app.py

# app.py
import streamlit as st
from groq import Groq
from textblob import TextBlob
from transformers import pipeline
import re
import random
from datetime import datetime
from reportlab.lib.pagesizes import letter
from reportlab.pdfgen import canvas
import io

# Initialize components with proper error handling
try:
    groq_client = Groq(api_key=st.secrets["GROQ_API_KEY"])
except KeyError:
    st.error("GROQ_API_KEY missing in secrets! Add it in Hugging Face settings.")
    st.stop()

# Initialize psychological models using verified public models
try:
    # Personality insights using zero-shot classification
    personality_analyzer = pipeline(
        "zero-shot-classification",
        model="facebook/bart-large-mnli"
    )
    
    # Emotion detection model
    emotion_classifier = pipeline(
        "text-classification", 
        model="j-hartmann/emotion-english-distilroberta-base"
    )
except Exception as e:
    st.error(f"Model loading error: {str(e)}")
    st.stop()

# Configure Streamlit
st.set_page_config(page_title="🧠 Mind Mapper", layout="wide", page_icon="🤖")

# Custom CSS
st.markdown("""
<style>
@keyframes rainbow {
  0% { color: #ff0000; }
  20% { color: #ff8000; }
  40% { color: #ffff00; }
  60% { color: #00ff00; }
  80% { color: #0000ff; }
  100% { color: #ff00ff; }
}
.personality-title {
  animation: rainbow 3s infinite;
  font-size: 2.5em !important;
}
.progress-bar {
  height: 25px;
  border-radius: 15px;
  background: linear-gradient(90deg, #FF6B6B 0%, #4ECDC4 100%);
}
</style>
""", unsafe_allow_html=True)

# Personality traits and questions
OCEAN_TRAITS = {
    "openness": ["curious", "creative", "adventurous"],
    "conscientiousness": ["organized", "responsible", "disciplined"],
    "extraversion": ["outgoing", "energetic", "sociable"],
    "agreeableness": ["friendly", "compassionate", "cooperative"],
    "neuroticism": ["sensitive", "nervous", "emotional"]
}

QUESTION_BANK = [
    {"text": "How do you typically approach new experiences? 🌍", "trait": "openness"},
    {"text": "Describe your ideal weekend vs reality 🏝️", "trait": "conscientiousness"},
    {"text": "How do you recharge after social events? 🪫", "trait": "extraversion"},
    {"text": "Your reaction to someone disagreeing with you? 💢", "trait": "agreeableness"},
    {"text": "How do you handle unexpected changes? 🌀", "trait": "neuroticism"}
]

def analyze_psychology(text):
    """Analyze personality traits using zero-shot classification"""
    candidate_labels = list(OCEAN_TRAITS.keys())
    result = personality_analyzer(text, candidate_labels, multi_label=True)
    return {label: score for label, score in zip(result['labels'], result['scores'])}

def analyze_emotions(text):
    """Detect emotional tone"""
    return emotion_classifier(text[:512])

def generate_quote(traits):
    """Generate personality-specific quote"""
    prompt = f"""Create a motivational quote for someone with these traits:
{traits}
Make it inspirational and include 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 generate_tips(traits):
    """Generate evidence-based psychological tips"""
    tips = []
    if traits.get('openness', 0) > 0.7:
        tips.append("🎨 Try creative cross-training: Write a short story about your day")
    if traits.get('neuroticism', 0) > 0.6:
        tips.append("🧘 Practice 4-7-8 breathing: Inhale 4s, hold 7s, exhale 8s")
    return tips[:5]

def create_pdf_report(report):
    """Generate downloadable PDF report"""
    buffer = io.BytesIO()
    p = canvas.Canvas(buffer, pagesize=letter)
    
    p.setFont("Helvetica-Bold", 16)
    p.drawString(100, 750, "🌟 Psychological Profile Report 🌟")
    p.setFont("Helvetica", 12)
    
    y_position = 700
    content = [
        f"Generated on: {datetime.now().strftime('%Y-%m-%d %H:%M')}",
        "",
        "Personality Traits:",
        *[f"- {trait.upper()}: {score:.2f}" for trait, score in report['traits'].items()],
        "",
        "Emotional Analysis:",
        *[f"- {emo['label']}: {emo['score']:.2f}" for emo in report['emotions']],
        "",
        "Personalized Quote:",
        report['quote'],
        "",
        "Growth Tips:",
        *[f"{i+1}. {tip}" for i, tip in enumerate(report['tips'])]
    
    for line in content:
        p.drawString(100, y_position, line)
        y_position -= 15
    
    p.save()
    buffer.seek(0)
    return buffer

# Session state management
if 'responses' not in st.session_state:
    st.session_state.responses = []
if 'current_q' not in st.session_state:
    st.session_state.current_q = 0

# Main UI
st.title("🧠 Mind Mapper")
st.markdown("### Your Personal Psychology Assistant 🔍✨")

# Progress tracker
progress = st.session_state.current_q / len(QUESTION_BANK)
st.markdown(f"""
<div style="background: #f0f2f6; border-radius: 15px; padding: 5px;">
    <div class="progress-bar" style="width: {progress*100}%; height: 25px;"></div>
</div>
""", unsafe_allow_html=True)

# Question flow
if st.session_state.current_q < len(QUESTION_BANK):
    q = QUESTION_BANK[st.session_state.current_q]
    
    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:
    # Generate report
    combined_text = "\n".join(st.session_state.responses)
    traits = analyze_psychology(combined_text)
    emotions = analyze_emotions(combined_text)
    
    report = {
        "traits": traits,
        "emotions": emotions,
        "quote": generate_quote(traits),
        "tips": generate_tips(traits)
    }
    
    st.balloons()
    st.markdown(f"## <div class='personality-title'>🌟 Your Psychological Profile 🌟</div>", unsafe_allow_html=True)
    
    # Trait Visualization
    with st.expander("🧩 Personality Breakdown"):
        cols = st.columns(5)
        for i, (trait, score) in enumerate(report['traits'].items()):
            cols[i].metric(label=trait.upper(), value=f"{score:.0%}")
    
    # Emotional Analysis
    with st.expander("🎭 Emotional Landscape"):
        emotion = max(report['emotions'], key=lambda x: x['score'])
        st.markdown(f"**Dominant Emotion**: {emotion['label']} ({emotion['score']:.0%})")
        st.progress(emotion['score'])
    
    # Recommendations
    col1, col2 = st.columns(2)
    with col1:
        st.markdown("### 💬 Personalized Wisdom")
        st.success(f'"{report["quote"]}"')
    with col2:
        st.markdown("### 🛠️ Actionable Tips")
        for tip in report['tips']:
            st.markdown(f"- {tip}")
    
    # PDF Report
    pdf_buffer = create_pdf_report(report)
    st.download_button(
        "📥 Download Full Report", 
        data=pdf_buffer, 
        file_name="psych_profile.pdf",
        mime="application/pdf"
    )

# Sidebar
with st.sidebar:
    st.markdown("## 🌈 How It Works")
    st.markdown("""
    1. Answer 5 psychology-based questions
    2. Get instant personality analysis
    3. Receive emotional insights
    4. Download personalized report
    
    **Science Behind It:**
    - Zero-shot personality classification
    - Emotion recognition (RoBERTa)
    - Cognitive behavioral therapy principles
    """)