Update app.py

app.py

@@ -6,6 +6,9 @@ from joblib import load
 import numpy as np
 from sklearn.preprocessing import LabelEncoder
 from time import time
+from huggingface_hub import hf_hub_download
+import pickle
+import os
 
 app = Flask(__name__)
 
@@ -15,6 +18,7 @@ app.secret_key = os.urandom(24)
 # Configurations
 UPLOAD_FOLDER = "uploads/"
 DATA_FOLDER = "data/"
+MODEL_FOLDER = "models/"
 
 # Define the model directory and label encoder directory
 MODEL_DIR = r'./Model'
@@ -29,14 +33,33 @@ ALLOWED_EXTENSIONS = {'csv', 'xlsx'}
 app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
 os.makedirs(app.config['UPLOAD_FOLDER'], exist_ok=True)
 
+app.config['DATA_FOLDER'] = UPLOAD_FOLDER
+os.makedirs(app.config['DATA_FOLDER'], exist_ok=True)
+
+app.config['MODEL_FOLDER'] = UPLOAD_FOLDER
+os.makedirs(app.config['MODEL_FOLDER'], exist_ok=True)
+
 # ------------------------------
 # Load Models and Label Encoders
 # ------------------------------
+
+# prediction analysis
+# Download the model file to the specified location
+file_path = hf_hub_download(
+    repo_id="WebashalarForML/Diamond_model_",
+    filename="models_list/bygrad/CatBoost_best_pipeline_BYGRADE.pkl",
+    cache_dir=specific_location
+)
+
+with open(file_path, "rb") as f:
+    model = pickle.load(f)
+
 gia_model = load(os.path.join(MODEL_DIR, 'linear_regression_model_gia_price.joblib'))
 grade_model = load(os.path.join(MODEL_DIR, 'linear_regression_model_grade_price.joblib'))
 bygrade_model = load(os.path.join(MODEL_DIR, 'linear_regression_model_bygrade_price.joblib'))
 makable_model = load(os.path.join(MODEL_DIR, 'linear_regression_model_makable_price.joblib'))
 
+# classifcation analysis
 col_model = load(os.path.join(MODEL_DIR, 'classification_LogisticRegression_col.joblib'))
 cts_model = load(os.path.join(MODEL_DIR, 'classification_LogisticRegression_cts.joblib'))
 cut_model = load(os.path.join(MODEL_DIR, 'classification_LogisticRegression_cut.joblib'))