commit

scripts/train_engagement_rate.py

@@ -4,6 +4,8 @@ from sklearn.model_selection import train_test_split
 from sklearn.metrics import mean_absolute_error
 import joblib
 from utils.preprocessing import preprocess_data
+from huggingface_hub import HfApi, login
+import os
 
 def train_engagement_rate():
     """Train the engagement rate prediction model."""
@@ -22,6 +24,16 @@ def train_engagement_rate():
     mae = mean_absolute_error(y_test, y_pred)
     print(f"Engagement Rate Prediction Model - MAE: {mae:.4f}")
 
-    # Save the model
-    joblib.dump(engagement_model, "models/engagement_rate_model.pkl")
-    print("Engagement Rate Model saved to models/engagement_rate_model.pkl")
+    # Save the model locally
+    joblib.dump(engagement_model, "engagement_rate_model.pkl")
+
+    # Upload the model to Hugging Face Hub
+    login(token=os.environ.get("HF_TOKEN"))  # Use your Hugging Face token
+    api = HfApi()
+    api.upload_file(
+        path_or_fileobj="engagement_rate_model.pkl",
+        path_in_repo="engagement_rate_model.pkl",
+        repo_id="Fred808/Insta-8084",  # Replace with your repo
+        repo_type="model",
+    )
+    print("Engagement Rate Model uploaded to Hugging Face Hub!")

scripts/train_promotion_strategy.py

@@ -4,6 +4,8 @@ from sklearn.model_selection import train_test_split
 from sklearn.metrics import accuracy_score
 import joblib
 from utils.preprocessing import preprocess_data
+from huggingface_hub import HfApi, login
+import os
 
 def train_promotion_strategy():
     """Train the promotion strategy model."""
@@ -25,6 +27,16 @@ def train_promotion_strategy():
     accuracy = accuracy_score(y_test, y_pred)
     print(f"Promotion Prediction Model Accuracy: {accuracy:.4f}")
 
-    # Save the model
-    joblib.dump(promotion_model, "models/promotion_strategy_model.pkl")
-    print("Promotion Strategy Model saved to models/promotion_strategy_model.pkl")
+    # Save the model locally
+    joblib.dump(promotion_model, "promotion_strategy_model.pkl")
+
+    # Upload the model to Hugging Face Hub
+    login(token=os.environ.get("HF_TOKEN"))  # Use your Hugging Face token
+    api = HfApi()
+    api.upload_file(
+        path_or_fileobj="promotion_strategy_model.pkl",
+        path_in_repo="promotion_strategy_model.pkl",
+        repo_id="Fred808/Insta-8084",  # Replace with your repo
+        repo_type="model",
+    )
+    print("Promotion Strategy Model uploaded to Hugging Face Hub!")

scripts/train_time_series.py

@@ -3,6 +3,8 @@ from prophet import Prophet
 from sklearn.metrics import mean_absolute_error
 import joblib
 from utils.preprocessing import preprocess_data
+from huggingface_hub import HfApi, login
+import os
 
 def train_time_series():
     """Train the time-series model for optimal posting times."""
@@ -18,6 +20,16 @@ def train_time_series():
     prophet_model = Prophet()
     prophet_model.fit(time_series_data)
 
-    # Save the model
-    joblib.dump(prophet_model, "models/prophet_model.pkl")
-    print("Prophet Model saved to models/prophet_model.pkl")
+    # Save the model locally
+    joblib.dump(prophet_model, "prophet_model.pkl")
+
+    # Upload the model to Hugging Face Hub
+    login(token=os.environ.get("HF_TOKEN"))  # Use your Hugging Face token
+    api = HfApi()
+    api.upload_file(
+        path_or_fileobj="prophet_model.pkl",
+        path_in_repo="prophet_model.pkl",
+        repo_id="Fred808/Insta-8084",  # Replace with your repo
+        repo_type="model",
+    )
+    print("Prophet Model uploaded to Hugging Face Hub!")

scripts/train_viral_potential.py

@@ -4,6 +4,8 @@ from sklearn.model_selection import train_test_split
 from sklearn.metrics import accuracy_score
 import joblib
 from utils.preprocessing import preprocess_data
+from huggingface_hub import HfApi, login
+import os
 
 def train_viral_potential():
     """Train the viral potential prediction model."""
@@ -25,6 +27,16 @@ def train_viral_potential():
     accuracy = accuracy_score(y_test, y_pred)
     print(f"Viral Potential Model Accuracy: {accuracy:.4f}")
 
-    # Save the model
-    joblib.dump(viral_model, "models/viral_potential_model.pkl")
-    print("Viral Potential Model saved to models/viral_potential_model.pkl")
+    # Save the model locally
+    joblib.dump(viral_model, "viral_potential_model.pkl")
+
+    # Upload the model to Hugging Face Hub
+    login(token=os.environ.get("HF_TOKEN"))  # Use your Hugging Face token
+    api = HfApi()
+    api.upload_file(
+        path_or_fileobj="viral_potential_model.pkl",
+        path_in_repo="viral_potential_model.pkl",
+        repo_id="Fred808/Insta-8084",  # Replace with your repo
+        repo_type="model",
+    )
+    print("Viral Potential Model uploaded to Hugging Face Hub!")