Update app.py

app.py

@@ -1,71 +1,71 @@
-!pip install gradio --upgrade  # Upgrade to the latest version of Gradio
-
-import gradio as gr
-import pandas as pd
-import joblib
-from huggingface_hub import hf_hub_download
-
-# Load the model and scaler from Hugging Face Hub
-model_path = hf_hub_download(repo_id="rama0519/DiabeticLogistic123", filename="logistic_regression_model.joblib")
-scaler_path = hf_hub_download(repo_id="rama0519/DiabeticLogistic123", filename="scaler.joblib")
-
-model = joblib.load(model_path)
-scaler = joblib.load(scaler_path)
-
-# Define reasonable ranges for each input parameter
-ranges = {
-    'Pregnancies': (0, 20),
-    'Glucose': (50, 250),
-    'BloodPressure': (40, 140),
-    'SkinThickness': (0, 100),
-    'Insulin': (0, 900),
-    'BMI': (10, 60),
-    'DiabetesPedigreeFunction': (0.0, 2.5),
-    'Age': (18, 100)
-}
-
-# Define the prediction function
-def predict_diabetes(pregnancies, glucose, blood_pressure, skin_thickness, insulin, bmi, diabetes_pedigree_function, age):
-    data = pd.DataFrame({
-        'Pregnancies': [pregnancies],
-        'Glucose': [glucose],
-        'BloodPressure': [blood_pressure],
-        'SkinThickness': [skin_thickness],
-        'Insulin': [insulin],
-        'BMI': [bmi],
-        'DiabetesPedigreeFunction': [diabetes_pedigree_function],
-        'Age': [age]
-    })
-
-    data_scaled = scaler.transform(data)
-    prediction = model.predict(data_scaled)
-
-    # Convert prediction to "Diabetic" (1) or "Not Diabetic" (0)
-    if prediction[0] == 1:
-        prediction_text = "Diabetic"
-    else:
-        prediction_text = "Not Diabetic"
-
-    return prediction_text
-
-# Create the Gradio interface
-# Use gr.Slider to allow for setting min and max values
-interface = gr.Interface(
-    fn=predict_diabetes,
-    inputs=[
-        gr.Slider(label="Pregnancies", minimum=ranges['Pregnancies'][0], maximum=ranges['Pregnancies'][1]),
-        gr.Slider(label="Glucose", minimum=ranges['Glucose'][0], maximum=ranges['Glucose'][1]),
-        gr.Slider(label="BloodPressure", minimum=ranges['BloodPressure'][0], maximum=ranges['BloodPressure'][1]),
-        gr.Slider(label="SkinThickness", minimum=ranges['SkinThickness'][0], maximum=ranges['SkinThickness'][1]),
-        gr.Slider(label="Insulin", minimum=ranges['Insulin'][0], maximum=ranges['Insulin'][1]),
-        gr.Slider(label="BMI", minimum=ranges['BMI'][0], maximum=ranges['BMI'][1]),
-        gr.Slider(label="DiabetesPedigreeFunction", minimum=ranges['DiabetesPedigreeFunction'][0], maximum=ranges['DiabetesPedigreeFunction'][1]),
-        gr.Slider(label="Age", minimum=ranges['Age'][0], maximum=ranges['Age'][1])
-    ],
-    outputs=gr.Textbox(label="Prediction"),
-    title="Diabetes Prediction",
-    description="Enter the medical details to predict if the patient is diabetic or not."
-)
-
-# Launch the interface
+#!pip install gradio --upgrade  # Upgrade to the latest version of Gradio
+
+import gradio as gr
+import pandas as pd
+import joblib
+from huggingface_hub import hf_hub_download
+
+# Load the model and scaler from Hugging Face Hub
+model_path = hf_hub_download(repo_id="rama0519/DiabeticLogistic123", filename="logistic_regression_model.joblib")
+scaler_path = hf_hub_download(repo_id="rama0519/DiabeticLogistic123", filename="scaler.joblib")
+
+model = joblib.load(model_path)
+scaler = joblib.load(scaler_path)
+
+# Define reasonable ranges for each input parameter
+ranges = {
+    'Pregnancies': (0, 20),
+    'Glucose': (50, 250),
+    'BloodPressure': (40, 140),
+    'SkinThickness': (0, 100),
+    'Insulin': (0, 900),
+    'BMI': (10, 60),
+    'DiabetesPedigreeFunction': (0.0, 2.5),
+    'Age': (18, 100)
+}
+
+# Define the prediction function
+def predict_diabetes(pregnancies, glucose, blood_pressure, skin_thickness, insulin, bmi, diabetes_pedigree_function, age):
+    data = pd.DataFrame({
+        'Pregnancies': [pregnancies],
+        'Glucose': [glucose],
+        'BloodPressure': [blood_pressure],
+        'SkinThickness': [skin_thickness],
+        'Insulin': [insulin],
+        'BMI': [bmi],
+        'DiabetesPedigreeFunction': [diabetes_pedigree_function],
+        'Age': [age]
+    })
+
+    data_scaled = scaler.transform(data)
+    prediction = model.predict(data_scaled)
+
+    # Convert prediction to "Diabetic" (1) or "Not Diabetic" (0)
+    if prediction[0] == 1:
+        prediction_text = "Diabetic"
+    else:
+        prediction_text = "Not Diabetic"
+
+    return prediction_text
+
+# Create the Gradio interface
+# Use gr.Slider to allow for setting min and max values
+interface = gr.Interface(
+    fn=predict_diabetes,
+    inputs=[
+        gr.Slider(label="Pregnancies", minimum=ranges['Pregnancies'][0], maximum=ranges['Pregnancies'][1]),
+        gr.Slider(label="Glucose", minimum=ranges['Glucose'][0], maximum=ranges['Glucose'][1]),
+        gr.Slider(label="BloodPressure", minimum=ranges['BloodPressure'][0], maximum=ranges['BloodPressure'][1]),
+        gr.Slider(label="SkinThickness", minimum=ranges['SkinThickness'][0], maximum=ranges['SkinThickness'][1]),
+        gr.Slider(label="Insulin", minimum=ranges['Insulin'][0], maximum=ranges['Insulin'][1]),
+        gr.Slider(label="BMI", minimum=ranges['BMI'][0], maximum=ranges['BMI'][1]),
+        gr.Slider(label="DiabetesPedigreeFunction", minimum=ranges['DiabetesPedigreeFunction'][0], maximum=ranges['DiabetesPedigreeFunction'][1]),
+        gr.Slider(label="Age", minimum=ranges['Age'][0], maximum=ranges['Age'][1])
+    ],
+    outputs=gr.Textbox(label="Prediction"),
+    title="Diabetes Prediction",
+    description="Enter the medical details to predict if the patient is diabetic or not."
+)
+
+# Launch the interface
 interface.launch()