fixed :fixed the response

features/Model/English_model/feature_names.json

@@ -0,0 +1,18 @@
+[
+  "perplexity",
+  "burst_mean",
+  "burst_std",
+  "burst_max",
+  "burst_min",
+  "burst_range",
+  "num_words",
+  "num_chars",
+  "num_sentences",
+  "avg_word_len",
+  "avg_sent_len",
+  "lexical_diversity",
+  "punct_ratio",
+  "caps_ratio",
+  "flesch_reading",
+  "flesch_grade"
+]

features/Model/English_model/metadata.json

@@ -0,0 +1,13 @@
+{
+  "selected_model": "hybrid_tfidf_logistic",
+  "cv_best_f1": 0.8593569681592504,
+  "num_engineered_features": 16,
+  "num_word_tfidf_features": 86956,
+  "num_char_tfidf_features": 80000,
+  "train_samples": 15952,
+  "test_samples": 3988,
+  "train_accuracy": 0.980253259779338,
+  "train_f1": 0.980182447310475,
+  "test_accuracy": 0.8713640922768305,
+  "test_f1": 0.8707482993197279
+}

features/nepali_text_classifier/inferencer.py

@@ -81,9 +81,9 @@ def classify_text(text: str, model_names=None, top_k: int = 2):
     return {
         "label": final_label,
         "confidence": round(avg_conf * 100, 2),
-        "selected_models": selected_names,
-        "model_predictions": per_model,
-        "votes": {"AI": ai_votes, "Human": human_votes},
-        "available_models": list(models.keys()),
-        "unavailable_models": artifacts["unavailable_models"],
+        # "selected_models": selected_names,
+        # "model_predictions": per_model,
+        # "votes": {"AI": ai_votes, "Human": human_votes},
+        # "available_models": list(models.keys()),
+        # "unavailable_models": artifacts["unavailable_models"],
     }

features/text_classifier/controller.py

@@ -12,22 +12,22 @@ from .preprocess import parse_docx, parse_pdf, parse_txt
 security = HTTPBearer()
 
 
-def build_bias_summary(ai_likelihood: float) -> dict[str, object]:
-    """Convert an AI likelihood score into a human-readable bias summary."""
-    if ai_likelihood > 50:
-        overall_bias = "AI"
-        bias_statement = f"The text is biased toward AI-generated writing ({ai_likelihood}% AI likelihood)."
-    elif ai_likelihood < 50:
-        overall_bias = "Human"
-        bias_statement = f"The text is biased toward human writing ({100 - ai_likelihood}% human likelihood)."
-    else:
-        overall_bias = "Balanced"
-        bias_statement = "The text is balanced between AI and human writing."
-
-    return {
-        "overall_bias": overall_bias,
-        "bias_statement": bias_statement,
-    }
+# def build_bias_summary(ai_likelihood: float) -> dict[str, object]:
+#     """Convert an AI likelihood score into a human-readable bias summary."""
+#     if ai_likelihood > 50:
+#         overall_bias = "AI"
+#         bias_statement = f"The text is biased toward AI-generated writing ({ai_likelihood}% AI likelihood)."
+#     elif ai_likelihood < 50:
+#         overall_bias = "Human"
+#         bias_statement = f"The text is biased toward human writing ({100 - ai_likelihood}% human likelihood)."
+#     else:
+#         overall_bias = "Balanced"
+#         bias_statement = "The text is balanced between AI and human writing."
+
+#     return {
+#         "overall_bias": overall_bias,
+#         "bias_statement": bias_statement,
+#     }
 
 
 # Verify Bearer token from Authorization header
@@ -54,12 +54,11 @@ async def handle_text_analysis(text: str):
         )
 
     label, perplexity, ai_likelihood = await asyncio.to_thread(classify_text, text)
-    bias_summary = build_bias_summary(ai_likelihood)
+    # bias_summary = build_bias_summary(ai_likelihood)
     return {
         "result": label,
         "perplexity": round(perplexity, 2),
         "ai_likelihood": ai_likelihood,
-        **bias_summary,
     }