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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'] = angles
extracted['prev_movement_angle'] = [0] + angles[1:]
extracted['angle_label'] = extracted['movement_angle'].apply(label_angle)
extracted['prev_angle_label'] = extracted['prev_movement_angle'].apply(label_angle)
return extracted
# =============== Load model ===============
model = XGBClassifier()
model.load_model("xgb_confusion_detector.model")
# =============== Gradio prediction function ===============
def predict_fn(file):
# file is a pandas-readable file (csv, etc.)
raw_df = pd.read_csv(file)
extracted = extract_features(raw_df, time_diff=0.05)
try:
features_to_use = extracted.drop(['isConfused'], axis=1)
except:
features_to_use = extracted
prediction = model.predict(features_to_use)
total_predictions = len(prediction)
confused_predictions = np.sum(prediction == 1)
confusion_ratio = confused_predictions / total_predictions
is_user_confused = confusion_ratio > 0.3
return {
"Prediction": prediction.tolist(),
"Confidence": model.predict_proba(features_to_use).tolist(),
"Confusion Ratio": confusion_ratio,
"User Confused?": is_user_confused
}
# =============== Gradio Interface ===============
interface = gr.Interface(
fn=predict_fn,
inputs=gr.File(label="Upload cursor movement CSV"),
outputs=[
gr.JSON(label="Prediction Details")
],
title="Confusion Detector",
description="Upload a CSV file with cursor movements (timestamp,x,y,isClick) to predict if the user is confused."
)
interface.launch()
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