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from fastapi import FastAPI
from pydantic import BaseModel
import pickle
import numpy as np
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import RedirectResponse
# Cargar el modelo desde el archivo .pkl
with open("miarbolcancer.pkl", "rb") as f:
model = pickle.load(f)
# Definir el modelo de datos con Pydantic (sin ca_cervix como entrada)
class PredictionInput(BaseModel):
behavior_sexualRisk: float
behavior_eating: float
behavior_personalHygine: float
intention_aggregation: float
intention_commitment: float
attitude_consistency: float
attitude_spontaneity: float
norm_significantPerson: float
norm_fulfillment: float
perception_vulnerability: float
perception_severity: float
motivation_strength: float
motivation_willingness: float
socialSupport_emotionality: float
socialSupport_appreciation: float
socialSupport_instrumental: float
empowerment_knowledge: float
empowerment_abilities: float
empowerment_desires: float
# Crear la aplicaci贸n FastAPI
app = FastAPI()
# CORS
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
# Redirigir de "/" a "/docs"
@app.get("/")
def redirect_to_docs():
return RedirectResponse(url="/docs")
# Definir el endpoint de predicci贸n
@app.post("/predict/")
def predict(input_data: PredictionInput):
# Convertir los datos de entrada en un array numpy
input_array = np.array([[input_data.behavior_sexualRisk, input_data.behavior_eating, input_data.behavior_personalHygine,
input_data.intention_aggregation, input_data.intention_commitment, input_data.attitude_consistency,
input_data.attitude_spontaneity, input_data.norm_significantPerson, input_data.norm_fulfillment,
input_data.perception_vulnerability, input_data.perception_severity, input_data.motivation_strength,
input_data.motivation_willingness, input_data.socialSupport_emotionality, input_data.socialSupport_appreciation,
input_data.socialSupport_instrumental, input_data.empowerment_knowledge, input_data.empowerment_abilities,
input_data.empowerment_desires]])
# Realizar la predicci贸n (el modelo debe predecir ca_cervix)
prediction = model.predict(input_array)
# Convertir la predicci贸n a tipo nativo Python (int o float)
prediction_value = prediction[0] if isinstance(prediction[0], (int, float)) else prediction[0].item()
# Retornar la predicci贸n (ca_cervix)
return {"ca_cervix_prediction": prediction_value}
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