Update app.py

app.py

@@ -1,9 +1,9 @@
 import gradio as gr
 from transformers import pipeline
 
-# Load the pre-trained model (using the trained model you provided)
+# Load the pre-trained model (using the provided model or custom trained model)
 def load_model():
-    # Use your trained model here; if it's hosted on Hugging Face, provide the path or URL to the model
+    # Use Hugging Face's 'cardiffnlp/twitter-roberta-base-sentiment' or a custom model path
     return pipeline('sentiment-analysis', model='cardiffnlp/twitter-roberta-base-sentiment')
 
 # Initialize the model
@@ -11,8 +11,14 @@ sentiment_model = load_model()
 
 # Function to analyze sentiment and provide motivational feedback
 def analyze_sentiment(user_input):
-    result = sentiment_model(user_input)[0]
-    sentiment = result['label'].lower()  # Convert to lowercase for easier comparison
+    # Get sentiment prediction
+    result = sentiment_model(user_input)
+
+    if not result:
+        return "Could not determine sentiment. Please try again."
+    
+    sentiment = result[0]['label'].lower()  # Extract sentiment label and convert to lowercase for comparison
+    print(f"Sentiment Analysis Result: {result}")  # Debug: Print model result for review
 
     # Analyze the mood and provide motivational messages accordingly
     if sentiment == 'negative':