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README

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+# 🚀 API de Detecção de Anomalias - JMeter
+
+Este Space implementa uma **API FastAPI** para detectar anomalias em testes de carga.
+
+## ▶ Funcionalidades
+
+- Treina baseline com `resultados_carga_normal.csv`
+- Valida com `resultados_carga_anomala.csv`
+- API `/upload` para classificar novos CSVs
+- Retorna:
+  - % de anomalias
+  - se disparou alerta (>5%)
+
+## ▶ Como rodar treinamento no Space
+

Script.py

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+from huggingface_hub import HfApi, create_repo, upload_file
+
+# -------------------------------------------
+# CONFIGURAÇÕES
+# -------------------------------------------
+
+
+REPO_NAME = "ChristianSardo/bimboca-dataset"  # Ex: christian-sardo/bimboca-dataset
+LOCAL_FILE_PATH = r"C:\Users\christian_sardo\Documents\ra\bimboca.csv"
+REMOTE_PATH = "bimboca.csv"  # Nome do arquivo no hub
+
+# -------------------------------------------
+# EXECUÇÃO
+# -------------------------------------------
+
+api = HfApi()
+
+# Criar repo se não existir
+try:
+    create_repo(repo_id=REPO_NAME, token=HF_TOKEN, repo_type="dataset")
+    print(f"Repositório criado: https://huggingface.co/datasets/{REPO_NAME}")
+except Exception as e:
+    print(f"Repo já existe ou outro aviso: {e}")
+
+# Upload do arquivo
+upload_file(
+    token=HF_TOKEN,
+    path_or_fileobj=LOCAL_FILE_PATH,
+    repo_id=REPO_NAME,
+    repo_type="dataset",
+    path_in_repo=REMOTE_PATH
+)
+
+print("Upload completo!")
+print(f"Dataset disponível em: https://huggingface.co/datasets/{REPO_NAME}")

app.py

@@ -3,27 +3,47 @@ import pandas as pd
 import joblib
 from io import BytesIO
 
-app = FastAPI()
+app = FastAPI(title="Detecção de Anomalias JMeter")
+
+MODEL_PATH = "modelo_anomalia.pkl"
+
+# Carrega o modelo na inicialização
+model = joblib.load(MODEL_PATH)
+
+
+def preprocess(df):
+    if df["success"].dtype == object:
+        df["success"] = df["success"].map(lambda x: 1 if str(x).lower() == "true" else 0)
+    return df[["elapsed", "success"]]
 
-model = joblib.load("modelo_anomalia.pkl")
 
 @app.get("/")
 def home():
-    return {"status": "API FastAPI rodando no Hugging Face!"}
+    return {
+        "status": "online",
+        "msg": "API de detecção de anomalias está funcionando!",
+        "rota_upload": "/upload"
+    }
 
-@app.post("/classify")
-async def classify_csv(file: UploadFile = File(...)):
+
+@app.post("/upload")
+async def classify(file: UploadFile = File(...)):
     content = await file.read()
     df = pd.read_csv(BytesIO(content))
 
-    X = df[["elapsed", "success"]]
+    X = preprocess(df)
     preds = model.predict(X)
 
+    df["anomalia"] = preds
+
     percent_anom = (preds == -1).mean() * 100
 
+    alerta = percent_anom > 5
+
     return {
-        "anomalias": int((preds == -1).sum()),
         "total": len(df),
-        "percentual": percent_anom,
-        "limite": percent_anom > 5
+        "anomalias": int((preds == -1).sum()),
+        "percentual_anomalias": percent_anom,
+        "alerta": alerta,
+        "limite": "Ultrapassado (>5%)" if alerta else "Normal",
     }

bimboca.csv

@@ -0,0 +1,51 @@
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requirements.txt

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+fastapi
+uvicorn
+pandas
+scikit-learn
+joblib
+python-multipart
+matplotlib

train.py

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+import pandas as pd
+from sklearn.ensemble import IsolationForest
+from sklearn.preprocessing import LabelEncoder
+import joblib
+
+# ARQUIVOS
+CSV_NORMAL = "resultados_carga_normal.csv"
+CSV_ANORMAL = "resultados_carga_anomala.csv"
+OUTPUT_MODEL = "modelo_anomalia.pkl"
+
+
+def preprocess(df):
+    # Converte success TRUE/FALSE para 1/0 se necessário
+    if df["success"].dtype == object:
+        df["success"] = df["success"].map(lambda x: 1 if str(x).lower() == "true" else 0)
+
+    # Apenas colunas importantes
+    return df[["elapsed", "success"]]
+
+
+def train_baseline():
+    print("🔵 Treinando baseline com resultados normais...")
+
+    df = pd.read_csv(CSV_NORMAL)
+    X = preprocess(df)
+
+    model = IsolationForest(contamination=0.05, random_state=42)
+    model.fit(X)
+
+    joblib.dump(model, OUTPUT_MODEL)
+    print("✅ Modelo salvo como", OUTPUT_MODEL)
+
+
+def validate_anomalies():
+    print("🔴 Validando com dados anômalos...")
+
+    df = pd.read_csv(CSV_ANORMAL)
+    X = preprocess(df)
+
+    model = joblib.load(OUTPUT_MODEL)
+
+    preds = model.predict(X)
+
+    percent_anom = (preds == -1).mean() * 100
+
+    print(f"🚨 Percentual de anomalias detectadas: {percent_anom:.2f}%")
+
+
+if __name__ == "__main__":
+    train_baseline()
+    validate_anomalies()