Update app.py

app.py

@@ -2,15 +2,13 @@ import streamlit as st
 from transformers import pipeline
 import torch
 
-# Check for device (GPU or CPU) availability
-device = 0 if torch.cuda.is_available() else -1  # Use GPU if available, else use CPU
-
-# Function to load the model with enhanced error handling
+# Function to load the model with error handling and ensure it's available
 @st.cache_resource
 def load_model():
     try:
+        # Attempt to load a model
         st.write("Attempting to load the emotion analysis model...")
-        emotion_analyzer = pipeline("text-classification", model="suryakiran786/T5-emotion", device=device)
+        emotion_analyzer = pipeline("text-classification", model="bhadresh-savani/bert-base-uncased-emotion", device=0 if torch.cuda.is_available() else -1)
         st.write("Model loaded successfully!")
         return emotion_analyzer
     except Exception as e:
@@ -20,7 +18,7 @@ def load_model():
 # Initialize the model (with caching to prevent reloads)
 emotion_analyzer = load_model()
 
-# Check if the model is loaded successfully
+# Check if the model is loaded
 if emotion_analyzer is None:
     st.warning("The emotion analysis model could not be loaded. Please try again.")
 else:
@@ -33,7 +31,7 @@ def predict_emotion_single(response):
         return {"Error": "Emotion analyzer model not initialized. Please check model loading."}
     
     try:
-        response = response.strip()
+        # Analyze the emotion of the response
         result = emotion_analyzer([response])
         return {res["label"]: round(res["score"], 4) for res in result}
     except Exception as e:
@@ -78,4 +76,4 @@ if st.button("Submit Responses"):
             st.write(f"Your Response: {response}")
             st.write(f"Emotion Analysis: {analysis}")
     else:
-        st.write("Please answer all the questions before submitting.")
+        st.write("Please answer all the questions before submitting.")