Create app.py

app.py

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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()