Update app.py

app.py

@@ -1,32 +1,28 @@
 import gradio as gr
 from transformers import pipeline
 
-# Load sentiment analysis pipeline
+# Load pre-trained sentiment analysis model
 sentiment_analysis = pipeline("sentiment-analysis", model="distilbert/distilbert-base-uncased-finetuned-sst-2-english")
 
-# Function to analyze user's mood based on input
+# analyze user's mood from text
 def analyze_mood(user_input):
-    # Analyze the mood from input text
     result = sentiment_analysis(user_input)[0]
     
-    # Define mood and suggestion based on sentiment analysis
-    mood = "Neutral"  # Default mood
-    suggestion = "You're doing okay! Stay calm 🌸"  # Default suggestion
-    
-    # Adjust mood based on analysis
-    if result["label"] == "POSITIVE" and result["score"] > 0.85:
+    # assign mood based on sentiment
+    if result["label"] == "POSITIVE":
         mood = "Happy"
         suggestion = "Keep doing what you're doing! 😊"
-    elif result["label"] == "NEGATIVE" and result["score"] > 0.85:
+    elif result["label"] == "NEGATIVE":
         mood = "Sad"
         suggestion = "Try to talk to someone, or take a break 💡"
+    else:
+        mood = "Neutral"
+        suggestion = "You're doing okay! Stay calm 🌸"
     
-    # Return mood and suggestion without parentheses and quotes
-    return "Your mood is: " + mood + ". " + suggestion
+    # Output mood and suggestion
+    return "Your mood is " + mood, suggestion
 
-# Set up Gradio inputs and outputs
 inputs = gr.Textbox(label="How are you feeling today?", placeholder="Type your thoughts here...")
 outputs = gr.Textbox(label="Mood and Suggestion")
 
-# Launch the Gradio interface
-gr.Interface(fn=analyze_mood, inputs=inputs, outputs=outputs, title="Mood Analyzer").launch()
+gr.Interface(fn=analyze_mood, inputs=inputs, outputs=outputs, title="Mood Analyzer").launch()