Delete main.py

main.py

@@ -1,39 +0,0 @@
-import PyPDF2
-import spacy
-from collections import Counter
-import heapq
-import io
-
-# Load spaCy model
-nlp = spacy.load("./en_core_web_sm-3.7.1")
-
-def read_pdf(file_stream):
-    text = ''
-    reader = PyPDF2.PdfReader(file_stream)
-    for page in reader.pages:
-        text += page.extract_text() + ' '
-    return text.strip()
-
-def extract_key_phrases(text):
-    doc = nlp(text)
-    # Combine noun chunks and named entities as candidates for key phrases
-    key_phrases = [chunk.text for chunk in doc.noun_chunks] + [ent.text for ent in doc.ents]
-    return key_phrases
-
-def score_sentences(text, key_phrases):
-    sentence_scores = {}
-    doc = nlp(text)
-    for sent in doc.sents:
-        for phrase in key_phrases:
-            if phrase in sent.text:
-                if sent in sentence_scores:
-                    sentence_scores[sent] += 1
-                else:
-                    sentence_scores[sent] = 1
-    return sentence_scores
-
-def summarize_text(sentence_scores, num_points=5):
-    summary_sentences = heapq.nlargest(num_points, sentence_scores, key=sentence_scores.get)
-    # Format summary as bullet points
-    summary = '\n'.join([f"- {sent.text}" for sent in summary_sentences])
-    return summary