Update app.py

app.py

@@ -1,76 +1,6 @@
-# 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] = pred[0]  # Return as is, without converting to int
-
-#         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()
-
-from fastapi import FastAPI, HTTPException
-from pydantic import BaseModel
 import joblib
 import pandas as pd
+import gradio as gr
 
 # Load the scaler and models
 scaler = joblib.load("models/scaler.joblib")
@@ -78,48 +8,62 @@ 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"),
+    "understanding": joblib.load("models/svm_model_understanding.joblib")
 }
 
-# Initialize the FastAPI app
-app = FastAPI(title="SVM Multi-Target Prediction API")
-
-# Define the input data model
-class InputData(BaseModel):
-    course_overview: float
-    reading_file: float
-    abstract_materiale: float
-    concrete_material: float
-    visual_materials: float
-    self_assessment: float
-    exercises_submit: float
-    quiz_submitted: float
-    playing: float
-    paused: float
-    unstarted: float
-    buffering: float
-
-# Define the prediction endpoint
-@app.post("/predict")
-def predict(input_data: InputData):
-    """
-    Predict target values based on input features using pre-trained SVM models.
-    """
+def predict(course_overview, reading_file, abstract_materiale, concrete_material, visual_materials,
+           self_assessment, exercises_submit, quiz_submitted, playing, paused, unstarted, buffering):
     try:
-        # Convert the input data to a DataFrame
-        input_df = pd.DataFrame([input_data.dict()])
-        
-        # Scale the input data
-        input_scaled = scaler.transform(input_df)
-        
-        # Generate predictions for each target
-        predictions = {
-            target: model.predict(input_scaled)[0]
-            for target, model in models.items()
+        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]
         }
-        return {"predictions": predictions}
 
-    except ValueError as ve:
-        raise HTTPException(status_code=400, detail=f"Input value error: {ve}")
+        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] = pred[0]  # Return as is, without converting to int
+
+        return predictions
+
     except Exception as e:
-        raise HTTPException(status_code=500, detail=f"Unexpected error: {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()
+