app.py

import streamlit as st
from transformers import pipeline
import torch
import time

# ---- Page Configuration ----
st.set_page_config(
    page_title="Emotion Prediction App",
    page_icon="🤗",
    layout="centered",
    initial_sidebar_state="expanded",
)

# ---- App Title ----
st.title("🌺 Emotion Prediction App 🌈")
st.subheader("Aloha! Enter your thoughts and let me predict your emotions. 🧠💡")

# ---- Background Information ----
st.markdown(
    """
    Welcome to the Emotion Prediction App! 
    This tool uses a state-of-the-art natural language processing (NLP) model to analyze your responses and predict your emotions.
    Perfect for everyone in Hawaii or anywhere looking for a simple, fun way to understand feelings better! 🌴✨
    """
)

# ---- Function to Load Emotion Analysis Model ----
@st.cache_resource
def load_emotion_model():
    try:
        st.info("⏳ Loading the emotion analysis model, please wait...")
        # Using a public model for emotion classification
        model = pipeline(
            "text-classification",
            model="bhadresh-savani/distilbert-base-uncased-emotion",
            device=0 if torch.cuda.is_available() else -1,  # Automatically use GPU if available
        )
        st.success("✅ Model loaded successfully!")
        return model
    except Exception as e:
        st.error(f"⚠️ Error loading model: {e}")
        return None

# ---- Load the Model ----
emotion_analyzer = load_emotion_model()

# ---- Function for Predicting Emotion ----
def predict_emotion(text):
    if emotion_analyzer is None:
        return {"Error": "Emotion analyzer model not initialized. Please reload the app."}
    
    try:
        # Analyze emotions
        result = emotion_analyzer([text])
        return {res["label"]: round(res["score"], 4) for res in result}
    except Exception as e:
        return {"Error": f"Prediction failed: {e}"}

# ---- User Input Section ----
st.write("### 🌟 Let's Get Started!")

questions = [
    "How are you feeling today?",
    "Describe your mood in a few words.",
    "What was the most significant emotion you felt this week?",
    "How do you handle stress or challenges?",
    "What motivates you the most right now?",
]

responses = {}

# ---- Ask Questions and Analyze Responses ----
for i, question in enumerate(questions, start=1):
    st.write(f"#### 🧐 Question {i}: {question}")
    user_response = st.text_input(f"Your answer to Q{i}:", key=f"q{i}")
    
    if user_response:
        with st.spinner("Analyzing emotion... 🎭"):
            analysis = predict_emotion(user_response)
        responses[question] = {"Response": user_response, "Analysis": analysis}
        st.success(f"🎯 Emotion Analysis: {analysis}")

# ---- Display Results ----
if st.button("Submit Responses"):
    st.write("### 📊 Emotion Analysis Results")
    if responses:
        for i, (question, details) in enumerate(responses.items(), start=1):
            st.write(f"#### Question {i}: {question}")
            st.write(f"**Your Response:** {details['Response']}")
            st.write(f"**Emotion Analysis:** {details['Analysis']}")
    else:
        st.warning("Please answer at least one question before submitting!")

# ---- Footer ----
st.markdown(
    """
    ---
    **Developed with 🤗 Transformers by OpenAI**  
    Designed for an intuitive and aesthetic experience. 🌺  
    """
)