app.py

app.py

@@ -1,60 +0,0 @@
-from fastapi import FastAPI
-from pydantic import BaseModel
-import pickle
-import numpy as np
-from fastapi.middleware.cors import CORSMiddleware
-
-# 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 aplicación FastAPI
-app = FastAPI()
-# CORS
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-# Definir el endpoint de predicción
-@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 predicción (el modelo debe predecir ca_cervix)
-    prediction = model.predict(input_array)
-    
-    # Retornar la predicción (ca_cervix)
-    return {"ca_cervix_prediction": prediction[0]}
-