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| # 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() | |