app.py

app.py

@@ -1,39 +1,40 @@
 
-"""
-Webapp Front End
-"""
-
 import gradio as gr
 import joblib
 import pandas as pd
 
-MODEL_PATH = "Random_Foresttest_model.pkl"
+# Path to the trained model
+MODEL_PATH = "tuned_model.pkl"
 
+# Load the model
 try:
-    rf_model = joblib.load(MODEL_PATH)
-except FileNotFoundError as e:
-    raise FileNotFoundError(
-        f"Model file not found at {MODEL_PATH}. Please check the path."
-    ) from e
-
-# Define the prediction function
-def predict_with_model(State: float, Sex: float, GeneralHealth: float, PhysicalHealthDays: float, MentalHealthDays: float, LastCheckupTime: float, PhysicalActivities: float, SleepHours: float, HadStroke: float, HadArthritis: float, HadDiabetes: float, SmokerStatus: float, ECigaretteUsage: float, RaceEthnicityCategory: float, AgeCategory: float, HeightInMeters: float, WeightInKilograms: float, BMI: float, AlcoholDrinkers: float, HighRiskLastYear: float):
+    model = joblib.load(MODEL_PATH)
+except FileNotFoundError:
+    raise FileNotFoundError(f"Model file not found at {MODEL_PATH}.")
+
+# Define prediction function
+def predict_with_model(State: str, Sex: str, GeneralHealth: str, PhysicalHealthDays: str, MentalHealthDays: str, LastCheckupTime: str, PhysicalActivities: str, SleepHours: str, HadStroke: str, HadArthritis: str, HadDiabetes: str, SmokerStatus: str, ECigaretteUsage: str, RaceEthnicityCategory: str, AgeCategory: str, HeightInMeters: str, WeightInKilograms: str, BMI: str, AlcoholDrinkers: str, HighRiskLastYear: str):
     try:
-        # Prepare input as a DataFrame
+        # Prepare input data as a DataFrame
         input_data = pd.DataFrame([[State, Sex, GeneralHealth, PhysicalHealthDays, MentalHealthDays, LastCheckupTime, PhysicalActivities, SleepHours, HadStroke, HadArthritis, HadDiabetes, SmokerStatus, ECigaretteUsage, RaceEthnicityCategory, AgeCategory, HeightInMeters, WeightInKilograms, BMI, AlcoholDrinkers, HighRiskLastYear]], columns=['State', 'Sex', 'GeneralHealth', 'PhysicalHealthDays', 'MentalHealthDays', 'LastCheckupTime', 'PhysicalActivities', 'SleepHours', 'HadStroke', 'HadArthritis', 'HadDiabetes', 'SmokerStatus', 'ECigaretteUsage', 'RaceEthnicityCategory', 'AgeCategory', 'HeightInMeters', 'WeightInKilograms', 'BMI', 'AlcoholDrinkers', 'HighRiskLastYear'])
-        prediction = rf_model.predict(input_data)
+        prediction = model.predict(input_data)
         return "Heart Disease Risk" if prediction[0] == 1 else "No Risk"
     except Exception as e:
         return f"Error during prediction: {e}"
 
 # Define the Gradio interface
 with gr.Blocks() as app:
-    gr.Markdown("# Heart Disease Prediction App")
-    gr.Markdown("### Provide input values for the features below and get a prediction.")
+    gr.Markdown("# Health Risk Prediction App")
+    gr.Markdown("### Input the feature values below to predict health risks.")
 
     input_components = []
     for feature in ['State', 'Sex', 'GeneralHealth', 'PhysicalHealthDays', 'MentalHealthDays', 'LastCheckupTime', 'PhysicalActivities', 'SleepHours', 'HadStroke', 'HadArthritis', 'HadDiabetes', 'SmokerStatus', 'ECigaretteUsage', 'RaceEthnicityCategory', 'AgeCategory', 'HeightInMeters', 'WeightInKilograms', 'BMI', 'AlcoholDrinkers', 'HighRiskLastYear']:
-        input_components.append(gr.Slider(0, 100, step=1, label=feature))
+        if "Days" in feature or "Hours" in feature or "BMI" in feature:
+            input_components.append(gr.Slider(0, 100, step=1, label=feature))
+        elif "Sex" in feature:
+            input_components.append(gr.Radio(["Male", "Female"], label=feature))
+        else:
+            input_components.append(gr.Textbox(label=feature))
 
     predict_button = gr.Button("Predict")
     output = gr.Textbox(label="Prediction Result")