Update app.py

app.py

@@ -6,15 +6,10 @@ from langchain_core.output_parsers import StrOutputParser
 from langchain.chat_models import ChatOpenAI
 from fpdf import FPDF
 import os
-import spacy
-from collections import Counter
-
-# Load SpaCy model for NLP tasks (install 'spacy' and 'en_core_web_sm' first)
-nlp = spacy.load("en_core_web_sm")
 
 # Set up Streamlit UI
 st.title('Educational Assistant')
-st.header('Summary, Quiz Generator, Q&A, Study Plan, Vocabulary Builder & Topic Extraction')
+st.header('Summary, Quiz Generator, Q&A, and Study Plan')
 st.sidebar.title('Drop your PDF here')
 
 # Input OpenAI API key from keyboard
@@ -22,8 +17,8 @@ openai_api_key = st.sidebar.text_input("Enter your OpenAI API Key", type="passwo
 
 user_file_upload = st.sidebar.file_uploader(label='', type='pdf')
 
-# Sidebar option selection for Summary, Quiz, Q&A, Study Plan, Vocabulary Builder, or Topic Extraction
-option = st.sidebar.radio("Choose an option", ('Generate Summary', 'Generate Quiz', 'Ask a Question', 'Generate Study Plan', 'Vocabulary Builder', 'Topic Extraction'))
+# Sidebar option selection for Summary, Quiz, Q&A, or Study Plan
+option = st.sidebar.radio("Choose an option", ('Generate Summary', 'Generate Quiz', 'Ask a Question', 'Generate Study Plan'))
 
 # Input for asking questions (only visible when "Ask a Question" is selected)
 question_input = None
@@ -48,28 +43,6 @@ def generate_pdf(response, filename="response.pdf"):
     # Return the file path
     return filename
 
-def extract_key_terms(text):
-    """
-    Extract key terms (nouns) from the text using spaCy.
-    """
-    doc = nlp(text)
-    key_terms = [token.text for token in doc if token.pos_ == "NOUN" and not token.is_stop]
-    
-    # Count frequency of terms (for demonstration purposes, we show the top 10 most frequent terms)
-    term_frequency = Counter(key_terms)
-    most_common_terms = term_frequency.most_common(10)
-    
-    return most_common_terms
-
-def extract_topics(text):
-    """
-    Extract main topics (entities) using spaCy's Named Entity Recognition (NER).
-    """
-    doc = nlp(text)
-    topics = [ent.text for ent in doc.ents]  # Extract named entities (topics)
-    
-    return set(topics)  # Return unique entities as topics
-
 if openai_api_key:
     # Set OpenAI API key
     openai.api_key = openai_api_key
@@ -178,27 +151,5 @@ if openai_api_key:
         pdf_filename = generate_pdf(study_plan_response, filename="study_plan_response.pdf")
         st.download_button("Download Study Plan as PDF", data=open(pdf_filename, "rb").read(), file_name=pdf_filename, mime="application/pdf")
 
-    elif option == 'Vocabulary Builder':
-        # Extract key terms from the document
-        key_terms = extract_key_terms(' '.join([page['text'] for page in data]))
-        st.write("Key Terms and Definitions:")
-        for term, frequency in key_terms:
-            st.write(f"{term} - Frequency: {frequency}")
-        
-        # Generate PDF for the key terms and offer it as a download
-        pdf_filename = generate_pdf(str(key_terms), filename="key_terms.pdf")
-        st.download_button("Download Vocabulary Builder PDF", data=open(pdf_filename, "rb").read(), file_name=pdf_filename, mime="application/pdf")
-
-    elif option == 'Topic Extraction':
-        # Extract topics (named entities) from the document
-        topics = extract_topics(' '.join([page['text'] for page in data]))
-        st.write("Extracted Topics:")
-        for topic in topics:
-            st.write(topic)
-        
-        # Generate PDF for the topics and offer it as a download
-        pdf_filename = generate_pdf(str(topics), filename="topics.pdf")
-        st.download_button("Download Topics PDF", data=open(pdf_filename, "rb").read(), file_name=pdf_filename, mime="application/pdf")
-
 else:
     st.sidebar.warning("Please enter your OpenAI API Key to proceed.")