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| # Import necessary libraries for streamlit and model loading | |
| import streamlit as st | |
| import pandas as pd | |
| import joblib | |
| from sklearn.preprocessing import StandardScaler | |
| from sklearn.metrics import roc_curve, auc, confusion_matrix, classification_report, accuracy_score | |
| import matplotlib.pyplot as plt | |
| import seaborn as sns | |
| # Load the saved model from Hugging Face Space | |
| #@st.cache(allow_output_mutation=True) | |
| def load_model(): | |
| model_path = 'best_stacking_model.pkl' # Assuming you uploaded the model to the space root directory | |
| model = joblib.load(model_path) | |
| return model | |
| # Function to process uploaded CSV | |
| def process_csv(uploaded_file): | |
| # Read the CSV file | |
| df = pd.read_csv(uploaded_file) | |
| return df | |
| # Streamlit app starts here | |
| st.title("Conjunctivitis Model Analysis App") | |
| # File uploader | |
| uploaded_file = st.file_uploader("Upload your CSV file", type="csv") | |
| if uploaded_file is not None: | |
| # Process uploaded file | |
| df = process_csv(uploaded_file) | |
| st.write("Data Preview:") | |
| st.write(df.head()) | |
| # Split into features and target (assuming the file has a 'Target_goal' column) | |
| if 'Target_goal' not in df.columns: | |
| st.error("Error: The uploaded CSV does not contain a 'Target_goal' column.") | |
| else: | |
| X = df.drop(columns=['Target_goal']) | |
| y = df['Target_goal'] | |
| # Standardize the data | |
| scaler = StandardScaler() | |
| X_scaled = scaler.fit_transform(X) | |
| # Load the model | |
| model = load_model() | |
| # Make predictions | |
| y_pred = model.predict(X_scaled) | |
| y_pred_proba = model.predict_proba(X_scaled)[:, 1] | |
| # Calculate metrics | |
| acc = accuracy_score(y, y_pred) | |
| fpr, tpr, _ = roc_curve(y, y_pred_proba) | |
| roc_auc = auc(fpr, tpr) | |
| st.write(f"Accuracy: {acc:.2f}") | |
| st.write(f"AUC: {roc_auc:.2f}") | |
| # Classification report | |
| st.write("Classification Report:") | |
| report = classification_report(y, y_pred, output_dict=True) | |
| st.write(pd.DataFrame(report).transpose()) | |
| # Confusion Matrix | |
| st.write("Confusion Matrix:") | |
| conf_matrix = confusion_matrix(y, y_pred) | |
| sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues') | |
| plt.title('Confusion Matrix') | |
| plt.xlabel('Predicted') | |
| plt.ylabel('Actual') | |
| st.pyplot(plt.gcf()) | |
| # ROC Curve | |
| st.write("ROC Curve:") | |
| plt.figure(figsize=(8, 6)) | |
| plt.plot(fpr, tpr, color='blue', lw=2, label='ROC Curve (AUC = %0.2f)' % roc_auc) | |
| plt.plot([0, 1], [0, 1], color='gray', lw=1, linestyle='--') | |
| plt.xlim([0.0, 1.0]) | |
| plt.ylim([0.0, 1.05]) | |
| plt.xlabel('False Positive Rate') | |
| plt.ylabel('True Positive Rate') | |
| plt.title('ROC Curve') | |
| plt.legend(loc="lower right") | |
| st.pyplot(plt.gcf()) | |