Update app.py

app.py

@@ -2,56 +2,85 @@ import gradio as gr
 import numpy as np
 import pandas as pd
 from sklearn.ensemble import IsolationForest
-from sklearn.svm import OneClassSVM
+from sklearn.preprocessing import LabelEncoder
+import matplotlib
+matplotlib.use('Agg')  # Use 'Agg' backend for compatibility
 import matplotlib.pyplot as plt
 from PIL import Image
+import os
 
-# Initialize models
-models = {
-    "Isolation Forest": IsolationForest(contamination=0.1),
-    "One-Class SVM": OneClassSVM(nu=0.1)
-}
+# Initialize the Isolation Forest model
+model = IsolationForest(contamination=0.1, random_state=42)
 
-def detect_anomalies(data_input, file_input, algorithm):
+def preprocess_data(data):
+    """
+    Preprocess the input data:
+    - Handle missing values
+    - Encode categorical variables
+    - Ensure all data is numeric
+    """
+    # Handle missing values by filling with the mean of the column
+    data = data.fillna(data.mean(numeric_only=True))
+
+    # Encode categorical variables
+    label_encoders = {}
+    for column in data.select_dtypes(include=['object']).columns:
+        le = LabelEncoder()
+        data[column] = le.fit_transform(data[column])
+        label_encoders[column] = le
+
+    # Ensure all data is numeric
+    data = data.apply(pd.to_numeric, errors='coerce')
+
+    return data, label_encoders
+
+def detect_anomalies(data_input, file_input):
     try:
         # Load data from file if provided
-        if file_input is not None:
-            file_ext = file_input.name.split('.')[-1]
-            if file_ext in ['xls', 'xlsx']:
-                data = pd.read_excel(file_input.name).values
-            elif file_ext == 'csv':
-                data = pd.read_csv(file_input.name).values
+        if file_input:
+            file_extension = os.path.splitext(file_input.name)[-1].lower()
+            if file_extension == ".csv":
+                data = pd.read_csv(file_input.name)
+            elif file_extension == ".json":
+                data = pd.read_json(file_input.name)
             else:
-                return {"Error": f"Unsupported file type: {file_ext}"}, None
+                return {"Error": f"Unsupported file type: {file_extension}"}, None
         else:
             # Use manual input if no file provided
-            data = np.array([float(x) for x in data_input.split(",")]).reshape(-1, 1)
+            data = pd.DataFrame([x.split(",") for x in data_input.split("\n")])
 
-        # Select the chosen model
-        model = models[algorithm]
+        # Preprocess the data
+        data, label_encoders = preprocess_data(data)
 
         # Fit model and predict anomalies
         model.fit(data)
         predictions = model.predict(data)
 
         # Extract anomalies
-        anomalies = data[predictions == -1].flatten()
+        anomalies = data[predictions == -1]
 
         # Create plot
         plt.figure(figsize=(8, 6))
-        plt.plot(data, 'bo', label="Data Points")
-        plt.plot(np.where(predictions == -1)[0], anomalies, 'ro', label="Anomalies")
-        plt.title(f"Anomaly Detection using {algorithm}")
+        plt.plot(data.index, data, 'o', label="Data Points")
+        plt.plot(anomalies.index, anomalies, 'ro', label="Anomalies")
+        plt.title("Anomaly Detection Results")
         plt.xlabel("Data Index")
         plt.ylabel("Data Value")
         plt.legend()
         plt.tight_layout()
 
-        # Save plot to image
-        plt.savefig("temp_plot.png")
-        plot_image = Image.open("temp_plot.png")
+        # Save plot to a temporary file
+        temp_plot_path = "temp_plot.png"
+        plt.savefig(temp_plot_path)
+        plt.close()
+
+        # Convert plot to PIL Image for Gradio
+        plot_image = Image.open(temp_plot_path)
+
+        # Clean up the temporary plot file
+        os.remove(temp_plot_path)
 
-        return {"Anomalies": anomalies.tolist(), "Total Anomalies": len(anomalies)}, plot_image
+        return {"Anomalies": anomalies.to_dict(orient='records'), "Total Anomalies": len(anomalies)}, plot_image
 
     except Exception as e:
         return {"Error": str(e)}, None
@@ -60,16 +89,15 @@ def detect_anomalies(data_input, file_input, algorithm):
 iface = gr.Interface(
     fn=detect_anomalies,
     inputs=[
-        gr.Textbox(label="Enter numbers separated by commas"),
-        gr.File(label="Upload Data File"),
-        gr.Radio(choices=["Isolation Forest", "One-Class SVM"], label="Select Algorithm")
+        gr.Textbox(label="Enter data (comma-separated values, one row per line)"),
+        gr.File(label="Upload Data File")
     ],
     outputs=[
         "json",
         gr.Image(type="pil", label="Anomaly Plot")
     ],
-    title="Anomaly Detection Application",
-    description="Upload a dataset or enter numbers to detect anomalies using the selected algorithm."
+    title="Anomaly Detector",
+    description="Enter a series of numbers or upload a data file to detect anomalies using Isolation Forest."
 )
 
 # Launch the interface