create app.py

app.py

@@ -0,0 +1,59 @@
+import nltk
+nltk.download('punkt')
+from nltk.corpus import treebank
+from nltk import PCFG, ViterbiParser
+nltk.download('all')
+# Load the treebank dataset
+nltk.download('treebank')
+corpus = treebank.parsed_sents()
+
+# Train a PCFG parser
+productions = []
+for tree in corpus:
+    productions += tree.productions()
+S = nltk.Nonterminal('S')
+grammar = nltk.induce_pcfg(S, productions)
+
+# Initialize the parser with the trained grammar
+parser = ViterbiParser(grammar)
+
+# Tokenize and parse a sentence
+sentences = ["this is a beautiful"]
+
+# Prepare gold standard parse trees for the sentences
+gold_standard_trees = list(treebank.parsed_sents()[:len(sentences)])
+
+# Initialize counters
+true_positives = 0
+false_positives = 0
+false_negatives = 0
+
+# Evaluate each sentence
+for sentence, gold_tree in zip(sentences, gold_standard_trees):
+    tokens = nltk.word_tokenize(sentence)
+    parsed_trees = list(parser.parse(tokens))
+    if parsed_trees:
+        # If the parser produced a parse tree, consider the first one
+        parsed_tree = parsed_trees[0]
+        # Compare each production in the parsed tree with the gold standard tree
+        for production in gold_tree.productions():
+            if production in parsed_tree.productions():
+                true_positives += 1
+            else:
+                false_negatives += 1
+        for production in parsed_tree.productions():
+            if production not in gold_tree.productions():
+                false_positives += 1
+
+# Compute precision, recall, and F1-score
+precision = true_positives / (true_positives + false_positives)
+recall = true_positives / (true_positives + false_negatives)
+f1_score = 2 * (precision * recall) / (precision + recall)
+
+# Compute accuracy
+accuracy = true_positives / (true_positives + false_positives + false_negatives)
+
+print("Precision:", precision)
+print("Recall:", recall)
+print("F1 Score:", f1_score)
+print("Accuracy:", accuracy)