app.py

# app.py
import streamlit as st
import torch
import random
import altair as alt
import pandas as pd
from datetime import datetime
from reportlab.lib.pagesizes import letter
from reportlab.pdfgen import canvas
import io
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from groq import Groq

# Initialize components
try:
    groq_client = Groq(api_key=st.secrets["GROQ_API_KEY"])
    model = AutoModelForSequenceClassification.from_pretrained("KevSun/Personality_LM", ignore_mismatched_sizes=True)
    tokenizer = AutoTokenizer.from_pretrained("KevSun/Personality_LM")
except Exception as e:
    st.error(f"Initialization error: {str(e)}")
    st.stop()

# Configure Streamlit
st.set_page_config(page_title="🧠 PersonaCraft", layout="wide", page_icon="🌟")

# Custom CSS
st.markdown("""
<style>
@keyframes fadeIn {
  from { opacity: 0; }
  to { opacity: 1; }
}

.quote-box {
    animation: fadeIn 1s ease-in;
    border-left: 5px solid #4CAF50;
    padding: 20px;
    margin: 20px 0;
    background: #f8fff9;
    border-radius: 10px;
    box-shadow: 0 4px 6px rgba(0,0,0,0.1);
}

.tip-card {
    padding: 15px;
    margin: 10px 0;
    background: #fff3e0;
    border-radius: 10px;
    border: 1px solid #ffab40;
}

.social-post {
    background: #e3f2fd;
    padding: 20px;
    border-radius: 15px;
    margin: 15px 0;
}

.viz-box {
    background: white;
    padding: 20px;
    border-radius: 15px;
    box-shadow: 0 2px 4px rgba(0,0,0,0.1);
}

.nav-btn {
    margin: 8px 0;
    width: 100%;
}
</style>
""", unsafe_allow_html=True)

# Personality configuration
OCEAN_TRAITS = ["agreeableness", "openness", "conscientiousness", "extraversion", "neuroticism"]
QUESTION_BANK = [
    {"text": "If your personality was a pizza topping, what would it be? 🍕", "trait": "openness"},
    {"text": "Describe your ideal alarm clock vs reality ⏰", "trait": "conscientiousness"},
    {"text": "How would you survive a surprise party? 🎉", "trait": "neuroticism"},
    {"text": "What's your spirit animal during deadlines? 🐾", "trait": "agreeableness"},
    {"text": "Plan a perfect day... for your nemesis! 😈", "trait": "extraversion"},
    {"text": "If stress was a color, what's your current shade? 🎨", "trait": "neuroticism"},
    {"text": "What would your browser history say about you? 🌐", "trait": "openness"},
    {"text": "Describe your phone's home screen as a poem 📱", "trait": "conscientiousness"},
    {"text": "React to 'Your idea is interesting, but...' 💡", "trait": "agreeableness"},
    {"text": "What's your superpower in awkward situations? 🦸", "trait": "extraversion"}
]

# Session state management
if 'started' not in st.session_state:
    st.session_state.started = False
if 'current_q' not in st.session_state:
    st.session_state.current_q = 0
if 'responses' not in st.session_state:
    st.session_state.responses = []
if 'page' not in st.session_state:
    st.session_state.page = "🏠 Home"

# Functions
def generate_quote():
    prompt = "Generate a fresh motivational quote about self-discovery with an emoji"
    response = groq_client.chat.completions.create(
        model="mixtral-8x7b-32768",
        messages=[{"role": "user", "content": prompt}],
        temperature=0.7
    )
    return response.choices[0].message.content

def analyze_personality(text):
    encoded_input = tokenizer(text, return_tensors='pt', padding=True, truncation=True, max_length=64)
    with torch.no_grad():
        outputs = model(**encoded_input)
    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
    return {trait: score.item() for trait, score in zip(OCEAN_TRAITS, predictions[0])}

def create_pdf_report(traits, quote):
    buffer = io.BytesIO()
    p = canvas.Canvas(buffer, pagesize=letter)
    p.setFont("Helvetica-Bold", 16)
    p.drawString(100, 750, "🌟 PersonaCraft Psychological Report 🌟")
    p.setFont("Helvetica", 12)
    
    y_position = 700
    for trait, score in traits.items():
        p.drawString(100, y_position, f"{trait.upper()}: {score:.2f}")
        y_position -= 20
    
    p.drawString(100, y_position-40, "Motivational Quote:")
    p.drawString(100, y_position-60, quote)
    p.save()
    buffer.seek(0)
    return buffer

def generate_social_post(platform, traits):
    platform_formats = {
        "LinkedIn": "professional tone with industry hashtags",
        "Instagram": "visual storytelling with emojis",
        "Facebook": "friendly community-oriented style",
        "WhatsApp": "casual conversational format",
        "Twitter": "concise with trending hashtags"
    }
    prompt = f"""Create a {platform} post about personal growth with these personality traits:
{traits}
Format: {platform_formats[platform]}
Include relevant emojis and keep under 280 characters."""
    
    response = groq_client.chat.completions.create(
        model="mixtral-8x7b-32768",
        messages=[{"role": "user", "content": prompt}],
        temperature=0.7
    )
    return response.choices[0].message.content

# Main UI
if not st.session_state.started:
    st.markdown(f"""
    <div class="quote-box">
        <h2>🌟 Welcome to PersonaCraft! 🌟</h2>
        <h3>{generate_quote()}</h3>
    </div>
    """, unsafe_allow_html=True)
    
    if st.button("🚀 Start Personality Journey!", use_container_width=True):
        st.session_state.started = True
        st.session_state.selected_questions = random.sample(QUESTION_BANK, 5)
        st.rerun()
else:
    # Sidebar navigation
    with st.sidebar:
        st.title("🧭 Navigation")
        st.markdown("---")
        if st.button("📋 Personality Report", key="btn1", use_container_width=True, 
                    help="View your detailed personality analysis"):
            st.session_state.page = "📋 Personality Report"
        
        if st.button("📊 Visual Analysis", key="btn2", use_container_width=True,
                    help="Explore visual representations of your personality"):
            st.session_state.page = "📊 Visual Analysis"
        
        if st.button("📱 Social Media Post", key="btn3", use_container_width=True,
                    help="Generate personalized social media posts"):
            st.session_state.page = "📱 Social Media Post"
        
        if st.button("💡 Success Tips", key="btn4", use_container_width=True,
                    help="Discover personalized improvement tips"):
            st.session_state.page = "💡 Success Tips"
        
        if st.button("📥 Download Report", key="btn5", use_container_width=True,
                    help="Download your complete personality report"):
            st.session_state.page = "📥 Download Report"

    # Question flow
    if st.session_state.current_q < 5:
        q = st.session_state.selected_questions[st.session_state.current_q]
        st.progress(st.session_state.current_q/5, text="Assessment Progress")
        
        with st.chat_message("assistant"):
            st.markdown(f"### {q['text']}")
            user_input = st.text_input("Your response:", key=f"q{st.session_state.current_q}")
            
            if st.button("Next ➡️"):
                st.session_state.responses.append(user_input)
                st.session_state.current_q += 1
                st.rerun()
    else:
        # Process responses
        traits = analyze_personality("\n".join(st.session_state.responses))
        quote = generate_quote()
        
        # Current page display
        if st.session_state.page == "📋 Personality Report":
            st.header("📋 Personality Breakdown")
            cols = st.columns(5)
            for i, (trait, score) in enumerate(traits.items()):
                cols[i].metric(label=trait.upper(), value=f"{score:.2f}")
            
            st.divider()
            st.header("🎭 Emotional Landscape")
            emotion_data = pd.DataFrame({
                "Trait": traits.keys(),
                "Score": traits.values()
            })
            st.altair_chart(alt.Chart(emotion_data).mark_bar().encode(
                x="Trait",
                y="Score",
                color=alt.Color("Trait", legend=None)
            ), use_container_width=True)

        elif st.session_state.page == "📊 Visual Analysis":
            st.header("📊 Personality Visualization")
            # Fix radar chart encoding
            radar_data = pd.DataFrame({
                "Trait": list(traits.keys()),
                "Score": list(traits.values()),
                "Angle": [i*(360/5) for i in range(5)]
            })
            
            chart = alt.Chart(radar_data).mark_line(point=True).encode(
                theta=alt.Theta("Angle:Q", stack=True),
                radius=alt.Radius("Score:Q", scale=alt.Scale(type='linear', zero=True)),
                color=alt.value("#4CAF50"),
                tooltip=["Trait", "Score"]
            ).project(type='radial')
            
            st.altair_chart(chart, use_container_width=True)

        elif st.session_state.page == "📱 Social Media Post":
            st.header("📱 Create Social Post")
            platform = st.selectbox("Select Platform:", ["LinkedIn", "Instagram", "Facebook", "WhatsApp", "Twitter"])
            
            if st.button("Generate Post 🚀"):
                post = generate_social_post(platform, traits)
                st.session_state.post = post
            
            if 'post' in st.session_state:
                st.markdown(f"""
                <div class="social-post">
                    <p>{st.session_state.post}</p>
                </div>
                """, unsafe_allow_html=True)
                st.button("📋 Copy to Clipboard", on_click=lambda: st.write(st.session_state.post))

        elif st.session_state.page == "💡 Success Tips":
            st.header("💡 Personality Success Tips")
            tips = [
                "🌱 Practice daily self-reflection journaling",
                "🤝 Seek diverse social interactions weekly",
                "🎯 Set SMART goals for personal development",
                "🧘♂️ Incorporate mindfulness practices daily",
                "📚 Engage in cross-disciplinary learning",
                "🗣️ Practice active listening techniques",
                "🔄 Embrace constructive feedback regularly",
                "⚖️ Maintain work-life harmony",
                "🌟 Develop emotional granularity skills",
                "🚀 Challenge comfort zones monthly"
            ]
            for tip in tips:
                st.markdown(f"<div class='tip-card'>{tip}</div>", unsafe_allow_html=True)

        elif st.session_state.page == "📥 Download Report":
            st.header("📥 Download Complete Report")
            pdf_buffer = create_pdf_report(traits, quote)
            st.download_button(
                "⬇️ Download PDF Report",
                data=pdf_buffer,
                file_name="personacraft_report.pdf",
                mime="application/pdf",
                use_container_width=True
            )