Update app.py

app.py

@@ -1,32 +1,32 @@
 import streamlit as st
-import random
+from transformers import pipeline
 
 # Set up the app title and description
-st.title("Personality Predictor")
-st.write("Enter some text about yourself or someone else, and this app will predict personality traits based on the Big Five model.")
+st.title("Enhanced Personality Predictor")
+st.write("Enter some text about yourself or someone else, and this app will predict personality traits based on NLP analysis.")
 
-# Text input area
-user_input = st.text_area("Write something here:", placeholder="E.g., I love meeting new people and trying new experiences.")
+# Load the Hugging Face pre-trained model
+@st.cache_resource
+def load_model():
+    return pipeline("text-classification", model="your-huggingface-model-name")
 
-# Placeholder for trait predictions
-traits = ["Extraversion", "Agreeableness", "Conscientiousness", "Neuroticism", "Openness"]
+# Initialize the model
+classifier = load_model()
 
-# Simulated predictions (replace with model predictions later)
-def predict_personality(text):
-    # Placeholder logic: Assign random scores to traits
-    return {trait: round(random.uniform(0, 1), 2) for trait in traits}
+# Text input area
+user_input = st.text_area("Write something here:", placeholder="E.g., I enjoy social gatherings and meeting new people.")
 
-# Button to predict personality
+# Predict personality traits
 if st.button("Predict Personality"):
     if user_input.strip():
-        # Call prediction function
-        predictions = predict_personality(user_input)
-        st.subheader("Predicted Personality Traits")
-        for trait, score in predictions.items():
-            st.write(f"**{trait}:** {score}")
+        with st.spinner("Analyzing text..."):
+            results = classifier(user_input)
+            st.subheader("Predicted Personality Traits")
+            for result in results:
+                st.write(f"**{result['label']}:** {result['score']:.2f}")
     else:
         st.warning("Please enter some text to predict personality.")
 
 # Footer
 st.write("---")
-st.write("This app uses NLP to simulate personality prediction. Replace the logic with a trained model for real predictions.")
+st.write("This app uses a Hugging Face NLP model for personality prediction. Replace the placeholder model name with a real one for production use.")