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app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+from sklearn.ensemble import IsolationForest
+from sklearn.preprocessing import StandardScaler
+import matplotlib.pyplot as plt
+from io import BytesIO
+
+# Streamlit UI
+st.title("Saif Check Anomalies")
+st.write("Upload an Excel file to detect anomalies")
+
+uploaded_file = st.file_uploader("Choose an Excel file", type=["xlsx","xls"])
+
+if uploaded_file:
+    # Process the file
+    df = pd.read_excel(uploaded_file)
+
+    # Remove string columns
+    df = df.select_dtypes(include=[int, float])
+
+    # Scale the features
+    scaler = StandardScaler()
+    scaled_data = scaler.fit_transform(df)
+
+    # Fit Isolation Forest model
+    clf = IsolationForest(contamination=0.15, random_state=42)
+    clf.fit(scaled_data)
+    predictions = clf.predict(scaled_data)
+
+    # Identify anomalies
+    anomaly_indices = np.where(predictions == -1)[0]
+    anomalies = df.iloc[anomaly_indices]
+
+    # Display the number of anomalies
+    num_anomalies = len(anomalies)
+    st.subheader(f"Number of anomalies detected: {num_anomalies}")
+
+    # Display anomalies
+    st.subheader("Anomalies Detected")
+    st.write(anomalies)
+
+    # Generate and display graphs
+    for col in df.columns:
+        fig, ax = plt.subplots()
+        ax.plot(df.index, df[col], label="Data")
+        ax.scatter(anomaly_indices, df[col].iloc[anomaly_indices], color='red', label="Anomalies")
+        ax.set_title(f"Anomalies in {col}")
+        ax.legend()
+        st.pyplot(fig)

requirements.txt

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+streamlit
+pandas
+numpy
+scikit-learn
+matplotlib
+openpyxl
+xlrd