config space

Dockerfile

@@ -0,0 +1,17 @@
+FROM python:3.13-slim
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+
+RUN apt-get update && apt-get install -y procps && \
+    pip install -U pip && \
+    rm /etc/localtime && \
+    ln -s /usr/share/zoneinfo/America/Mexico_City /etc/localtime && \
+    pip install -r ./requirements.txt
+
+COPY ./api.py /code/
+
+EXPOSE 8000
+
+CMD ["uvicorn","api:app", "--host", "0.0.0.0", "--port", "8000"]

README.md

@@ -7,6 +7,7 @@ sdk: docker
 pinned: false
 license: mit
 short_description: API of the Depression Classification project
+app_port: 8000
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

api.py

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+import pickle
+import mlflow
+from fastapi import FastAPI
+from pydantic import BaseModel
+from mlflow import MlflowClient
+from dotenv import load_dotenv
+import os
+import pandas as pd
+import xgboost as xgb
+
+# Iniciar sesión en databricks
+load_dotenv(override=True)  # Carga las variables del archivo .env
+
+mlflow.set_tracking_uri("databricks")
+client = MlflowClient()
+
+EXPERIMENT_NAME = "/Users/pipochatgpt@gmail.com/Depression_Classification_prefect"
+
+# Buscar el mejor modelo de los 3 candidatos
+run_ = mlflow.search_runs(order_by=["metrics.f1 DESC"],
+                          output_format="list",
+                          filter_string="tags.candidate = 'true'",
+                          experiment_names=[EXPERIMENT_NAME]
+                          )[0]
+run_id = run_.info.run_id
+
+# Descargar artifacts (preprocessor)
+client.download_artifacts(
+    run_id=run_id,
+    path="preprocessor",
+    dst_path="."
+)
+
+with open("preprocessor/encoder.pkl", "rb") as f_in:
+    encoder = pickle.load(f_in)
+
+with open("preprocessor/scaler.pkl", "rb") as f_in:
+    scaler = pickle.load(f_in)
+
+# Cargar modelo campeón del Registry
+model_name = "workspace.default.DepressionClassificationPrefect"
+alias = "champion"
+
+model_uri = f"models:/{model_name}@{alias}"
+model = mlflow.pyfunc.load_model(model_uri)
+
+# Preprocess de entrada
+def preprocess(input_data):
+
+    df = pd.DataFrame([input_data.dict()])
+
+    # Renombrar columnas al formato del modelo entrenado
+    df = df.rename(columns={
+        "AcademicPressure": "Academic Pressure",
+        "StudySatisfaction": "Study Satisfaction",
+        "SleepDuration": "Sleep Duration",
+        "DietaryHabits": "Dietary Habits",
+        "FamilyHistory": "Family History of Mental Illness",
+        "SuicidalThoughts": "Have you ever had suicidal thoughts ?",
+        "FinancialStress": "Financial Stress",
+        "WorkStudyHours": "Work/Study Hours"
+    })
+
+    # Re ordenar columans
+    columnas = ["Gender","Age","City","Academic Pressure","CGPA","Study Satisfaction","Sleep Duration","Dietary Habits","Degree","Have you ever had suicidal thoughts ?","Work/Study Hours","Financial Stress","Family History of Mental Illness"]
+
+    df = df[columnas]
+    
+    # Aplicar el encoder de One Hot
+    city_encoded = encoder.transform(df[["City"]])
+    city_cols = encoder.get_feature_names_out(["City"])
+    city_df = pd.DataFrame(city_encoded, columns=city_cols)
+
+    # Dropear la columna de City 
+    df = df.drop(columns=["City"])
+
+    # Unir ambos datasets por columnas
+    df_proc = pd.concat([df, city_df], axis=1)
+
+    # Escalar los datos
+    df_scaled = scaler.transform(df_proc)
+
+    return df_scaled
+
+
+# Realizar predicciones
+def make_prediction(input_data):
+    X = preprocess(input_data)
+    pred = model.predict(X)
+    return int(pred[0])
+
+# FASTAPI
+app = FastAPI()
+
+# Clase de pydantic de como van los datos
+class InputData(BaseModel):
+    Gender: int
+    Age: int
+    AcademicPressure: float
+    CGPA: float
+    FinancialStress: float
+    StudySatisfaction: float
+    SleepDuration: int
+    DietaryHabits: int
+    WorkStudyHours: float
+    Degree: int
+    City: str
+    FamilyHistory: int
+    SuicidalThoughts: int
+
+@app.post("/predict")
+def predict_endpoint(input_data: InputData):
+    result = make_prediction(input_data)
+    return {"prediction": result}

requirements.txt

@@ -0,0 +1,8 @@
+fastapi==0.122.0
+mlflow==3.6.0
+mlflow_skinny==3.6.0
+mlflow_tracing==3.6.0
+pandas==2.3.3
+pydantic==2.12.4
+python-dotenv==1.2.1
+xgboost==3.1.2