Update app.py

app.py

@@ -2,39 +2,49 @@ import streamlit as st
 import pandas as pd
 import json
 import joblib
-from huggingface_hub import hf_hub_download
-
-
-import streamlit as st
-import huggingface_hub
+import requests
+from io import BytesIO
+from zipfile import ZipFile
 
 # Display the version of huggingface_hub
+import huggingface_hub
 st.write(f"Hugging Face Hub version: {huggingface_hub.__version__}")
 
+# Function to download models from Hugging Face using requests
+def download_model_from_huggingface(repo_id, filename):
+    """Downloads a model file from Hugging Face repository."""
+    url = f"https://huggingface.co/{repo_id}/resolve/main/{filename}"
+    response = requests.get(url)
+    if response.status_code == 200:
+        return BytesIO(response.content)
+    else:
+        raise Exception(f"Failed to download {filename}")
+
 # Function to load models safely
-def load_model(model_path):
-    """Loads a model based on the file type, ensuring safe execution."""
+def load_model(model_file):
+    """Loads a model from a BytesIO object."""
     try:
-        return joblib.load(model_path)
+        # Here we're assuming the model is in a `.joblib` file format
+        return joblib.load(model_file)
     except Exception as e:
-        st.error(f"Error loading model {model_path}: {e}")
+        st.error(f"Error loading model: {e}")
         return None
 
 # Set repository ID and model filenames
 REPO_ID = "totoro74/Intelligent_Customer_Analyzer"
 
-# Load models directly from Hugging Face
+# Load models using the download function
 try:
-    # Use hf_hub_download to download the model
-    bert_topic_model_path = hf_hub_download(repo_id=REPO_ID, filename="models/bertopic_model.joblib")
-    bert_topic_model = joblib.load(bert_topic_model_path)
+    # Download and load the BERTopic model
+    bert_topic_model_file = download_model_from_huggingface(REPO_ID, "models/bertopic_model.joblib")
+    bert_topic_model = load_model(bert_topic_model_file)
 
-    # Download and load Recommendation model
-    recommendation_model_path = hf_hub_download(repo_id=REPO_ID, filename="models/recommendation_model.joblib")
-    recommendation_model = joblib.load(recommendation_model_path)
+    # Download and load the Recommendation model
+    recommendation_model_file = download_model_from_huggingface(REPO_ID, "models/recommendation_model.joblib")
+    recommendation_model = load_model(recommendation_model_file)
 
 except Exception as e:
-    st.error(f"⚠️ Error loading models from Hugging Face: {e}")
+    st.error(f"⚠️ Error loading models: {e}")
 
 # Streamlit app layout
 st.title("📊 Intelligent Customer Feedback Analyzer")