Update app.py

app.py

@@ -6,6 +6,7 @@ from transformers import pipeline
 import numpy as np
 import re
 import random
+from datetime import datetime
 from reportlab.lib.pagesizes import letter
 from reportlab.pdfgen import canvas
 import io
@@ -13,7 +14,11 @@ import io
 # Initialize components
 groq_client = Groq(api_key=st.secrets["GROQ_API_KEY"])
 personality_classifier = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base")
-big5_model = pipeline("text-classification", model="digitalepidemiologylab/bigfive-personality-traits")
+big5_model = pipeline(
+    "text-classification", 
+    model="ElizaB/bigfive-personality-traits",
+    top_k=5  # Get all Big Five traits
+)
 
 # Configure Streamlit
 st.set_page_config(page_title="🧠 Personality Prodigy", layout="wide", page_icon="🤖")
@@ -68,8 +73,8 @@ def get_dynamic_questions(responses):
 
 def analyze_big5(text):
     """Analyze Big Five personality traits using OCEAN model"""
-    result = big5_model(text[:512])[0]
-    return {label.split("_")[1].lower(): score for label, score in zip(result['label'], result['score'])}
+    results = big5_model(text[:512])
+    return {result['label'].split("_")[1].lower(): result['score'] for result in results}
 
 def analyze_emotions(text):
     """Detect emotional tone using RoBERTa"""
@@ -91,13 +96,13 @@ def generate_quote(traits):
 def generate_tips(traits):
     """Generate evidence-based psychological tips"""
     tips = []
-    if traits['openness'] > 0.7:
+    if traits.get('openness', 0) > 0.7:
         tips.extend(["🎨 Try creative cross-training (write a poem about your work)", 
                     "🌍 Learn something new daily about different cultures"])
-    if traits['neuroticism'] > 0.6:
+    if traits.get('neuroticism', 0) > 0.6:
         tips.extend(["🧘 Practice box breathing: 4s in, 4s hold, 4s out",
                     "📝 Keep a worry journal to externalize anxieties"])
-    return tips[:10]  # Return top 10 tips
+    return tips[:10]
 
 def create_personality_report(responses):
     """Generate comprehensive personality analysis"""
@@ -109,6 +114,41 @@ def create_personality_report(responses):
         "tips": generate_tips(analyze_big5(text))
     }
 
+def create_pdf_report(report):
+    """Generate downloadable PDF report"""
+    buffer = io.BytesIO()
+    p = canvas.Canvas(buffer, pagesize=letter)
+    
+    # Header
+    p.setFont("Helvetica-Bold", 16)
+    p.drawString(100, 750, "🌟 Personality Prodigy Report 🌟")
+    p.setFont("Helvetica", 12)
+    
+    # Content
+    y_position = 700
+    content = [
+        f"Generated on: {datetime.now().strftime('%Y-%m-%d %H:%M')}",
+        "",
+        "Big Five Personality Traits:",
+        *[f"- {trait.upper()}: {score:.2f}" for trait, score in report['big5'].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 = []
@@ -181,13 +221,13 @@ else:
             st.markdown(f"- {tip}")
     
     # Downloadable Report
-    pdf_buffer = io.BytesIO()
-    p = canvas.Canvas(pdf_buffer, pagesize=letter)
-    p.drawString(100, 750, f"Personality Report - {datetime.now().strftime('%Y-%m-%d')}")
-    # ... [Add PDF content creation logic] ...
-    p.save()
-    
-    st.download_button("📥 Download Full Report", data=pdf_buffer, file_name="personality_report.pdf")
+    pdf_buffer = create_pdf_report(report)
+    st.download_button(
+        "📥 Download Full Report", 
+        data=pdf_buffer, 
+        file_name="personality_report.pdf",
+        mime="application/pdf"
+    )
 
 # Sidebar
 with st.sidebar: