Upload 3 files

app.py

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+import gradio as gr
+import pandas as pd
+import numpy as np
+from xgboost import XGBClassifier
+
+# =============== Your feature engineering functions ===============
+def calculate_angle(x1, y1, x2, y2, x3, y3):
+    v1 = [x2 - x1, y2 - y1]
+    v2 = [x3 - x2, y3 - y2]
+    dot = v1[0]*v2[0] + v1[1]*v2[1]
+    mag1 = np.sqrt(v1[0]**2 + v1[1]**2)
+    mag2 = np.sqrt(v2[0]**2 + v2[1]**2)
+    
+    if mag1 == 0 or mag2 == 0:
+        return 0.0
+    cos_theta = np.clip(dot / (mag1 * mag2), -1.0, 1.0)
+    angle = np.degrees(np.arccos(cos_theta))
+    return angle
+
+def label_angle(angle):
+    if np.isnan(angle):    
+        return 0
+    if angle < 30:
+        return 0
+    elif angle < 60:
+        return 1
+    elif angle < 90:
+        return 2
+    elif angle < 120:
+        return 3
+    elif angle < 150:
+        return 4
+    else:
+        return 5
+
+def extract_features(extracted, time_diff=0.05):
+    extracted['distance_covered'] = 0.0
+    extracted['idle_time'] = 0.0
+
+    for i in range(1, len(extracted)):
+        dx = extracted.loc[i, 'x'] - extracted.loc[i-1, 'x']
+        dy = extracted.loc[i, 'y'] - extracted.loc[i-1, 'y']
+        distance = np.sqrt(dx**2 + dy**2)
+        extracted.loc[i, 'distance_covered'] = distance
+
+        if extracted.loc[i, 'x'] == extracted.loc[i-1, 'x'] and extracted.loc[i, 'y'] == extracted.loc[i-1, 'y']:
+            extracted.loc[i, 'idle_time'] = extracted.loc[i-1, 'idle_time'] + time_diff
+        else:
+            extracted.loc[i, 'idle_time'] = 0.0
+
+    extracted['cursor_speed'] = extracted['distance_covered'] / time_diff
+    extracted['acceleration'] = extracted['cursor_speed'] / time_diff
+
+    angles = []
+    for i in range(1, len(extracted) - 1):
+        angle = calculate_angle(
+            extracted.loc[i-1, 'x'], extracted.loc[i-1, 'y'],
+            extracted.loc[i, 'x'], extracted.loc[i, 'y'],
+            extracted.loc[i+1, 'x'], extracted.loc[i+1, 'y']
+        )
+        angles.append(angle)
+
+    extracted = extracted.iloc[1:-1].copy()
+    extracted['movement_angle'] =_

requirements - Copy.txt

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+xgboost==2.0.3
+pandas==2.2.3
+scikit-learn==1.2.2
+numpy==1.26.4
+gradio==4.25.0
+

xgb_confusion_detector.model

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4ac2aa059f96781f58beb0cc6488bf84301ecf4c1454890dc65c530e2f5afbc8
+size 86835