarybracket_ga = gr.Interface(fn=salarybracket,
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salarybracket_ga.launch(share=True, debug=True)
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# Load the pickled model
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model = tf.keras.models.load_model("census.h5")
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# Mapping of categorical variables to encoded values
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mapping = {
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'workclass': {' ?': 0, ' Federal-gov': 1, ' Local-gov': 2, ' Never-worked': 3, ' Private': 4, ' Self-emp-inc': 5, ' Self-emp-not-inc': 6, ' State-gov': 7, ' Without-pay': 8},
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'education': {' 10th': 0, ' 11th': 1, ' 12th': 2, ' 1st-4th': 3, ' 5th-6th': 4, ' 7th-8th': 5, ' 9th': 6, ' Assoc-acdm': 7, ' Assoc-voc': 8, ' Bachelors': 9, ' Doctorate': 10, ' HS-grad': 11, ' Masters': 12, ' Preschool': 13, ' Prof-school': 14, ' Some-college': 15},
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'marital_status': {' Divorced': 0, ' Married-AF-spouse': 1, ' Married-civ-spouse': 2, ' Married-spouse-absent': 3, ' Never-married': 4, ' Separated': 5, ' Widowed': 6},
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'occupation': {' ?': 0, ' Adm-clerical': 1, ' Armed-Forces': 2, ' Craft-repair': 3, ' Exec-managerial': 4, ' Farming-fishing': 5, ' Handlers-cleaners': 6, ' Machine-op-inspct': 7, ' Other-service': 8, ' Priv-house-serv': 9, ' Prof-specialty': 10, ' Protective-serv': 11, ' Sales': 12, ' Tech-support': 13, ' Transport-moving': 14},
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'relationship': {' Husband': 0, ' Not-in-family': 1, ' Other-relative': 2, ' Own-child': 3, ' Unmarried': 4, ' Wife': 5},
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'race': {' Amer-Indian-Eskimo': 0, ' Asian-Pac-Islander': 1, ' Black': 2, ' Other': 3, ' White': 4},
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'gender': {' Female': 0, ' Male': 1},
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'native_country': {' ?': 0, ' Cambodia': 1, ' Canada': 2, ' China': 3, ' Columbia': 4, ' Cuba': 5, ' Dominican-Republic': 6, ' Ecuador': 7, ' El-Salvador': 8, ' England': 9, ' France': 10, ' Germany': 11, ' Greece': 12, ' Guatemala': 13, ' Haiti': 14, ' Honduras': 15, ' Hong': 16, ' Hungary': 17, ' India': 18, ' Iran': 19, ' Ireland': 20, ' Italy': 21, ' Jamaica': 22, ' Japan': 23, ' Laos': 24, ' Mexico': 25, ' Nicaragua': 26, ' Outlying-US(Guam-USVI-etc)': 27, ' Peru': 28, ' Philippines': 29, ' Poland': 30, ' Portugal': 31, ' Puerto-Rico': 32, ' Scotland': 33, ' South': 34, ' Taiwan': 35, ' Thailand': 36, ' Trinadad&Tobago': 37, ' United-States': 38, ' Vietnam': 39, ' Yugoslavia': 40}
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}
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# Define the function for making predictions
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def salarybracket(age, workclass, education, education_num, marital_status, occupation, relationship, race, gender, capital_gain, capital_loss, hours_per_week, native_country):
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inputs = np.array([[age, workclass, education, education_num, marital_status, occupation, relationship, race, gender, capital_gain, capital_loss, hours_per_week, native_country]])
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result = "Income_bracket lesser than or equal to 50K ⬇️" if prediction_value <= 0.5 else "Income_bracket greater than 50K ⬆️"
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return f"{result}"
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# Convert mapping to markdown table
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markdown_table = "| Column | Category | Encoded Value |\n|--------|----------|---------------|\n"
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for column, categories in mapping.items():
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for category, value in categories.items():
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markdown_table += f"| {column} | {category} | {value} |\n"
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# Create the Gradio interface
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salarybracket_ga = gr.Interface(fn=salarybracket,
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inputs=[
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gr.Number(17, 90, label="Age [17 to 90]"),
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gr.Number(0, 8, label="Workclass [0 to 8]"),
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gr.Number(0, 15, label="Education [0 to 15]"),
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gr.Number(1, 16, label="Education Num [1 to 16]"),
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gr.Number(0, 6, label="Marital Status [0 to 6]"),
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gr.Number(0, 14, label="Occupation [0 to 14]"),
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gr.Number(0, 5, label="Relationship [0 to 5]"),
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gr.Number(0, 4, label="Race [0 to 4]"),
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gr.Number(0, 1, label="Gender [0 to 1]"),
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gr.Number(0, 99999, label="Capital Gain [0 to 99999]"),
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gr.Number(0, 4356, label="Capital Loss [0 to 4356]"),
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gr.Number(1, 99, label="Hours per Week [1 to 99]"),
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gr.Number(0, 40, label="Native Country [0 to 40]"),
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],
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outputs="text",
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title="Salary Bracket Prediction - Income <=50k or >50K ",
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description=f"Predicting Income_bracket Prediction Using TensorFlow\n\n### Mapping of Categorical Variables\n{markdown_table}",
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theme='dark'
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)
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salarybracket_ga.launch(share=True, debug=True)
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