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# 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
# 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"),
}
# 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.
"""
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:
raise HTTPException(status_code=500, detail=f"Unexpected error: {e}")
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