Update app.py

app.py

@@ -1,64 +1,112 @@
-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'] =_
+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 (x,y,time) to predict if the user is confused."
+)
+
+interface.launch()