Update app.py

app.py

@@ -1,62 +1,17 @@
-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:
-            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.")
-
-transformers-cli cache --clear
 
+# Ensure you have the correct model name
+model_name = "distilbert-base-uncased-finetuned-sst-2"  # Change if needed (verify on Hugging Face model hub)
+
+# Optionally, you can pass a token if the model is private or requires authentication
+# Use your Hugging Face token here if needed (skip this part if the model is public)
+token = "your_token_here"  # Replace with your actual Hugging Face token if needed
+
+# Initialize the emotion analyzer pipeline
+try:
+    emotion_analyzer = pipeline("text-classification", 
+                                model=model_name, 
+                                use_auth_token=token if token else None)
+    print("Model loaded successfully!")
+except Exception as e:
+    print(f"An error occurred: {e}")