app.py

# 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

# 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

# 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']
    
    # 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.")
    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.")
    
    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)

# 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."
)

# Launch the Gradio interface
iface.launch()