update app.py

app.py

@@ -1,10 +1,11 @@
+import streamlit as st
 import nltk
-nltk.download('punkt')
 from nltk.corpus import treebank
 from nltk import PCFG, ViterbiParser
+nltk.download('punkt')
 nltk.download('all')
 # Load the treebank dataset
-nltk.download('treebank')
+# nltk.download('treebank')
 corpus = treebank.parsed_sents()
 
 # Train a PCFG parser
@@ -17,43 +18,24 @@ 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):
+def evaluate_parser(sentence):
     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
+        return parsed_tree
+    else:
+        return "Failed to parse the sentence."
 
-# 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)
+# Streamlit UI
+st.title("PCFG Parser Evaluation")
 
-# Compute accuracy
-accuracy = true_positives / (true_positives + false_positives + false_negatives)
+# Input text box for entering sentences
+sentence = st.text_input("Enter a sentence:", "this is a beautiful")
 
-print("Precision:", precision)
-print("Recall:", recall)
-print("F1 Score:", f1_score)
-print("Accuracy:", accuracy)
+# Button to trigger parsing
+if st.button("Parse"):
+    parsed_tree = evaluate_parser(sentence)
+    st.write("Parsed Tree:")
+    st.write(parsed_tree)