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app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import accuracy_score, classification_report
+
+# Set page config
+st.set_page_config(
+    page_title="Diabetes Detection App",
+    page_icon="🏥",
+    layout="wide"
+)
+
+# Title
+st.title("Diabetes Detection App")
+st.write("This app predicts diabetes using various health metrics.")
+
+# Load data
+@st.cache_data
+def load_data():
+    # Load the Pima Indians Diabetes Database
+    columns = ['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 
+               'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome']
+    
+    data = pd.read_csv('https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv', 
+                       names=columns)
+    return data
+
+# Load and prepare data
+data = load_data()
+
+# Sidebar for user input
+st.sidebar.header('User Input Features')
+
+def user_input_features():
+    pregnancies = st.sidebar.slider('Pregnancies', 0, 17, 3)
+    glucose = st.sidebar.slider('Glucose', 0, 200, 120)
+    blood_pressure = st.sidebar.slider('Blood Pressure', 0, 122, 70)
+    skin_thickness = st.sidebar.slider('Skin Thickness', 0, 100, 20)
+    insulin = st.sidebar.slider('Insulin', 0, 846, 79)
+    bmi = st.sidebar.slider('BMI', 0.0, 67.1, 31.4)
+    diabetes_pedigree = st.sidebar.slider('Diabetes Pedigree Function', 0.078, 2.42, 0.3725)
+    age = st.sidebar.slider('Age', 21, 81, 29)
+    
+    data = {
+        'Pregnancies': pregnancies,
+        'Glucose': glucose,
+        'BloodPressure': blood_pressure,
+        'SkinThickness': skin_thickness,
+        'Insulin': insulin,
+        'BMI': bmi,
+        'DiabetesPedigreeFunction': diabetes_pedigree,
+        'Age': age
+    }
+    return pd.DataFrame(data, index=[0])
+
+# Get user input
+user_data = user_input_features()
+
+# Display user input
+st.subheader('User Input Features')
+st.write(user_data)
+
+# Prepare the model
+X = data.drop('Outcome', axis=1)
+y = data['Outcome']
+
+# Split the data
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Scale the features
+scaler = StandardScaler()
+X_train_scaled = scaler.fit_transform(X_train)
+X_test_scaled = scaler.transform(X_test)
+
+# Train the model
+model = RandomForestClassifier(n_estimators=100, random_state=42)
+model.fit(X_train_scaled, y_train)
+
+# Make prediction on user input
+user_data_scaled = scaler.transform(user_data)
+prediction = model.predict(user_data_scaled)
+prediction_proba = model.predict_proba(user_data_scaled)
+
+# Show prediction
+st.subheader('Prediction')
+if prediction[0] == 0:
+    st.write('The model predicts: No Diabetes')
+else:
+    st.write('The model predicts: Diabetes')
+
+st.subheader('Prediction Probability')
+st.write(f'Probability of No Diabetes: {prediction_proba[0][0]:.2%}')
+st.write(f'Probability of Diabetes: {prediction_proba[0][1]:.2%}')
+
+# Model performance
+st.subheader('Model Performance')
+y_pred = model.predict(X_test_scaled)
+st.write(f'Model Accuracy: {accuracy_score(y_test, y_pred):.2%}')
+
+# Feature importance
+st.subheader('Feature Importance')
+feature_importance = pd.DataFrame({
+    'Feature': X.columns,
+    'Importance': model.feature_importances_
+}).sort_values('Importance', ascending=False)
+
+st.bar_chart(feature_importance.set_index('Feature'))

devcontainer.json

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+{
+  "name": "Python 3",
+  // Or use a Dockerfile or Docker Compose file. More info: https://containers.dev/guide/dockerfile
+  "image": "mcr.microsoft.com/devcontainers/python:1-3.11-bullseye",
+  "customizations": {
+    "codespaces": {
+      "openFiles": [
+        "README.md",
+        "app.py"
+      ]
+    },
+    "vscode": {
+      "settings": {},
+      "extensions": [
+        "ms-python.python",
+        "ms-python.vscode-pylance"
+      ]
+    }
+  },
+  "updateContentCommand": "[ -f packages.txt ] && sudo apt update && sudo apt upgrade -y && sudo xargs apt install -y <packages.txt; [ -f requirements.txt ] && pip3 install --user -r requirements.txt; pip3 install --user streamlit; echo '✅ Packages installed and Requirements met'",
+  "postAttachCommand": {
+    "server": "streamlit run app.py --server.enableCORS false --server.enableXsrfProtection false"
+  },
+  "portsAttributes": {
+    "8501": {
+      "label": "Application",
+      "onAutoForward": "openPreview"
+    }
+  },
+  "forwardPorts": [
+    8501
+  ]
+}

requirements.txt

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+altair==5.5.0
+attrs==25.1.0
+blinker==1.9.0
+cachetools==5.5.1
+certifi==2025.1.31
+charset-normalizer==3.4.1
+click==8.1.8
+gitdb==4.0.12
+gitpython==3.1.44
+idna==3.10
+jinja2==3.1.5
+joblib==1.4.2
+jsonschema==4.23.0
+jsonschema-specifications==2024.10.1
+markdown-it-py==3.0.0
+markupsafe==3.0.2
+mdurl==0.1.2
+narwhals==1.25.1
+numpy==2.2.2
+packaging==24.2
+pandas==2.2.3
+pillow==11.1.0
+protobuf==5.29.3
+pyarrow==19.0.0
+pydeck==0.9.1
+pygments==2.19.1
+python-dateutil==2.9.0.post0
+pytz==2025.1
+referencing==0.36.2
+requests==2.32.3
+rich==13.9.4
+rpds-py==0.22.3
+scikit-learn==1.6.1
+scipy==1.15.1
+six==1.17.0
+smmap==5.0.2
+streamlit==1.42.0
+tenacity==9.0.0
+threadpoolctl==3.5.0
+toml==0.10.2
+tornado==6.4.2
+typing-extensions==4.12.2
+tzdata==2025.1
+urllib3==2.3.0