y

API_fastapi_server.py

@@ -1,42 +1,29 @@
 ## An python API for our Model
 ## Design by edander32 (Edmilson Alexandre) and jjambo(Joaquim Jambo)
 
-from fastapi import FastAPI, HTTPException
-from pydantic import BaseModel
+import gradio as gr
 import pandas as pd
 import joblib
 from huggingface_hub import hf_hub_download
 
-# Inicializa o app
-app = FastAPI(title="Exoplanet Classifier API")
+# Carrega modelo
+model_path = hf_hub_download(repo_id="Edalexan/ia-pkl", filename="exoplanet_model.pkl")
+model = joblib.load(model_path)
 
-# Baixa e carrega o modelo
-model_path = hf_hub_download(
-    repo_id="Edalexan/ia-pkl",  # substitua pelo seu repo/ID
-    filename="exoplanet_model.pkl"
+def predict(feature1, feature2, feature3):
+    df = pd.DataFrame([[feature1, feature2, feature3]], columns=["feature1", "feature2", "feature3"])
+    pred = model.predict(df)[0]
+    prob = model.predict_proba(df)[0][1]
+    return {"prediction": int(pred), "probability": float(prob)}
+
+# Interface mínima só para expor a API
+iface = gr.Interface(
+    fn=predict,
+    inputs=["number", "number", "number"],
+    outputs="json"
 )
-model = joblib.load(model_path)
 
-# Define o modelo de entrada
-class InputData(BaseModel):
-    # Substitua pelos nomes reais das features que seu modelo espera
-    feature1: float
-    feature2: float
-    feature3: float  # e assim por diante
-
-@app.get("/")
-def home():
-    return {"message": "API do classificador de exoplanetas online."}
-
-@app.post("/predict")
-def predict(input_data: InputData):
-    try:
-        # Converte para DataFrame
-        df = pd.DataFrame([input_data.dict()])
-        pred = model.predict(df)[0]
-        prob = model.predict_proba(df)[0][1]
-        return {"prediction": int(pred), "probability": float(prob)}
-    except Exception as e:
-        raise HTTPException(status_code=400, detail=str(e))
+iface.launch(server_name="0.0.0.0", server_port=7860)
+