Pin sklearn to 1.3.2 for model compatibility

Dockerfile

@@ -6,7 +6,7 @@ RUN apt-get update && apt-get install -y curl nginx supervisor && \
     apt-get clean && rm -rf /var/lib/apt/lists/*
 
 RUN pip install --no-cache-dir fastapi==0.111.0 uvicorn==0.30.1 pydantic==2.7.4 \
-    pydantic-settings==2.3.4 numpy==1.26.4 pandas==2.2.2 scikit-learn==1.5.1 \
+    pydantic-settings==2.3.4 numpy==1.26.4 pandas==2.2.2 scikit-learn==1.3.2 \
     scipy==1.13.1 nltk==3.8.1 tensorflow==2.16.1 keras==3.3.3 \
     torch==2.3.1 transformers==4.41.2 joblib==1.4.2
 

ml_service/app/services/model_loader.py

@@ -85,8 +85,6 @@ class ModelManager:
             if os.path.exists(vec_path) and os.path.exists(model_path):
                 self.lr_vectorizer = joblib.load(vec_path)
                 self.lr_model = joblib.load(model_path)
-                if not hasattr(self.lr_model, 'multi_class'):
-                    self.lr_model.multi_class = 'ovr'
                 self.models_loaded["logistic_regression"] = True
         except Exception as e:
             print(f"Failed to load Logistic Regression: {e}")

ml_service/requirements.txt

@@ -4,7 +4,7 @@ pydantic==2.7.4
 pydantic-settings==2.3.4
 numpy==1.26.4
 pandas==2.2.2
-scikit-learn==1.5.1
+scikit-learn==1.3.2
 scipy==1.13.1
 nltk==3.8.1
 tensorflow==2.16.1