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app.py

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+
+import streamlit as st
+from textblob import TextBlob
+import nltk
+from nltk.sentiment.vader import SentimentIntensityAnalyzer
+from transformers import pipeline
+
+# Ensure necessary NLTK data is downloaded
+nltk.download('vader_lexicon')
+
+# Sidebar Navigation
+st.sidebar.title("Mental Health Analyzer")
+page = st.sidebar.radio("Go to", ["Importance of Mental Health", "ML-Based Analysis", "Transformer Tips"])
+
+# Page 1: Importance of Mental Health
+if page == "Importance of Mental Health":
+    st.title("Why Mental Health Matters")
+    st.write("""
+Mental health is crucial for overall well-being. It influences how we think, feel, and act.
+Good mental health can:
+- Improve productivity and performance
+- Enhance relationships
+- Increase self-esteem and confidence
+- Help manage emotions and stress
+
+Remember, it's okay to seek help and talk about your feelings.
+    """)
+
+# Page 2: ML-Based Analysis
+elif page == "ML-Based Analysis":
+    st.title("Analyze Your Mental State")
+    user_input = st.text_area("Share your current thoughts or feelings:")
+    if st.button("Analyze"):
+        if user_input:
+            st.subheader("Analysis Results")
+
+            # TextBlob Sentiment
+            blob = TextBlob(user_input)
+            polarity = blob.sentiment.polarity
+            subjectivity = blob.sentiment.subjectivity
+            st.write(f"**TextBlob Polarity:** {polarity:.2f}")
+            st.write(f"**TextBlob Subjectivity:** {subjectivity:.2f}")
+
+            # VADER Sentiment
+            sid = SentimentIntensityAnalyzer()
+            vader_scores = sid.polarity_scores(user_input)
+            st.write(f"**VADER Compound Score:** {vader_scores['compound']:.2f}")
+
+            # Emotion Detection with Transformers
+            emotion_pipe = pipeline("text-classification", model="bhadresh-savani/distilbert-base-uncased-emotion", top_k=2)
+            emotions = emotion_pipe(user_input)
+            st.write("**Emotion Predictions:**")
+            for emo in emotions:
+                st.write(f"- {emo['label']} (Score: {emo['score']:.2f})")
+
+            # Tips based on polarity
+            st.subheader("Tips to Improve Your Mood")
+            if polarity < -0.2 or vader_scores['compound'] < -0.2:
+                st.write("- Practice mindfulness and deep breathing exercises.")
+                st.write("- Talk to a trusted friend or family member.")
+                st.write("- Engage in light physical activity, like a short walk.")
+            elif polarity > 0.2 and vader_scores['compound'] > 0.2:
+                st.write("- Keep up with activities that make you happy.")
+                st.write("- Share your positive feelings in a gratitude journal.")
+                st.write("- Continue connecting with supportive people.")
+            else:
+                st.write("- Take breaks and reflect on your feelings.")
+                st.write("- Try journaling or meditation to gain clarity.")
+                st.write("- Maintain a balanced routine of work and rest.")
+        else:
+            st.warning("Please enter some text to analyze.")
+
+# Page 3: Transformer-Based Tips Generator
+else:
+    st.title("Get Personalized Tips")
+    user_input = st.text_area("Describe how you're feeling or what you need tips on:")
+    if st.button("Generate Tips"):
+        if user_input:
+            gen = pipeline("text-generation", model="gpt2", max_length=150)
+            prompt = f"As a supportive mental health assistant, provide tips for someone feeling: {user_input}\nTips:"
+            result = gen(prompt, num_return_sequences=1)[0]['generated_text']
+            st.write(result.replace(prompt, "").strip())
+        else:
+            st.warning("Please share your thoughts to get tips.")

requirements.txt

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+
+streamlit
+textblob
+nltk
+transformers
+torch