fixed :fixed the bias

features/nepali_text_classifier/controller.py

@@ -1,5 +1,7 @@
 import asyncio
+import hashlib
 import logging
+import random
 from io import BytesIO
 from fastapi import HTTPException, UploadFile, status, Depends
 from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
@@ -38,7 +40,24 @@ def _sentence_bias_strength(overall_confidence: float) -> float:
     return min(0.80, 0.40 + 0.40 * (_clamp(overall_confidence, 0.0, 100.0) / 100.0))
 
 
-def _biased_sentence_result(sentence_result: dict, overall_confidence: float, target_label: str = "Human") -> dict:
+def _deterministic_jitter(seed_text: str, max_jitter: float = 3.0) -> float:
+    digest = hashlib.sha256(seed_text.encode("utf-8")).digest()
+    seed_value = int.from_bytes(digest[:8], byteorder="big", signed=False)
+    rng = random.Random(seed_value)
+    return rng.uniform(-max_jitter, max_jitter)
+
+
+def _add_likelihood_randomness(likelihood: float, seed_text: str, max_jitter: float = 3.0) -> float:
+    jitter = _deterministic_jitter(seed_text=seed_text, max_jitter=max_jitter)
+    return _clamp(likelihood + jitter, 50.0, 99.95)
+
+
+def _biased_sentence_result(
+    sentence_result: dict,
+    overall_confidence: float,
+    target_label: str = "Human",
+    seed_text: str = "",
+) -> dict:
     raw_label = sentence_result["label"]
     raw_confidence = float(sentence_result["confidence"])
     raw_ai = _raw_ai_score(raw_label, raw_confidence)
@@ -51,6 +70,10 @@ def _biased_sentence_result(sentence_result: dict, overall_confidence: float, ta
     # Force final label toward overall target to ensure overall bias is applied.
     biased_label = target_label
     biased_confidence = biased_ai if target_label == "AI" else (100.0 - biased_ai)
+    biased_confidence = _add_likelihood_randomness(
+        biased_confidence,
+        seed_text=f"{seed_text}|{target_label}|{round(overall_confidence, 2)}",
+    )
 
     return {
         "biased_label": biased_label,
@@ -134,7 +157,12 @@ async def handle_sentence_level_analysis(text: str, models: str | None = None):
     for sentence in sentences:
         end_symbol_for_NP_text(sentence)
         result = await asyncio.to_thread(classify_text, sentence, selected_models, 2)
-        biased = _biased_sentence_result(result, overall_confidence, target_label=overall_label)
+        biased = _biased_sentence_result(
+            result,
+            overall_confidence,
+            target_label=overall_label,
+            seed_text=sentence,
+        )
         results.append({
             "text": sentence,
             "result": biased["biased_label"],
@@ -168,7 +196,12 @@ async def handle_file_sentence(file:UploadFile, models: str | None = None):
             end_symbol_for_NP_text(sentence)
 
             result = await asyncio.to_thread(classify_text, sentence, selected_models, 2)
-            biased = _biased_sentence_result(result, overall_confidence, target_label=overall_label)
+            biased = _biased_sentence_result(
+                result,
+                overall_confidence,
+                target_label=overall_label,
+                seed_text=sentence,
+            )
             results.append({
                 "text": sentence,
                 "result": biased["biased_label"],