Update app.py

app.py

@@ -7,68 +7,61 @@ import nltk
 import gradio as gr
 from sklearn.metrics.pairwise import cosine_similarity
 
-import os
-
-current_path = os.getcwd()
-
-print(f"The current working directory is: {current_path}")
-
-
-# class HadithClassificationApp:
-#     def __init__(self):
-#         # Download NLTK resources if needed
-#         nltk.download('punkt')
-
-#         # Load the dataset and labels
-#         self.dataset = pd.read_csv("Preprocess_LK_Hadith_dataset.csv")
-#         self.labels = self.dataset['Arabic_Grade']
-
-#         # Load the models
-#         with open("tfidf_vectorizer.pkl", "rb") as f:
-#             self.vectorizer = pickle.load(f)
-#         with open("cosine_similarity_model.pkl", "rb") as f:
-#             self.X = pickle.load(f)
-
-#     @staticmethod
-#     def remove_tashkeel(text):
-#         tashkeel_pattern = re.compile(r'[\u0617-\u061A\u064B-\u0652]')
-#         return re.sub(tashkeel_pattern, '', text)
-
-#     def preprocess_arabic_text(self, text):
-#         text = self.remove_tashkeel(text)
-#         tokens = nltk.word_tokenize(text)
-#         cleaned_tokens = [token for token in tokens if token.isalnum()]
-#         lowercase_tokens = [token.lower() for token in cleaned_tokens]
-#         return " ".join(lowercase_tokens)
-
-#     def predict_label(self, input_text, threshold=0.5):
-#         input_text = self.preprocess_arabic_text(input_text)
-#         input_vector = self.vectorizer.transform([input_text])
-#         similarities = cosine_similarity(input_vector, self.X).flatten()
-
-#         max_index = np.argmax(similarities)
-#         max_similarity = similarities[max_index]
-
-#         if max_similarity >= threshold:
-#             return self.labels.iloc[max_index]
-#         else:
-#             return "No similar text found in dataset"
-
-#     def classify_hadith(self, input_text):
-#         return self.predict_label(input_text)
-
-# if __name__ == "__main__":
-#     # Initialize the app
-#     hadith_classification_app = HadithClassificationApp()
-
-#     # Set up the Gradio interface
-#     iface = gr.Interface(
-#         fn=hadith_classification_app.classify_hadith,
-#         inputs="text",
-#         outputs="text",
-#         title="Hadith Classification App",
-#         description="Classify Hadith text based on pre-trained model."
-#     )
-
-#     # Launch the Gradio interface
-#     iface.launch()
+class HadithClassificationApp:
+    def __init__(self):
+        # Download NLTK resources if needed
+        nltk.download('punkt')
+
+        # Load the dataset and labels
+        self.dataset = pd.read_csv("/home/user/app/Preprocess_LK_Hadith_dataset.csv")
+        self.labels = self.dataset['Arabic_Grade']
+
+        # Load the models
+        with open("tfidf_vectorizer.pkl", "rb") as f:
+            self.vectorizer = pickle.load(f)
+        with open("cosine_similarity_model.pkl", "rb") as f:
+            self.X = pickle.load(f)
+
+    @staticmethod
+    def remove_tashkeel(text):
+        tashkeel_pattern = re.compile(r'[\u0617-\u061A\u064B-\u0652]')
+        return re.sub(tashkeel_pattern, '', text)
+
+    def preprocess_arabic_text(self, text):
+        text = self.remove_tashkeel(text)
+        tokens = nltk.word_tokenize(text)
+        cleaned_tokens = [token for token in tokens if token.isalnum()]
+        lowercase_tokens = [token.lower() for token in cleaned_tokens]
+        return " ".join(lowercase_tokens)
+
+    def predict_label(self, input_text, threshold=0.5):
+        input_text = self.preprocess_arabic_text(input_text)
+        input_vector = self.vectorizer.transform([input_text])
+        similarities = cosine_similarity(input_vector, self.X).flatten()
+
+        max_index = np.argmax(similarities)
+        max_similarity = similarities[max_index]
+
+        if max_similarity >= threshold:
+            return self.labels.iloc[max_index]
+        else:
+            return "No similar text found in dataset"
+
+    def classify_hadith(self, input_text):
+        return self.predict_label(input_text)
+
+if __name__ == "__main__":
+    # Initialize the app
+    hadith_classification_app = HadithClassificationApp()
+
+    # Set up the Gradio interface
+    iface = gr.Interface(
+        fn=hadith_classification_app.classify_hadith,
+        inputs="text",
+        outputs="text",
+        title="Hadith Classification App",
+        description="Classify Hadith text based on pre-trained model."
+    )
+
+    # Launch the Gradio interface
+    iface.launch()