Update app.py

app.py

@@ -1,77 +1,77 @@
-
-
-
-import gradio as gr
-import pandas as pd
-import joblib
-import numpy as np
-import os
-
-# Load the model
-model = joblib.load("/content/Project/metadata/mlflow/mlartifacts/be863d23729643aea0be7bf045f1fe7c/artifacts/train/model/model.joblib")
-
-
-
-def predict_stroke_risk(age, gender, hypertension, heart_disease, ever_married,
-                       work_type, residence_type, avg_glucose_level, bmi, smoking_status):
-    """Make stroke prediction with the trained model"""
-    
-    # Create a DataFrame with the input data
-    data = pd.DataFrame({
-        'age': [age],
-        'gender': [gender.lower()],
-        'hypertension': [hypertension],
-        'heart_disease': [heart_disease],
-        'ever_married': [ever_married.lower()],
-        'work_type': [work_type.lower()],
-        'Residence_type': [residence_type.lower()],
-        'avg_glucose_level': [avg_glucose_level],
-        'bmi': [bmi],
-        'smoking_status': [smoking_status.lower()]
-    })
-    
-    # Make prediction
-    prediction = model.predict(data)
-    probability = model.predict_proba(data)[0][1]
-    
-    # Create result message
-    if prediction[0] == 1:
-        result = f"High Risk of Stroke (Probability: {probability:.2%})"
-    else:
-        result = f"Low Risk of Stroke (Probability: {probability:.2%})"
-    
-    return result
-
-# Create the Gradio interface
-iface = gr.Interface(
-    fn=predict_stroke_risk,
-    inputs=[
-        gr.Slider(minimum=0, maximum=120, step=1, label="Age"),
-        gr.Dropdown(choices=["Male", "Female", "Other"], label="Gender"),
-        gr.Checkbox(label="Hypertension"),
-        gr.Checkbox(label="Heart Disease"),
-        gr.Dropdown(choices=["Yes", "No"], label="Ever Married"),
-        gr.Dropdown(
-            choices=["Private", "Self-employed", "Govt_job", "Children", "Never_worked"],
-            label="Work Type"
-        ),
-        gr.Dropdown(choices=["Urban", "Rural"], label="Residence Type"),
-        gr.Slider(minimum=50, maximum=300, step=1, label="Average Glucose Level"),
-        gr.Slider(minimum=10, maximum=70, step=0.1, label="BMI"),
-        gr.Dropdown(
-            choices=["formerly smoked", "never smoked", "smokes", "Unknown"],
-            label="Smoking Status"
-        )
-    ],
-    outputs=gr.Text(label="Prediction Result"),
-    title="Stroke Risk Prediction",
-    description="Enter patient information to predict stroke risk.",
-    examples=[
-        [45, "Male", True, False, "Yes", "Private", "Urban", 95.5, 28.5, "never smoked"],
-        [67, "Female", True, True, "Yes", "Self-employed", "Rural", 228.7, 36.6, "formerly smoked"]
-    ]
-)
-
-# Launch the app
-if __name__ == "__main__":
+
+
+
+import gradio as gr
+import pandas as pd
+import joblib
+import numpy as np
+import os
+
+# Load the model
+model = joblib.load("/home/user/app/model.joblib") 
+
+
+
+def predict_stroke_risk(age, gender, hypertension, heart_disease, ever_married,
+                       work_type, residence_type, avg_glucose_level, bmi, smoking_status):
+    """Make stroke prediction with the trained model"""
+    
+    # Create a DataFrame with the input data
+    data = pd.DataFrame({
+        'age': [age],
+        'gender': [gender.lower()],
+        'hypertension': [hypertension],
+        'heart_disease': [heart_disease],
+        'ever_married': [ever_married.lower()],
+        'work_type': [work_type.lower()],
+        'Residence_type': [residence_type.lower()],
+        'avg_glucose_level': [avg_glucose_level],
+        'bmi': [bmi],
+        'smoking_status': [smoking_status.lower()]
+    })
+    
+    # Make prediction
+    prediction = model.predict(data)
+    probability = model.predict_proba(data)[0][1]
+    
+    # Create result message
+    if prediction[0] == 1:
+        result = f"High Risk of Stroke (Probability: {probability:.2%})"
+    else:
+        result = f"Low Risk of Stroke (Probability: {probability:.2%})"
+    
+    return result
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=predict_stroke_risk,
+    inputs=[
+        gr.Slider(minimum=0, maximum=120, step=1, label="Age"),
+        gr.Dropdown(choices=["Male", "Female", "Other"], label="Gender"),
+        gr.Checkbox(label="Hypertension"),
+        gr.Checkbox(label="Heart Disease"),
+        gr.Dropdown(choices=["Yes", "No"], label="Ever Married"),
+        gr.Dropdown(
+            choices=["Private", "Self-employed", "Govt_job", "Children", "Never_worked"],
+            label="Work Type"
+        ),
+        gr.Dropdown(choices=["Urban", "Rural"], label="Residence Type"),
+        gr.Slider(minimum=50, maximum=300, step=1, label="Average Glucose Level"),
+        gr.Slider(minimum=10, maximum=70, step=0.1, label="BMI"),
+        gr.Dropdown(
+            choices=["formerly smoked", "never smoked", "smokes", "Unknown"],
+            label="Smoking Status"
+        )
+    ],
+    outputs=gr.Text(label="Prediction Result"),
+    title="Stroke Risk Prediction",
+    description="Enter patient information to predict stroke risk.",
+    examples=[
+        [45, "Male", True, False, "Yes", "Private", "Urban", 95.5, 28.5, "never smoked"],
+        [67, "Female", True, True, "Yes", "Self-employed", "Rural", 228.7, 36.6, "formerly smoked"]
+    ]
+)
+
+# Launch the app
+if __name__ == "__main__":
     iface.launch()