Upload folder using huggingface_hub

app.py

@@ -9,6 +9,7 @@ from typing import List, Optional
 # Download your model pickle from the Hub on startup
 #model_path = hf_hub_download(repo_id="Projects-by-IF/causal-model-Z15-v2", filename="trained_causal_model_v4.pkl")
 model_path = hf_hub_download(repo_id="DIGMMUNI/causal_model", filename="trained_causal_model_whole_city.pkl")
+#model_path = hf_hub_download(repo_id="DIGMMUNI/causal_model", filename="trained_causal_model_v1.pkl")
 
 with open(model_path, "rb") as f:
     model = pickle.load(f)
@@ -17,8 +18,10 @@ with open(model_path, "rb") as f:
 # import os
 # MODEL_DIR = r"./model"
 # MODEL_FILE = "trained_causal_model_whole_city.pkl"
+# #MODEL_FILE = "trained_causal_model_v1.pkl"
 # model_path = os.path.join(MODEL_DIR, MODEL_FILE)
 
+print("Loading model from {}".format(model_path))
 app = FastAPI()
 
 #model = joblib.load(model_path)
@@ -37,6 +40,6 @@ def predict(data: InputData):
         T1 = np.array(data.T1)
         effect = model.effect(X=X, T0=T0, T1=T1).tolist()
     else:
-        effect = model.effect(X).tolist()
+      effect = model.effect(X).tolist()
 
     return {"effect": effect}