Create app.py

app.py

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+# Install necessary libraries
+# pip install streamlit transformers datasets
+
+import streamlit as st
+from transformers import pipeline
+
+# Load pre-trained model from Hugging Face
+emotion_analyzer = pipeline("text-classification", model="distilbert-base-uncased-finetuned-sst-2")
+
+# Define the function to analyze emotions and suggest strategies
+def analyze_and_suggest(responses):
+    suggestions = []
+    for response in responses:
+        # Get the sentiment analysis result
+        result = emotion_analyzer(response)[0]
+        label = result['label']
+
+        # Suggest strategies based on sentiment
+        if label == "NEGATIVE":
+            suggestions.append("Try deep breathing exercises or mindfulness activities.")
+        elif label == "POSITIVE":
+            suggestions.append("Great! Keep the positivity going with a walk or some light exercise.")
+        else:
+            suggestions.append("Consider focusing on better sleep or reflecting on your priorities.")
+    
+    return suggestions
+
+# Streamlit App
+st.title("Personalized Self-Care Strategy App")
+st.markdown("### Answer the following questions to get personalized self-care suggestions.")
+
+# List of questions
+questions = [
+    "1. How do you feel about your overall health today?",
+    "2. How have you been sleeping recently?",
+    "3. Do you feel overwhelmed with tasks or emotions?",
+    "4. What are your energy levels like today?",
+    "5. How often do you exercise or engage in physical activity?"
+]
+
+# Collect user inputs
+responses = []
+for question in questions:
+    responses.append(st.text_input(question, placeholder="Type your response here..."))
+
+# Button to analyze and provide suggestions
+if st.button("Get Self-Care Suggestions"):
+    if all(responses):  # Ensure all questions are answered
+        suggestions = analyze_and_suggest(responses)
+        st.markdown("### **Your Personalized Suggestions**")
+        for i, suggestion in enumerate(suggestions, 1):
+            st.write(f"**{i}.** {suggestion}")
+    else:
+        st.error("Please answer all the questions before proceeding.")