Update app.py

app.py

@@ -1,71 +1,29 @@
-# Import required libraries
-import nltk
-from nltk.corpus import stopwords
-from nltk.tokenize import word_tokenize
-from nltk.tag import pos_tag
-from transformers import pipeline
 import gradio as gr
+from transformers import pipeline
 
-# Download NLTK data
-nltk.download('punkt')
-nltk.download('averaged_perceptron_tagger')
-nltk.download('stopwords')
-
-# Load Hugging Face's sentiment analysis pipeline
-sentiment_analyzer = pipeline('sentiment-analysis')
-
-# Function to extract keywords (nouns and verbs)
-def extract_keywords(text):
-    stop_words = set(stopwords.words('english'))
-    words = word_tokenize(text)
-    words_filtered = [word for word in words if word.isalnum() and word.lower() not in stop_words]
-    
-    # Part-of-speech tagging
-    tagged = pos_tag(words_filtered)
-    
-    # Keep only nouns and verbs
-    keywords = [word for word, tag in tagged if tag.startswith('NN') or tag.startswith('VB')]
-    return keywords
+# pipeline
+sentiment_analysis = pipeline("sentiment-analysis", model="distilbert/distilbert-base-uncased-finetuned-sst-2-english")
 
-# Analyze mood and provide suggestions based on keywords
-def analyze_journal(text):
-    keywords = extract_keywords(text)
-    sentiment_result = sentiment_analyzer(text)[0]
-    mood_label = sentiment_result['label']
+# this takes user input and analyzes mood
+def analyze_mood(user_input):
+    # Analyze input text
+    result = sentiment_analysis(user_input)[0]
     
-    # Generate suggestions based on keywords and mood
-    suggestions = []
-    
-    if mood_label == "POSITIVE":
-        suggestions.append("It seems you're feeling good! Keep up the positive activities.")
-    elif mood_label == "NEGATIVE":
-        suggestions.append("It looks like you're feeling down. Consider trying mindfulness exercises or talking to a friend.")
+    # Set the mood
+    if result["label"] == "POSITIVE":
+        mood = "Happy"
+        suggestion = "Keep doing what you're doing! 😊"
+    elif result["label"] == "NEGATIVE":
+        mood = "Sad"
+        suggestion = "Try to talk to someone, or take a break 💡"
     else:
-        suggestions.append("You're feeling neutral. It's a good time to reflect and engage in self-care.")
-
-    # Personalized suggestions based on keywords
-    if 'work' in keywords or 'job' in keywords:
-        suggestions.append("You mentioned work. Remember to balance tasks with self-care to avoid burnout.")
-    
-    if 'stress' in keywords or 'anxious' in keywords:
-        suggestions.append("It seems like you're feeling stressed. Deep breathing exercises or a short walk might help.")
+        mood = "Neutral"
+        suggestion = "You're doing okay! Stay calm 🌸"
     
-    if 'happy' in keywords or 'joy' in keywords:
-        suggestions.append("You're in a good mood! Keep doing activities that bring you joy.")
-
-    if 'tired' in keywords or 'sleep' in keywords:
-        suggestions.append("You're feeling tired. Getting enough rest is important for mental well-being.")
-
-    return f"Keywords: {', '.join(keywords)}\nMood: {mood_label}\n\nSuggestions:\n- " + "\n- ".join(suggestions)
+    # Return the mood and the suggestion for the user
+    return "Your mood is: " + mood, suggestion
 
-# Gradio interface for the journal analyzer
-iface = gr.Interface(
-    fn=analyze_journal,  # Function to call for analyzing the journal
-    inputs=gr.components.Textbox(lines=5, label="Write your journal entry here"),  # Input for journal text
-    outputs="text",  # Output as text (keywords, mood, and suggestions)
-    title="Mental Health Mood Analyzer",
-    description="Write about your day, and the analyzer will suggest improvements based on your mood and keywords."
-)
+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
-iface.launch()
+gr.Interface(fn=analyze_mood, inputs=inputs, outputs=outputs, title="Mood Analyzer").launch()