Update streamlit_app.py

streamlit_app.py

@@ -1,6 +1,14 @@
 import streamlit as st
+import joblib
 import torch
 from transformers import GPT2LMHeadModel, GPT2TokenizerFast
+import numpy as np
+
+# -------------------------------
+# Load Logistic Regression model
+# -------------------------------
+vectorizer = joblib.load('src/vectorizer.pkl')
+model = joblib.load('src/logistic_model.pkl')
 
 # -------------------------------
 # Load GPT-2 model and tokenizer
@@ -25,30 +33,64 @@ def calculate_perplexity(text):
         perplexity = torch.exp(loss).item()
     return perplexity
 
+# -------------------------------
+# Final prediction combining both
+# -------------------------------
+def final_score(ai_prob, perplexity):
+    # Normalize perplexity: higher => more human-like (score 0), lower => more AI-like (score 1)
+    if perplexity > 300:
+        perp_score = 0.0
+    elif perplexity < 10:
+        perp_score = 1.0
+    else:
+        perp_score = 1.0 - ((perplexity - 10) / (300 - 10))  # scale to [0,1]
+        perp_score = max(0.0, min(1.0, perp_score))
+
+    # Combine: 70% weight to perplexity, 30% to logistic regression
+    final_ai_score = (0.7 * perp_score) + (0.3 * ai_prob)
+    return final_ai_score, perp_score
+
 # -------------------------------
 # Streamlit UI
 # -------------------------------
-st.set_page_config(page_title="Text Perplexity Checker", page_icon="📊", layout="centered")
+st.set_page_config(page_title="AI Text Detector", page_icon="🤖", layout="centered")
 
 st.markdown("""
-<h2 style='text-align: center; color: #4CAF50;'>📊 Text Perplexity Checker</h2>
-<p style='text-align: center;'>Check the perplexity of a sentence to estimate its predictability.</p>
+<h2 style='text-align: center; color: #4CAF50;'>🤖 AI vs Human Text Detector</h2>
+<p style='text-align: center;'>Enter a sentence to check if it was written by a human or generated by AI.</p>
 """, unsafe_allow_html=True)
 
 user_input = st.text_area("Enter your sentence here:", height=150)
 
-if st.button("Calculate Perplexity"):
+if st.button("Check"):
     if user_input.strip() == "":
         st.warning("Please enter a sentence before submitting.")
     else:
+        # Logistic model prediction
+        transformed_input = vectorizer.transform([user_input])
+        prediction = model.predict_proba(transformed_input)
+        ai_prob = prediction[0][1]
+        human_prob = prediction[0][0]
+
+        # Perplexity calculation
         perplexity_score = calculate_perplexity(user_input)
 
-        st.subheader("📈 Perplexity Result")
+        # Combine scores
+        final_ai_score, perp_score = final_score(ai_prob, perplexity_score)
+
+        # Display Results
+        st.subheader("🔍 Result:")
+        if final_ai_score > 0.5:
+            st.error("❗ This text is likely **AI-generated**.")
+        else:
+            st.success("✅ This text is likely **Human-written**.")
+
+        st.markdown(f"**Logistic Model Confidence:** {ai_prob:.3f} AI vs {human_prob:.3f} Human")
         st.markdown(f"**Perplexity Score:** {perplexity_score:.2f}")
+        st.markdown(f"**Combined AI Score:** {final_ai_score:.3f} (Weighted)")
 
+        # Interpretation
         if perplexity_score < 30:
-            st.info("🧠 Low perplexity: The text is highly predictable (possibly AI-generated).")
+            st.info("🧠 Low perplexity suggests the text is highly predictable—possibly AI-generated.")
         elif perplexity_score > 100:
-            st.info("🧠 High perplexity: The text is more varied and complex (likely human-written).")
-        else:
-            st.info("🧠 Moderate perplexity: Could be either human or AI-generated.")
+            st.info("🧠 High perplexity suggests human-like variation or complexity.")