Update app.py

app.py

@@ -1,16 +1,5 @@
 import streamlit as st
-import spacy
 
-# Load the spaCy English model
-nlp = spacy.load("en_core_web_sm")
-
-# Function to analyze and explain a sentence
-def analyze_sentence(sentence):
-    doc = nlp(sentence)
-    explanations = []
-    for token in doc:
-        explanations.append((token.text, token.lemma_, token.pos_, spacy.explain(token.tag_)))
-    return explanations
 
 # Streamlit app
 st.title("Biology English Grammar Helper")
@@ -209,10 +198,4 @@ if tense_selected:
     st.write(f"**Use:** {tense_info['use']}")
     st.markdown("**Examples:**")
     for example, explanation in tense_info['examples']:
-        st.write(f"- **{example}**: {explanation}")
-        if st.button(f'Analyze "{example}"', key=example):  # Unique key for each button
-            with st.spinner('Analyzing...'):
-                explanations = analyze_sentence(example)
-                st.write("Analysis (Word, Base Form, Part of Speech, Explanation):")
-                for exp in explanations:
-                    st.write(exp)
+        st.write(f"- **{example}**: {explanation}")