Update app.py

app.py

@@ -1,6 +1,76 @@
+# 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")
@@ -8,61 +78,48 @@ 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"),
 }
 
-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]
-        }
+# Initialize the FastAPI app
+app = FastAPI(title="SVM Multi-Target Prediction API")
 
-        input_df = pd.DataFrame(input_data)
-        input_scaled = scaler.transform(input_df)
+# 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
 
-        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
+# Define the prediction endpoint
+@app.post("/predict")
+def predict(input_data: InputData):
+    """
+    Predict target values based on input features using pre-trained SVM models.
+    """
+    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()
+        }
+        return {"predictions": predictions}
 
+    except ValueError as ve:
+        raise HTTPException(status_code=400, detail=f"Input value error: {ve}")
     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()
+        raise HTTPException(status_code=500, detail=f"Unexpected error: {e}")