Update app.py

app.py

@@ -1,12 +1,8 @@
-import os
-import json
-import ast
 import streamlit as st
 import torch
 import torch.nn.functional as F
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import re
-import math
 import logging
 import pandas as pd
 
@@ -21,42 +17,6 @@ st.logo(
     link="https://dejan.ai/",
 )
 
-# --- Load heuristic weights from environment secrets, with JSON→Python fallback ---
-@st.cache_resource
-def load_heuristic_weights():
-    def _load(env_key):
-        raw = os.environ[env_key]
-        try:
-            return json.loads(raw)
-        except json.JSONDecodeError:
-            return ast.literal_eval(raw)
-    ai = _load("AI_WEIGHTS_JSON")
-    og = _load("OG_WEIGHTS_JSON")
-    return ai, og
-
-AI_WEIGHTS, OG_WEIGHTS = load_heuristic_weights()
-SIGMOID_K = 0.5
-
-def tokenize(text):
-    return re.findall(r'\b[a-z]{2,}\b', text.lower())
-
-def classify_text_likelihood(text: str) -> float:
-    tokens = tokenize(text)
-    if not tokens:
-        return 0.5
-    ai_score = og_score = matched = 0
-    for t in tokens:
-        aw = AI_WEIGHTS.get(t, 0)
-        ow = OG_WEIGHTS.get(t, 0)
-        if aw or ow:
-            matched += 1
-            ai_score += aw
-            og_score += ow
-    if matched == 0:
-        return 0.5
-    net = ai_score - og_score
-    return 1 / (1 + math.exp(-SIGMOID_K * net))
-
 # --- Logging & Streamlit setup ---
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
@@ -158,11 +118,8 @@ if st.button("Classify", type="primary"):
             st.markdown("### 🔍 Highlighted Text")
             st.markdown(" ".join(highlighted_sentences), unsafe_allow_html=True)
 
+            # Overall score (just model avg)
             avg = torch.mean(probs, dim=0)
             model_ai = avg[0].item()
-            heuristic_ai = classify_text_likelihood(text)
-            combined = min(model_ai + heuristic_ai, 1.0)
 
-            st.subheader(f"⚖️ AI Likelihood: {combined*100:.1f}%")
-            st.write(f"🤖 Model: {model_ai*100:.1f}%")
-            st.write(f"🛠️ Heuristic: {heuristic_ai*100:.1f}%")
+            st.subheader(f"⚖️ AI Likelihood: {model_ai*100:.1f}%")