Update app.py

app.py

@@ -1,16 +1,15 @@
 import streamlit as st
 from transformers import pipeline
 import torch
-from datasets import load_dataset
 
-# Ensure proper usage of device (CPU or GPU)
+# Check for device (GPU or CPU) availability
 device = 0 if torch.cuda.is_available() else -1  # Use GPU if available, else use CPU
 
-# Load the T5-based Emotion Classifier model
+# Function to load the model with enhanced error handling
 @st.cache_resource
 def load_model():
     try:
-        st.write("Loading the emotion analysis model...")
+        st.write("Attempting to load the emotion analysis model...")
         emotion_analyzer = pipeline("text-classification", model="suryakiran786/T5-emotion", device=device)
         st.write("Model loaded successfully!")
         return emotion_analyzer
@@ -21,16 +20,25 @@ def load_model():
 # Initialize the model (with caching to prevent reloads)
 emotion_analyzer = load_model()
 
+# Check if the model is loaded successfully
+if emotion_analyzer is None:
+    st.warning("The emotion analysis model could not be loaded. Please try again.")
+else:
+    st.success("Emotion model is ready for predictions!")
+
 # Function to predict emotion for a single response
 def predict_emotion_single(response):
     if emotion_analyzer is None:
+        st.error("Model not loaded. Please try reloading the app.")
         return {"Error": "Emotion analyzer model not initialized. Please check model loading."}
+    
     try:
         response = response.strip()
         result = emotion_analyzer([response])
         return {res["label"]: round(res["score"], 4) for res in result}
     except Exception as e:
-        return {"Error": str(e)}
+        st.error(f"Prediction failed: {e}")
+        return {"Error": f"Prediction failed: {e}"}
 
 # Streamlit App Layout
 st.title("Behavior Prediction App")