Update app.py

app.py

@@ -1,7 +1,15 @@
 import streamlit as st
 import pandas as pd
-from scripts.load_models import distilbert_model, bert_topic_model, recommendation_model
-import gdown
+import json
+import joblib
+import os
+
+# Load models from the "models" folder
+models_dir = "models"
+# distilbert_model = joblib.load(os.path.join(models_dir, "distilbert_model.joblib"))
+bert_topic_model = joblib.load(os.path.join(models_dir, "bert_topic_model.joblib"))
+recommendation_model = joblib.load(os.path.join(models_dir, "recommendation_model.joblib"))
+
 # Streamlit app layout
 st.title("Intelligent Customer Feedback Analyzer")
 st.write("Analyze customer feedback for sentiment, topics, and get personalized recommendations.")
@@ -38,19 +46,18 @@ if uploaded_file:
         for feedback_text in feedback_text_list:
             if st.button(f'Analyze Feedback: "{feedback_text[:30]}..."'):
                 # Sentiment Analysis
-                sentiment = distilbert_model.predict([feedback_text])
+                sentiment = distilbert_model.predict([feedback_text])[0]  # Get the first result
                 sentiment_result = 'Positive' if sentiment == 1 else 'Negative'
                 st.write(f"Sentiment: {sentiment_result}")
 
                 # Topic Modeling
-                topics = bert_topic_model.predict([feedback_text])
+                topics = bert_topic_model.predict([feedback_text])[0]  # Get the first topic
                 st.write(f"Predicted Topic(s): {topics}")
 
                 # Recommendation System
-                recommendations = recommendation_model.predict([feedback_text])
+                recommendations = recommendation_model.predict([feedback_text])[0]  # Get the first recommendation
                 st.write(f"Recommended Actions: {recommendations}")
     else:
         st.error("Unable to extract feedback from the file.")
 else:
     st.info("Please upload a feedback file to analyze.")
-