Update app.py

app.py

@@ -4,36 +4,38 @@ import json
 import joblib
 import torch
 import os
+from huggingface_hub import hf_hub_download
 
 # Function to load models safely
 def load_model(model_path, is_pytorch=False):
     """Loads a model based on the file type, ensuring safe execution."""
     try:
         if is_pytorch:
-            return torch.load(model_path, map_location=torch.device('cpu'), weights_only=False)
+            return torch.load(model_path, map_location=torch.device('cpu'))
         else:
             return joblib.load(model_path)
     except Exception as e:
         st.error(f"Error loading model {model_path}: {e}")
         return None
 
-# Model file paths (ensure they exist)
-distilbert_model_path = "models/distilbert_model.pt"
-bert_topic_model_path = "models/bertopic_model.joblib"
-recommendation_model_path = "models/recommendation_model.joblib"
+# Load models directly from Hugging Face
+REPO_ID = "totoro74/Intelligent_Customer_Analyzer"
 
-# Check if models exist before loading
-if not os.path.exists(distilbert_model_path):
-    st.error(f"Model not found: {distilbert_model_path}")
-if not os.path.exists(bert_topic_model_path):
-    st.error(f"Model not found: {bert_topic_model_path}")
-if not os.path.exists(recommendation_model_path):
-    st.error(f"Model not found: {recommendation_model_path}")
+try:
+    # Load DistilBERT model
+    distilbert_model_path = hf_hub_download(repo_id=REPO_ID, filename="distilbert_model.pt")
+    distilbert_model = torch.load(distilbert_model_path, map_location=torch.device('cpu'))
 
-# Load models safely
-distilbert_model = load_model(distilbert_model_path, is_pytorch=True)
-bert_topic_model = load_model(bert_topic_model_path)
-recommendation_model = load_model(recommendation_model_path)
+    # Load BERTopic model
+    bert_topic_model_path = hf_hub_download(repo_id=REPO_ID, filename="bertopic_model.joblib")
+    bert_topic_model = joblib.load(bert_topic_model_path)
+
+    # Load Recommendation model
+    recommendation_model_path = hf_hub_download(repo_id=REPO_ID, filename="recommendation_model.joblib")
+    recommendation_model = joblib.load(recommendation_model_path)
+
+except Exception as e:
+    st.error(f"⚠️ Error loading models from Hugging Face: {e}")
 
 # Streamlit app layout
 st.title("📊 Intelligent Customer Feedback Analyzer")