added app.py , pickle files, diabetes_ipynb , requirements

.gitignore

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+# Ignore virtual environment
+venv3/
+
+# Ignore environment files
+.env
+
+# Ignore Python compiled files
+*.pyc
+__pycache__/
+
+
+diabetes_prediction.ipynb

app.py

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+import streamlit as st
+import pickle
+import numpy as np
+import pandas as pd
+
+# page configuration
+st.set_page_config(
+    page_title="Diabetes Prediction App",
+    page_icon="🏥",
+    layout="centered"
+)
+
+# loading the model
+@st.cache_resource
+def load_model():
+    try:
+        with open('xgboost_model.pkl', 'rb') as file:
+            model = pickle.load(file)
+        return model
+    except Exception as e:
+        st.error(f"Error loading model: {str(e)}")
+        return None
+
+def preprocess_input(gender, age, hypertension, heart_disease, smoking_history, bmi, hba1c_level, blood_glucose_level):
+    """
+    Preprocess the input data (matching training data)
+    """
+    
+    data = {
+        'gender_Female': [0],
+        'gender_Male': [0],
+        'gender_Other': [0],
+        'smoking_history_No Info': [0],
+        'smoking_history_current': [0],
+        'smoking_history_ever': [0],
+        'smoking_history_former': [0],
+        'smoking_history_never': [0],
+        'smoking_history_not current': [0],
+        'age': [age],
+        'hypertension': [hypertension],
+        'heart_disease': [heart_disease],
+        'bmi': [bmi],
+        'HbA1c_level': [hba1c_level],
+        'blood_glucose_level': [blood_glucose_level]
+    }
+    
+    # gender
+    gender_map = {0:'Female', 1:'Male'}
+    data[f'gender_{gender_map[gender]}'] = [1]
+    
+    # smoking history
+    smoking_map = {
+        0: 'never',
+        1: 'former',
+        2: 'current',
+        3: 'not current',
+        4: 'ever',
+        5: 'No Info'
+    }
+    data[f'smoking_history_{smoking_map[smoking_history]}'] = [1]
+    
+    # dataFrame 
+    df = pd.DataFrame(data)
+    
+    # Ensure exact column order as seen in the training data
+    expected_columns = [
+        'gender_Female', 'gender_Male', 'gender_Other',
+        'smoking_history_No Info', 'smoking_history_current',
+        'smoking_history_ever', 'smoking_history_former',
+        'smoking_history_never', 'smoking_history_not current', 'age',
+        'hypertension', 'heart_disease', 'bmi', 'HbA1c_level',
+        'blood_glucose_level'
+    ]
+    
+    df = df.reindex(columns=expected_columns,fill_value=0)
+    return df
+
+def main():
+    st.title("Diabetes Prediction System 🏥")
+    st.markdown("""
+    This app predicts the likelihood of diabetes based on various health parameters.
+    Please fill in the information below to get a prediction.
+    """)
+    
+    with st.form("prediction_form"):
+        st.subheader("Patient Information")
+        
+        col1, col2 = st.columns(2)
+        
+        with col1:
+            gender = st.selectbox(
+                "Gender",
+                options=[0, 1],
+                format_func=lambda x: "Female" if x == 0 else "Male"
+            )
+            
+            age = st.number_input(
+                "Age",
+                min_value=0,
+                max_value=120,
+                value=40
+            )
+            
+            hypertension = st.selectbox(
+                "Hypertension",
+                options=[0, 1],
+                format_func= lambda x:  "No" if x == 0 else "Yes"
+            )
+            
+            heart_disease = st.selectbox(
+                "Heart Disease",
+                options=[0, 1],
+                format_func= lambda x: "No" if x == 0 else  "Yes"
+            )
+        
+        with col2:
+            smoking_history = st.selectbox(
+                "Smoking History",
+                options=[0, 1, 2, 3, 4, 5],
+                format_func=lambda x: {
+                    0: "Never",
+                    1: "Former",
+                    2: "Current",
+                    3: "Not Current",
+                    4: "Ever",
+                    5: "No Info"
+                }[x]
+            )
+            
+            bmi = st.number_input(
+                "BMI",
+                min_value=10.0,
+                max_value=100.0,
+                value=25.0,
+                step=0.1
+            )
+            
+            hba1c_level = st.number_input(
+                "HbA1c Level",
+                min_value=3.0,
+                max_value=15.0,
+                value=5.5,
+                step=0.1
+            )
+            
+            blood_glucose_level = st.number_input(
+                "Blood Glucose Level",
+                min_value=50,
+                max_value=500,
+                value=120
+            )
+
+        submit_button = st.form_submit_button("Predict")
+
+    model = load_model()
+
+    if submit_button and model is not None:
+        try:
+            # Preprocess the input data
+            input_df = preprocess_input(
+                gender, age, hypertension, heart_disease, 
+                smoking_history, bmi, hba1c_level, blood_glucose_level
+            )
+            
+            # Debug information
+            with st.expander("Show preprocessed features"):
+                st.write(input_df)
+
+            # Make prediction
+            prediction = model.predict(input_df)
+            probability = model.predict_proba(input_df)[0][1]
+
+            # Display results
+            st.subheader("Prediction Results")
+            
+            col1, col2 = st.columns(2)
+            
+            with col1:
+                if prediction[0] == 1:
+                    st.error("High Risk of Diabetes")
+                else:
+                    st.success("Low Risk of Diabetes")
+            
+            with col2:
+                st.metric(
+                    label="Risk Probability",
+                    value=f"{probability:.1%}"
+                )
+
+            # Display input summary
+            st.subheader("Input Summary")
+            summary_df = pd.DataFrame({
+                'Feature': [
+                    'Gender', 'Age', 'Hypertension', 'Heart Disease',
+                    'Smoking History', 'BMI', 'HbA1c Level', 'Blood Glucose Level'
+                ],
+                'Value': [
+                    'Male' if gender == 1 else 'Female',
+                    f"{age} years",
+                    'Yes' if hypertension == 1 else 'No',
+                    'Yes' if heart_disease == 1 else 'No',
+                    {0: "Never", 1: "Former", 2: "Current", 3: "Not Current", 4: "Ever", 5: "No Info"}[smoking_history],
+                    f"{bmi:.1f}",
+                    f"{hba1c_level:.1f}",
+                    f"{blood_glucose_level:.0f}"
+                ]
+            })
+            st.dataframe(summary_df, hide_index=True)
+
+        except Exception as e:
+            st.error(f"Error making prediction: {str(e)}")
+            st.error("Please check the model compatibility with the input features.")
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+fastapi
+streamlit
+pandas
+numpy
+xgboost
+scikit-learn