Create app.py

app.py

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+import pickle
+import gradio as gr
+
+# Load the pickled model
+with model = tf.keras.models.load_model("census.h5")
+
+# 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 = [[age, workclass, education, education_num, marital_status, occupation, relationship, race, gender, capital_gain, capital_loss, hours_per_week, native_country]]
+    prediction = model.predict(inputs)
+    prediction_value = prediction[0]
+   
+    # Categorize prediction_value
+    if prediction_value == 0:
+        result = "Income_bracket lesserthan or equal to 50K "
+    else:
+        result = "Income_bracket greater than to 50K"
+                         
+    return f"Income_bracket Prediction: {prediction_value} \n\nResult: {result}"
+
+
+
+# Create the Gradio interface
+cerviccancer_ga = gr.Interface(fn=cerviccancer, 
+                               inputs = [
+                                            gr.Number(13.0, 84.0, label="Age: [13 to 84]"),
+                                            gr.Number(1.0, 28.0, label="workclass: [1 to 28]"),
+                                            gr.Number(10.0, 32.0, label="education: [10 to 32]"),
+                                            gr.Number(0.0, 11.0, label="education_num: [0 to 11]"),
+                                            gr.Number(0.0, 1.0, label="marital_status: [0 or 1]"),
+                                            gr.Number(0.0, 37.0, label="occupation: [0 to 37]"),
+                                            gr.Number(0.0, 37.0, label="relationship: [0 to 37]"),
+                                            gr.Number(0.0, 1.0, label="race: [0 or 1]"),
+                                            gr.Number(0.0, 30.0, label="gender: [0 to 30]"),
+                                            gr.Number(0.0, 1.0, label="capital_gain: [0 or 1]"),
+                                            gr.Number(0.0, 19.0, label="capital_loss: [0.0  19.0]"),
+                                            gr.Number(0.0, 1.0, label="hours_per_week: [0 or 1]"),
+                                            gr.Number(0.0, 1.0, label="native_country: [0 or 1]"),
+                                        ],
+                                outputs="text", title="Cervical Cancer Risk Prediction", 
+                               examples = [
+                                            [75,0,0,6,6,0,2,1,0,0,0,1,3,0,0],
+                                            [25,4,11,9,2,13,2,4,0,0,0,48,38,1,1],
+                                            [29,4,1,7,4,3,3,2,1,0,0,40,14,0,0],
+                                            [51,5,12,14,2,4,0,4,1,15024,0,50,38,1,1],
+                                            [66,0,15,10,2,0,0,4,1,0,1825,40,38,1,1],
+                                        ],
+                                description="Predicting Income_bracket Prediction Using Machine Learning",
+                                theme='dark'
+                               )
+
+cerviccancer_ga.launch(share=True,debug=True)