Upload FIles

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.ensemble import IsolationForest
+from sklearn.preprocessing import StandardScaler
+
+# Set page title and icon
+st.set_page_config(page_title="Anomaly Detection App", page_icon="🔍")
+
+# Custom CSS for better styling
+st.markdown("""
+<style>
+    .stButton>button {
+        background-color: #4CAF50;
+        color: white;
+        font-weight: bold;
+        border-radius: 5px;
+        padding: 10px 20px;
+    }
+    .stDownloadButton>button {
+        background-color: #008CBA;
+        color: white;
+        font-weight: bold;
+        border-radius: 5px;
+        padding: 10px 20px;
+    }
+    .stMarkdown h1 {
+        color: #4CAF50;
+    }
+    .stMarkdown h2 {
+        color: #008CBA;
+    }
+</style>
+""", unsafe_allow_html=True)
+
+# Title of the app
+st.title("🔍 Anomaly Detection App")
+st.write("""
+This app uses the **Isolation Forest** algorithm to detect anomalies in your dataset.
+Upload a CSV file, and the app will identify anomalies in the data.
+""")
+
+# Upload dataset
+uploaded_file = st.file_uploader("Upload your dataset (CSV file)", type=["csv"])
+
+if uploaded_file is not None:
+    # Load the dataset
+    df = pd.read_csv(uploaded_file)
+    
+    # Show dataset preview
+    st.write("### Dataset Preview")
+    st.write(df.head())
+
+    # Select features for anomaly detection
+    st.write("### Select Features")
+    features = st.multiselect("Choose the features to use for anomaly detection", df.columns)
+
+    if features:
+        # Allow user to adjust contamination parameter
+        st.write("### Adjust Model Parameters")
+        contamination = st.slider("Contamination (proportion of anomalies)", 0.01, 0.5, 0.1, 0.01)
+        
+        # Preprocess the data
+        scaler = StandardScaler()
+        df_scaled = scaler.fit_transform(df[features])
+
+        # Train the Isolation Forest model
+        with st.spinner("Training the model and detecting anomalies..."):
+            model = IsolationForest(n_estimators=100, contamination=contamination, random_state=42)
+            model.fit(df_scaled)
+
+            # Predict anomalies
+            predictions = model.predict(df_scaled)
+            df['anomaly'] = predictions  # -1 for anomaly, 1 for normal
+
+        # Display results
+        st.write("### Anomaly Detection Results")
+        st.write(df)
+
+        # Filter and display only anomalies
+        anomalies = df[df['anomaly'] == -1]
+        st.write(f"### Detected Anomalies (Total: {len(anomalies)})")
+        st.write(anomalies)
+
+        # Visualize anomalies
+        st.write("### Visualize Anomalies")
+        if len(features) >= 2:
+            fig, ax = plt.subplots()
+            sns.scatterplot(data=df, x=features[0], y=features[1], hue='anomaly', palette={1: 'blue', -1: 'red'})
+            st.pyplot(fig)
+        else:
+            st.warning("Please select at least 2 features to visualize anomalies.")
+
+        # Download results as CSV
+        st.write("### Download Results")
+        st.download_button(
+            label="Download Results as CSV",
+            data=df.to_csv(index=False).encode('utf-8'),
+            file_name='anomaly_detection_results.csv',
+            mime='text/csv',
+        )
+else:
+    st.write("Please upload a CSV file to get started.")

requirements.txt

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+scikit-learn
+streamlit
+pandas
+numpy