Update app.py

app.py

@@ -7,51 +7,49 @@ model = tf.keras.models.load_model("census.h5")
 
 # Mapping of categorical variables to encoded values
 mapping = {
-    '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},
-    '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},
-    'marital_status': {' Divorced': 0, ' Married-AF-spouse': 1, ' Married-civ-spouse': 2, ' Married-spouse-absent': 3, ' Never-married': 4, ' Separated': 5, ' Widowed': 6},
-    '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},
-    'relationship': {' Husband': 0, ' Not-in-family': 1, ' Other-relative': 2, ' Own-child': 3, ' Unmarried': 4, ' Wife': 5},
-    'race': {' Amer-Indian-Eskimo': 0, ' Asian-Pac-Islander': 1, ' Black': 2, ' Other': 3, ' White': 4},
-    'gender': {' Female': 0, ' Male': 1},
+    '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}, 
+    '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}, 
+    'marital_status': {' Divorced': 0, ' Married-AF-spouse': 1, ' Married-civ-spouse': 2, ' Married-spouse-absent': 3, ' Never-married': 4, ' Separated': 5, ' Widowed': 6}, 
+    '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}, 
+    'relationship': {' Husband': 0, ' Not-in-family': 1, ' Other-relative': 2, ' Own-child': 3, ' Unmarried': 4, ' Wife': 5}, 
+    'race': {' Amer-Indian-Eskimo': 0, ' Asian-Pac-Islander': 1, ' Black': 2, ' Other': 3, ' White': 4}, 
+    'gender': {' Female': 0, ' Male': 1}, 
     '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}
 }
 
 # Define the function for making predictions
-def salarybracket(age, workclass, education, education_num, marital_status, occupation, relationship, race, gender, capital_gain, capital_loss, hours_per_week, native_country):
-    inputs = np.array([[age, workclass, education, education_num, marital_status, occupation, relationship, race, gender, capital_gain, capital_loss, hours_per_week, native_country]])
+def salarybracket(age, workclass, education, marital_status, occupation, relationship, race, gender, native_country):
+    inputs = np.array([[age, workclass, education, marital_status, occupation, relationship, race, gender, native_country]])
     prediction = model.predict(inputs)
     prediction_value = prediction[0][0]  # Assuming the prediction is a scalar
     result = "Income_bracket lesser than or equal to 50K ⬇️" if prediction_value <= 0.5 else "Income_bracket greater than 50K ⬆️"
     return f"{result}"
 
-# Convert mapping to markdown table
-markdown_table = "| Column | Category | Encoded Value |\n|--------|----------|---------------|\n"
-for column, categories in mapping.items():
-    for category, value in categories.items():
-        markdown_table += f"| {column} | {category} | {value} |\n"
+# Convert mapping to Gradio dropdown options
+dropdown_options = {}
+for column, values in mapping.items():
+    options = []
+    for label, value in values.items():
+        options.append({"label": label, "value": value})
+    dropdown_options[column] = options
 
 # Create the Gradio interface
 salarybracket_ga = gr.Interface(fn=salarybracket, 
-                                 inputs=[
-                                     gr.inputs.Slider(17, 90, label="Age"),
-                                     gr.inputs.Dropdown(list(mapping['workclass'].keys()), label="Workclass"),
-                                     gr.inputs.Dropdown(list(mapping['education'].keys()), label="Education"),
-                                     gr.inputs.Slider(1, 16, label="Education Num"),
-                                     gr.inputs.Dropdown(list(mapping['marital_status'].keys()), label="Marital Status"),
-                                     gr.inputs.Dropdown(list(mapping['occupation'].keys()), label="Occupation"),
-                                     gr.inputs.Dropdown(list(mapping['relationship'].keys()), label="Relationship"),
-                                     gr.inputs.Dropdown(list(mapping['race'].keys()), label="Race"),
-                                     gr.inputs.Dropdown(list(mapping['gender'].keys()), label="Gender"),
-                                     gr.inputs.Slider(0, 99999, label="Capital Gain"),
-                                     gr.inputs.Slider(0, 4356, label="Capital Loss"),
-                                     gr.inputs.Slider(1, 99, label="Hours per Week"),
-                                     gr.inputs.Dropdown(list(mapping['native_country'].keys()), label="Native Country"),
-                                 ],
-                                 outputs="text", 
-                                 title="Salary Bracket Prediction - Income <=50k or >50K ", 
-                                 description=f"Predicting Income_bracket Prediction Using TensorFlow\n\n### Mapping of Categorical Variables\n{markdown_table}",
-                                 theme='dark'
-                                )
+                                inputs=[
+                                    gr.Slider(17, 90, label="Age"),
+                                    gr.Dropdown(dropdown_options['workclass'], label="Workclass"),
+                                    gr.Dropdown(dropdown_options['education'], label="Education"),
+                                    gr.Dropdown(dropdown_options['marital_status'], label="Marital Status"),
+                                    gr.Dropdown(dropdown_options['occupation'], label="Occupation"),
+                                    gr.Dropdown(dropdown_options['relationship'], label="Relationship"),
+                                    gr.Dropdown(dropdown_options['race'], label="Race"),
+                                    gr.Dropdown(dropdown_options['gender'], label="Gender"),
+                                    gr.Dropdown(dropdown_options['native_country'], label="Native Country")
+                                ],
+                                outputs="text",
+                                title="Salary Bracket Prediction",
+                                description="Predicting Income Bracket Using TensorFlow",
+                                theme='dark'
+                               )
 
-salarybracket_ga.launch(share=True, debug=True)
+salarybracket_ga.launch(share=True, debug=True)