Update app.py

app.py

@@ -1,36 +1,50 @@
 import gradio as gr
 from transformers import pipeline
 
-# Load the pre-trained model (cached for performance)
+# Load the pre-trained model (using the trained model you provided)
 def load_model():
+    # Use your trained model here; if it's hosted on Hugging Face, provide the path or URL to the model
     return pipeline('sentiment-analysis', model='cardiffnlp/twitter-roberta-base-sentiment')
 
+# Initialize the model
 sentiment_model = load_model()
 
-# Define the function to analyze sentiment
+# 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
-    
-    # Customize messages based on detected sentiment
+
+    # Analyze the mood and provide motivational messages accordingly
     if sentiment == 'negative':
-        return "Mood Detected: Negative 😔\n\nStay positive! 🌟 Remember, tough times don't last, but tough people do!"
+        return (
+            "Mood Detected: Negative 😔\n\n"
+            "Stay positive! 🌟 Remember, tough times don't last, but tough people do!"
+        )
     elif sentiment == 'neutral':
-        return "Mood Detected: Neutral 😐\n\nIt's good to reflect on steady days. Keep your goals in mind, and stay motivated!"
+        return (
+            "Mood Detected: Neutral 😐\n\n"
+            "It's good to reflect on steady days. Keep your goals in mind, and stay motivated!"
+        )
     elif sentiment == 'positive':
-        return "Mood Detected: Positive 😊\n\nYou're on the right track! Keep shining! 🌞"
+        return (
+            "Mood Detected: Positive 😊\n\n"
+            "You're on the right track! Keep shining! 🌞"
+        )
     else:
-        return "Mood Detected: Unknown 🤔\n\nKeep going, you're doing great!"
+        return (
+            "Mood Detected: Unknown 🤔\n\n"
+            "Keep going, you're doing great!"
+        )
 
 # Gradio UI
 def chatbot_ui():
-    # Define the interface
+    # Define the Gradio interface
     interface = gr.Interface(
         fn=analyze_sentiment,
-        inputs=gr.Textbox(label="Enter your text here:"),
+        inputs=gr.Textbox(label="Enter your text here:", placeholder="Type your feelings or thoughts..."),
         outputs=gr.Textbox(label="Motivational Message"),
         title="Student Sentiment Analysis Chatbot",
-        description="This chatbot detects your mood and provides positive or motivational messages."
+        description="This chatbot detects your mood and provides positive or motivational messages based on sentiment analysis."
     )
 
     return interface