updateing placeholders

app.py

@@ -3,6 +3,8 @@ import pandas as pd
 import joblib
 from huggingface_hub import hf_hub_download
 
+from modelConnector import ModelConnector
+
 # ===========================
 #  LOAD MODEL & DATASET
 # ===========================

model-loader.py

@@ -1,75 +0,0 @@
-import os
-import joblib
-import pandas as pd
-from huggingface_hub import hf_hub_download, HfApi
-
-
-app = Flask(__name__)
-
-# Hugging Face model & dataset repos
-MODEL_REPO = "taimax13/is_click_predictor"
-MODEL_FILENAME = "rf_model.pkl"
-DATA_REPO = "taimax13/is_click_data"
-LOCAL_MODEL_PATH = f"models/{MODEL_FILENAME}"
-
-# Hugging Face API
-api = HfApi()
-
-
-# ===========================
-#  CHECK IF MODEL EXISTS
-# ===========================
-
-@app.route("/check_model", methods=["GET"])
-def check_model():
-    """Check if a model exists on Hugging Face"""
-    try:
-        model_path = hf_hub_download(repo_id=MODEL_REPO, filename=MODEL_FILENAME)
-        return jsonify({"status": "found", "message": "Model exists", "path": model_path})
-    except Exception:
-        return jsonify({"status": "not_found", "message": "Model not found, needs training"})
-
-
-# ===========================
-#  TRAIN A NEW MODEL
-# ===========================
-
-@app.route("/train", methods=["POST"])
-def train():
-    """Train a new model and upload it to Hugging Face"""
-    try:
-        # Load training dataset
-        train_data_path = hf_hub_download(repo_id=DATA_REPO, filename="train_dataset_full.csv")
-        train_data = pd.read_csv(train_data_path)
-
-        X_train = train_data.drop(columns=["is_click"])
-        y_train = train_data["is_click"]
-
-        # Train model
-        models = train_models(X_train, y_train)
-        rf_model = models["RandomForest"]
-
-        # Save locally
-        os.makedirs("models", exist_ok=True)
-        joblib.dump(rf_model, LOCAL_MODEL_PATH)
-
-        # Upload to Hugging Face
-        api.upload_file(
-            path_or_fileobj=LOCAL_MODEL_PATH,
-            path_in_repo=MODEL_FILENAME,
-            repo_id=MODEL_REPO,
-        )
-
-        return jsonify({"status": "success", "message": "Model trained and uploaded!"})
-
-    except Exception as e:
-        return jsonify({"status": "error", "message": str(e)})
-
-
-# ===========================
-#  RETRAIN EXISTING MODEL
-# ===========================
-
-@app.route("/retrain", methods=["POST"])
-def retrain():
-    """Retrain the model using

modelConnector.py

@@ -0,0 +1,104 @@
+import os
+import joblib
+import pandas as pd
+from huggingface_hub import hf_hub_download, HfApi
+from model_trainer import train_models  # Assumes model_trainer.py exists with train_models function
+
+# Hugging Face Model & Dataset Information
+MODEL_REPO = "taimax13/is_click_predictor"
+MODEL_FILENAME = "rf_model.pkl"
+DATA_REPO = "taimax13/is_click_data"
+LOCAL_MODEL_PATH = f"models/{MODEL_FILENAME}"
+
+# Hugging Face API
+api = HfApi()
+
+
+class ModelConnector:
+    def __init__(self):
+        """Initialize model connector and check if model exists."""
+        os.makedirs("models", exist_ok=True)
+        self.model = self.load_model()
+
+    def check_model_exists(self):
+        """Check if the model exists on Hugging Face."""
+        try:
+            hf_hub_download(repo_id=MODEL_REPO, filename=MODEL_FILENAME)
+            return True
+        except Exception:
+            return False
+
+    def load_model(self):
+        """Download and load the model from Hugging Face."""
+        if self.check_model_exists():
+            model_path = hf_hub_download(repo_id=MODEL_REPO, filename=MODEL_FILENAME)
+            return joblib.load(model_path)
+        return None
+
+    def train_model(self):
+        """Train a new model and upload it to Hugging Face."""
+        try:
+            # Load dataset
+            train_data_path = hf_hub_download(repo_id=DATA_REPO, filename="train_dataset_full.csv")
+            train_data = pd.read_csv(train_data_path)
+
+            X_train = train_data.drop(columns=["is_click"])
+            y_train = train_data["is_click"]
+
+            # Train model
+            models = train_models(X_train, y_train)
+            rf_model = models["RandomForest"]
+
+            # Save locally
+            joblib.dump(rf_model, LOCAL_MODEL_PATH)
+
+            # Upload to Hugging Face
+            api.upload_file(
+                path_or_fileobj=LOCAL_MODEL_PATH,
+                path_in_repo=MODEL_FILENAME,
+                repo_id=MODEL_REPO,
+            )
+
+            self.model = rf_model  # Update instance with trained model
+            return "Model trained and uploaded successfully!"
+
+        except Exception as e:
+            return f"Error during training: {str(e)}"
+
+    def retrain_model(self):
+        """Retrain the existing model with new data."""
+        try:
+            # Load dataset
+            train_data_path = hf_hub_download(repo_id=DATA_REPO, filename="train_dataset_full.csv")
+            train_data = pd.read_csv(train_data_path)
+
+            X_train = train_data.drop(columns=["is_click"])
+            y_train = train_data["is_click"]
+
+            if self.model is None:
+                return "No existing model found. Train a new model first."
+
+            # Retrain the model
+            self.model.fit(X_train, y_train)
+
+            # Save & upload retrained model
+            joblib.dump(self.model, LOCAL_MODEL_PATH)
+            api.upload_file(
+                path_or_fileobj=LOCAL_MODEL_PATH,
+                path_in_repo=MODEL_FILENAME,
+                repo_id=MODEL_REPO,
+            )
+
+            return "Model retrained and uploaded successfully!"
+
+        except Exception as e:
+            return f"Error during retraining: {str(e)}"
+
+    def predict(self, input_data):
+        """Make predictions using the loaded model."""
+        if self.model is None:
+            return "No model found. Train the model first."
+
+        input_df = pd.DataFrame([input_data])
+        prediction = self.model.predict(input_df)[0]
+        return int(prediction)