fixing errors

app.py

@@ -4,34 +4,60 @@ import numpy as np
 
 from intent import predict_intent
 from topic_model import get_review_topic
-import download_models
 from huggingface_hub import hf_hub_download
 
 
-# download sentiment pipeline
+class TextPreprocessor:
+    """Stub class to mimic the preprocessing step used when training.
+
+    The original pipeline was pickled with this class defined in ``__main__``
+    (a notebook).  When the module is imported on HuggingFace the class lives in
+    ``app`` instead, so unpickling fails unless we make a dummy version
+    available under the old name.
+    """
+    def __init__(self):
+        pass
+
+    def fit(self, X, y=None):
+        return self
+
+    def transform(self, X):
+        return X
+
+    def fit_transform(self, X, y=None):
+        return X
+
+    def __call__(self, x):
+        return x
+
+
+# register stub in __main__ so joblib can resolve the name during unpickling
+import __main__
+__main__.TextPreprocessor = TextPreprocessor
+
+
+# download sentiment pipeline and other assets from the space repo
 pipeline_file = hf_hub_download(
     repo_id="Maarij-Aqeel/Customer_review_analyzer",
     filename="sentiment_pipeline.pkl"
 )
-vectorizer = hf_hub_download(
+vectorizer_file = hf_hub_download(
     repo_id="Maarij-Aqeel/Customer_review_analyzer",
     filename="tfidf_vectorizer.pkl"
 )
-nmf_model = hf_hub_download(
+nmf_file = hf_hub_download(
     repo_id="Maarij-Aqeel/Customer_review_analyzer",
     filename="nmf_model.pkl"
 )
 
-
-
 try:
     sentiment_pipeline = joblib.load(pipeline_file)
 except Exception as e:  # pragma: no cover - helpful when learning
     raise RuntimeError("could not load sentiment pipeline: %s" % e)
 
 try:
-    vectorizer = joblib.load(vectorizer)
-    nmf_model = joblib.load(nmf_model)
+    vectorizer = joblib.load(vectorizer_file)
+    nmf_model = joblib.load(nmf_file)
 except Exception as e:
     raise RuntimeError("could not load vectorizer/nmf model: %s" % e)