Update app.py

app.py

@@ -1,70 +1,70 @@
-# app.py
-
-import joblib
-import pandas as pd
-import gradio as gr
-
-# Load the scaler and models
-scaler = joblib.load("models/scaler.joblib")
-models = {
-    "processing": joblib.load("models/svm_model_processing.joblib"),
-    "perception": joblib.load("models/svm_model_perception.joblib"),
-    "input": joblib.load("models/svm_model_input.joblib"),
-    "understanding": joblib.load("models/svm_model_understanding.joblib")
-}
-
-def predict(course_overview, reading_file, abstract_materiale, concrete_material, visual_materials,
-           self_assessment, exercises_submit, quiz_submitted, playing, paused, unstarted, buffering):
-    try:
-        input_data = {
-            "course_overview": [course_overview],
-            "reading_file": [reading_file],
-            "abstract_materiale": [abstract_materiale],
-            "concrete_material": [concrete_material],
-            "visual_materials": [visual_materials],
-            "self_assessment": [self_assessment],
-            "exercises_submit": [exercises_submit],
-            "quiz_submitted": [quiz_submitted],
-            "playing": [playing],
-            "paused": [paused],
-            "unstarted": [unstarted],
-            "buffering": [buffering]
-        }
-
-        input_df = pd.DataFrame(input_data)
-        input_scaled = scaler.transform(input_df)
-
-        predictions = {}
-        for target, model in models.items():
-            pred = model.predict(input_scaled)
-            predictions[target] = int(pred[0])
-
-        return predictions
-
-    except Exception as e:
-        return {"error": str(e)}
-
-# Define Gradio interface
-iface = gr.Interface(
-    fn=predict,
-    inputs=[
-        gr.inputs.Number(label="Course Overview"),
-        gr.inputs.Number(label="Reading File"),
-        gr.inputs.Number(label="Abstract Materiale"),
-        gr.inputs.Number(label="Concrete Material"),
-        gr.inputs.Number(label="Visual Materials"),
-        gr.inputs.Number(label="Self Assessment"),
-        gr.inputs.Number(label="Exercises Submit"),
-        gr.inputs.Number(label="Quiz Submitted"),
-        gr.inputs.Number(label="Playing"),
-        gr.inputs.Number(label="Paused"),
-        gr.inputs.Number(label="Unstarted"),
-        gr.inputs.Number(label="Buffering")
-    ],
-    outputs="json",
-    title="SVM Multi-Target Prediction",
-    description="Enter the feature values to get predictions for processing, perception, input, and understanding."
-)
-
-if __name__ == "__main__":
-    iface.launch()
+# app.py
+
+import joblib
+import pandas as pd
+import gradio as gr
+
+# Load the scaler and models
+scaler = joblib.load("models/scaler.joblib")
+models = {
+    "processing": joblib.load("models/svm_model_processing.joblib"),
+    "perception": joblib.load("models/svm_model_perception.joblib"),
+    "input": joblib.load("models/svm_model_input.joblib"),
+    "understanding": joblib.load("models/svm_model_understanding.joblib")
+}
+
+def predict(course_overview, reading_file, abstract_materiale, concrete_material, visual_materials,
+           self_assessment, exercises_submit, quiz_submitted, playing, paused, unstarted, buffering):
+    try:
+        input_data = {
+            "course_overview": [course_overview],
+            "reading_file": [reading_file],
+            "abstract_materiale": [abstract_materiale],
+            "concrete_material": [concrete_material],
+            "visual_materials": [visual_materials],
+            "self_assessment": [self_assessment],
+            "exercises_submit": [exercises_submit],
+            "quiz_submitted": [quiz_submitted],
+            "playing": [playing],
+            "paused": [paused],
+            "unstarted": [unstarted],
+            "buffering": [buffering]
+        }
+
+        input_df = pd.DataFrame(input_data)
+        input_scaled = scaler.transform(input_df)
+
+        predictions = {}
+        for target, model in models.items():
+            pred = model.predict(input_scaled)
+            predictions[target] = int(pred[0])
+
+        return predictions
+
+    except Exception as e:
+        return {"error": str(e)}
+
+# Define Gradio interface using the latest syntax
+iface = gr.Interface(
+    fn=predict,
+    inputs=[
+        gr.Number(label="Course Overview"),
+        gr.Number(label="Reading File"),
+        gr.Number(label="Abstract Materiale"),
+        gr.Number(label="Concrete Material"),
+        gr.Number(label="Visual Materials"),
+        gr.Number(label="Self Assessment"),
+        gr.Number(label="Exercises Submit"),
+        gr.Number(label="Quiz Submitted"),
+        gr.Number(label="Playing"),
+        gr.Number(label="Paused"),
+        gr.Number(label="Unstarted"),
+        gr.Number(label="Buffering")
+    ],
+    outputs=gr.JSON(),
+    title="SVM Multi-Target Prediction",
+    description="Enter the feature values to get predictions for processing, perception, input, and understanding."
+)
+
+if __name__ == "__main__":
+    iface.launch()