Update app.py

app.py

@@ -12,10 +12,10 @@ from reportlab.pdfgen import canvas
 import io
 from transformers import AutoModel, AutoTokenizer
 
-# Set page config as the very first Streamlit command
+# Set page configuration as the very first command
 st.set_page_config(page_title="🧠 Personality Prodigy", layout="wide", page_icon="🤖")
 
-# Initialize Groq client (Ensure API key is available in Streamlit secrets)
+# Initialize Groq client (ensure API key is available in Streamlit secrets)
 if "GROQ_API_KEY" in st.secrets:
     groq_client = Groq(api_key=st.secrets["GROQ_API_KEY"])
 else:
@@ -30,11 +30,11 @@ try:
 except Exception as e:
     st.error(f"Error loading emotion classifier: {e}")
 
-# Big Five Personality Model using a valid, open source model
+# Big Five Personality Model using a publicly available model on Hugging Face
 try:
     big5_model = pipeline(
         "text-classification",
-        model="pratik7/bigfive-personality",  # Updated model identifier
+        model="m3hrdadfi/bigfive-personality",  # Updated model identifier
         top_k=5
     )
 except Exception as e:
@@ -82,7 +82,7 @@ QUESTION_BANK = [
 def analyze_big5(text):
     """Analyze Big Five personality traits using the OCEAN model."""
     results = big5_model(text[:512])
-    # Assuming labels are in a format like "BIGFIVE_<TRAIT>"
+    # Process the returned labels; adjust split logic if needed based on the model's output format
     return {result['label'].split("_")[-1].lower(): result['score'] for result in results}
 
 def get_dynamic_questions(responses):
@@ -99,7 +99,7 @@ def analyze_emotions(text):
     return personality_classifier(text[:512])
 
 def generate_quote(traits):
-    """Generate personality-specific quote using Groq/Mistral."""
+    """Generate a personality-specific quote using Groq/Mistral."""
     prompt = f"""Create a motivational quote for someone with these traits:
 {traits}
 Make it inspirational, funny, and include an emoji."""
@@ -127,7 +127,7 @@ def generate_tips(traits):
     return tips[:10]
 
 def create_personality_report(responses):
-    """Generate comprehensive personality analysis."""
+    """Generate a comprehensive personality analysis."""
     text = "\n".join(responses)
     big5_results = analyze_big5(text)
     return {