Update app.py

app.py

@@ -1,110 +1,62 @@
-import streamlit as st
-import os
-
-# Suggestion Database
-suggestion_database = {
-    "sadness": {
-        "suggestions": ["Try a guided meditation", "Take a walk in nature", "Connect with a friend"],
-        "articles": [
-            {"title": "Overcoming Sadness", "url": "https://example.com/sadness1"},
-            {"title": "Understanding Depression", "url": "https://example.com/sadness2"},
-        ],
-        "videos": [
-            {"title": "Mindfulness for Sadness", "url": "https://www.youtube.com/watch?v=sadnessvideo1"},
-            {"title": "Coping with Grief", "url": "https://www.youtube.com/watch?v=sadnessvideo2"},
-        ],
-    },
-    "joy": {
-        "suggestions": ["Practice gratitude", "Engage in a hobby", "Spend time with loved ones"],
-        "articles": [
-            {"title": "The Benefits of Joy", "url": "https://example.com/joy1"},
-            {"title": "Maintaining Positive Emotions", "url": "https://example.com/joy2"},
-        ],
-        "videos": [
-            {"title": "Boosting Your Happiness", "url": "https://www.youtube.com/watch?v=joyvideo1"},
-            {"title": "Practicing Gratitude", "url": "https://www.youtube.com/watch?v=joyvideo2"},
-        ],
-    },
-    "neutral": {
-        "suggestions": ["Take a break", "Engage in a relaxing activity", "Spend time in nature"],
-        "articles": [
-            {"title": "Importance of Self-Care", "url": "https://example.com/selfcare1"},
-            {"title": "Stress Management Techniques", "url": "https://example.com/stress1"},
-        ],
-        "videos": [
-            {"title": "Relaxation Techniques", "url": "https://www.youtube.com/watch?v=relaxvideo1"},
-            {"title": "Mindfulness Exercises", "url": "https://www.youtube.com/watch?v=mindfulnessvideo1"},
-        ],
-    },
-}
-
-# Function to fetch relevant resources
-def get_relevant_resources(emotion):
-    return suggestion_database.get(emotion, {"suggestions": [], "articles": [], "videos": []})
-
-# Debugging Model Initialization
-def load_emotion_model(model_path="path/to/model"):
-    if not os.path.exists(model_path):
-        st.error(f"Model file not found at {model_path}")
-        return None
+# Install necessary libraries (if not already installed)
+# pip install streamlit transformers datasets
 
-    try:
-        # Replace with actual model loading logic
-        model = "Dummy model loaded"
-        st.success("Model loaded successfully!")
-        return model
-    except Exception as e:
-        st.error(f"Error loading model: {e}")
-        return None
-
-# Analyze User Input and Provide Suggestions
-def analyze_emotion(user_input, model):
-    if model is None:
-        return "neutral"  # Default to neutral if model failed to load
-    try:
-        # Dummy emotion analysis (replace with model prediction logic)
-        if "sad" in user_input.lower():
-            return "sadness"
-        elif "happy" in user_input.lower() or "joy" in user_input.lower():
-            return "joy"
+import streamlit as st
+from transformers import pipeline
+from datasets import load_dataset
+
+# Load the pre-trained model for sentiment analysis (using a valid model from Hugging Face)
+emotion_analyzer = pipeline("text-classification", model="distilbert-base-uncased-finetuned-sst-2")
+
+# Load a dataset from Hugging Face (Sentiment Analysis - SST-2)
+dataset = load_dataset("glue", "sst2")
+
+# Example of how to use a dataset (showing the first few examples)
+st.write("Dataset Sample (SST-2):")
+st.write(dataset["train"][0:3])  # Display the first 3 samples
+
+# 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:
-            return "neutral"
-    except Exception as e:
-        st.error(f"Error during emotion analysis: {e}")
-        return "neutral"
-
-# Streamlit Interface
-def main():
-    st.title("Emotion-Based Suggestions")
-
-    # Load Model
-    st.sidebar.title("Model Loader")
-    model_path = st.sidebar.text_input("Model Path", "path/to/model")
-    model = load_emotion_model(model_path)
-
-    # User Input
-    st.header("How are you feeling today?")
-    user_input = st.text_input("Describe your mood in a few words:")
-
-    if user_input:
-        # Analyze Emotion
-        emotion = analyze_emotion(user_input, model)
-        st.subheader(f"Detected Emotion: {emotion.capitalize()}")
-
-        # Fetch and Display Suggestions
-        resources = get_relevant_resources(emotion)
-        st.subheader("Suggestions for You:")
-        for suggestion in resources["suggestions"]:
-            st.write(f"- {suggestion}")
-
-        st.subheader("Articles to Explore:")
-        for article in resources["articles"]:
-            st.write(f"- [{article['title']}]({article['url']})")
-
-        st.subheader("Videos to Watch:")
-        for video in resources["videos"]:
-            st.write(f"- [{video['title']}]({video['url']})")
-
-# Run the App
-if __name__ == "__main__":
-    main()
+            suggestions.append("Consider focusing on better sleep or reflecting on your priorities.")
+    
+    return suggestions
+
+# Streamlit App UI
+st.title("Personalized Self-Care Strategy App")
+st.markdown("### Answer the following questions to get personalized self-care suggestions.")
+
+# List of questions for user to answer
+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 responses
+responses = []
+for question in questions:
+    responses.append(st.text_input(question, placeholder="Type your response here..."))
+
+# Button to analyze and provide self-care 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.")