Update app.py

app.py

@@ -1,13 +1,35 @@
 import gradio as gr
-from transformers import pipeline
+import joblib
 
-# Load the model from Hugging Face Hub
-model_name = "yosrissa/AITAPostsTopicsClassifier"  # Replace with your Hugging Face model name
-classifier = pipeline("text-classification", model=model_name)
+# Define the custom pipeline
+class CustomSVMTextClassificationPipeline:
+    def __init__(self, model_path, vectorizer_path):
+        # Load the model and vectorizer
+        self.model = joblib.load(model_path)
+        self.vectorizer = joblib.load(vectorizer_path)
+    
+    def __call__(self, texts):
+        if isinstance(texts, str):
+            texts = [texts]  # Ensure input is a list
+
+        # Preprocess input using the vectorizer
+        preprocessed_texts = self.vectorizer.transform(texts)
+
+        # Predict using the model
+        predictions = self.model.predict(preprocessed_texts)
+
+        # Convert predictions into a readable format
+        results = [{"text": text, "predictions": list(pred)} for text, pred in zip(texts, predictions)]
+        return results
+
+# Load the model and vectorizer
+model_path = "svm_multi_output_model.pkl"  # Replace with your model file path
+vectorizer_path = "tfidf_vectorizer.pkl"  # Replace with your vectorizer file path
+classifier = CustomSVMTextClassificationPipeline(model_path, vectorizer_path)
 
 def classify_text(input_text):
     """
-    Classify the input text using the model.
+    Classify the input text using the custom pipeline.
     """
     results = classifier(input_text)
     return results