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from fastapi import FastAPI
from pydantic import BaseModel
from hypothesis import BaseModelHypothesis
from random_forest_dependencies import RandomForestDependencies
from random_forest_model import RandomForestModel
from main_model import PredictMainModel
import torch.nn as nn
import torch
import numpy as np
app = FastAPI()
class PredictRequest(BaseModel):
question: str
answer: str
backspace_count: int
typing_duration: int
letter_click_counts: dict[str, int]
@app.post("/predict")
async def predict(request: PredictRequest):
request_dict = request.model_dump()
question = request_dict.get("question")
answer = request_dict.get("answer")
backspace_count = request_dict.get("backspace_count")
typing_duration = request_dict.get("typing_duration")
letter_click_counts = request_dict.get("letter_click_counts")
hypothesis = BaseModelHypothesis()
features_normalized_text_length = hypothesis.calculate_normalized_text_length_features(
answer)
features_not_normalized = hypothesis.calculate_not_normalized_features(
answer)
combined_additional_features = np.concatenate(
(features_normalized_text_length, features_not_normalized), axis=1)
main_model = PredictMainModel()
main_model_probability = main_model.predict(
answer, combined_additional_features)
random_forest_features = RandomForestDependencies()
secondary_model_features = random_forest_features.calculate_features(
question, answer, main_model_probability, backspace_count, typing_duration, letter_click_counts)
secondary_model = RandomForestModel()
secondary_model_prediction = secondary_model.predict(
secondary_model_features)
return {
"main_model_probability": main_model_probability,
"final_prediction": secondary_model_prediction,
"prediction_class": "AI" if secondary_model_prediction == 1 else "HUMAN"
}
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