Update app.py

app.py

@@ -1,56 +1,36 @@
 import gradio as gr
 from transformers import pipeline
 
-# Load the pre-trained model (using the provided model or custom trained model)
+# Load the pre-trained model (cached for performance)
 def load_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
 sentiment_model = load_model()
 
-# Function to analyze sentiment and provide motivational feedback
+# Define the function to analyze sentiment
 def analyze_sentiment(user_input):
-    # Get sentiment prediction
-    result = sentiment_model(user_input)
-
-    if not result:
-        return "Could not determine sentiment. Please try again."
+    result = sentiment_model(user_input)[0]
+    sentiment = result['label'].lower()  # Convert to lowercase for easier comparison
     
-    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
+    # Customize messages based on detected sentiment
     if sentiment == 'negative':
-        return (
-            "Mood Detected: Negative 😔\n\n"
-            "Stay positive! 🌟 Remember, tough times don't last, but tough people do!"
-        )
+        return "Mood Detected: Negative 😔\n\nStay positive! 🌟 Remember, tough times don't last, but tough people do!"
     elif sentiment == 'neutral':
-        return (
-            "Mood Detected: Neutral 😐\n\n"
-            "It's good to reflect on steady days. Keep your goals in mind, and stay motivated!"
-        )
+        return "Mood Detected: Neutral 😐\n\nIt's good to reflect on steady days. Keep your goals in mind, and stay motivated!"
     elif sentiment == 'positive':
-        return (
-            "Mood Detected: Positive 😊\n\n"
-            "You're on the right track! Keep shining! 🌞"
-        )
+        return "Mood Detected: Positive 😊\n\nYou're on the right track! Keep shining! 🌞"
     else:
-        return (
-            "Mood Detected: Unknown 🤔\n\n"
-            "Keep going, you're doing great!"
-        )
+        return "Mood Detected: Unknown 🤔\n\nKeep going, you're doing great!"
 
 # Gradio UI
 def chatbot_ui():
-    # Define the Gradio interface
+    # Define the interface
     interface = gr.Interface(
         fn=analyze_sentiment,
-        inputs=gr.Textbox(label="Enter your text here:", placeholder="Type your feelings or thoughts..."),
+        inputs=gr.Textbox(label="Enter your text here:"),
         outputs=gr.Textbox(label="Motivational Message"),
         title="Student Sentiment Analysis Chatbot",
-        description="This chatbot detects your mood and provides positive or motivational messages based on sentiment analysis."
+        description="This chatbot detects your mood and provides positive or motivational messages."
     )
 
     return interface