Upload desription.py

desription.py

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+import numpy as np
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+import pickle
+import json
+
+class WordDescriptionModel:
+    def __init__(self):
+        self.vectorizer = TfidfVectorizer()
+        self.word_descriptions = {}
+        self.word_vectors = None
+        
+    def train(self, word_desc_pairs):
+        for word, desc in word_desc_pairs:
+            self.word_descriptions[word.lower()] = desc
+            
+        descriptions = list(self.word_descriptions.values())
+        self.word_vectors = self.vectorizer.fit_transform(descriptions)
+        
+    def get_description(self, word, similarity_threshold=0.3):
+        word = word.lower()
+        
+        if word in self.word_descriptions:
+            return True, self.word_descriptions[word]
+            
+        word_vector = self.vectorizer.transform([word])
+        
+        similarities = cosine_similarity(word_vector, self.word_vectors).flatten()
+        
+        max_sim_idx = np.argmax(similarities)
+        
+        if similarities[max_sim_idx] >= similarity_threshold:
+            similar_word = list(self.word_descriptions.keys())[max_sim_idx]
+            return True, f"Similar to '{similar_word}': {self.word_descriptions[similar_word]}"
+        else:
+            return False, f"No description available for '{word}'. Please provide one for training."
+    
+    def add_new_word(self, word, description):
+        
+        word = word.lower()
+        self.word_descriptions[word] = description
+        # Retrain vectors with updated dataset
+        descriptions = list(self.word_descriptions.values())
+        self.word_vectors = self.vectorizer.fit_transform(descriptions)
+        
+    def save_model(self, filename):
+        
+        model_data = {
+            'word_descriptions': self.word_descriptions,
+            'vectorizer': self.vectorizer,
+            'word_vectors': self.word_vectors
+        }
+        with open(filename, 'wb') as f:
+            pickle.dump(model_data, f)
+            
+    def load_model(self, filename):
+       
+        try:
+            with open(filename, 'rb') as f:
+                model_data = pickle.load(f)
+                self.word_descriptions = model_data['word_descriptions']
+                self.vectorizer = model_data['vectorizer']
+                self.word_vectors = model_data['word_vectors']
+            return True
+        except FileNotFoundError:
+            return False
+
+def main():
+    model = WordDescriptionModel()
+    model_file = 'word_description_model.pkl'
+    
+    if not model.load_model(model_file):
+        print("Training new model with initial data...")
+        initial_data = [
+            ('software', 'Computer programs and associated documentation and data that provide instructions for computers to perform specific tasks.'),
+            ('hardware', 'Physical components that make up a computer system or electronic device.'),
+            ('programming', 'Process of creating sets of instructions that tell a computer how to perform tasks.'),
+            ('database', 'Organized collection of structured information or data stored electronically in a computer system.'),
+            ('algorithm', 'Step-by-step procedure or formula for solving a problem or accomplishing a task.')
+        ]
+        model.train(initial_data)
+    
+    while True:
+        print("\n=== Word Description System ===")
+        word = input("Enter a word to get its description (or 'quit' to exit): ").strip()
+        
+        if word.lower() == 'quit':
+            break
+            
+        found, description = model.get_description(word)
+        print(f"\nResult: {description}")
+        
+        if not found:
+            print("\nLet's add this word to our database!")
+            new_description = input("Please provide a description for this word: ").strip()
+            model.add_new_word(word, new_description)
+            print(f"\nThank you! '{word}' has been added to the database.")
+            
+            model.save_model(model_file)
+            print("Model has been updated and saved.")
+
+if __name__ == "__main__":
+    main()