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utils/flashcards.py

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+import openai
+import os
+
+openai.api_key = os.getenv("OPENAI_API_KEY")
+
+def generate_flashcards(text, num_cards=5):
+    prompt = f"Generate {num_cards} flashcards in Q&A format from:\n{text}"
+    response = openai.ChatCompletion.create(
+        model="gpt-4",
+        messages=[{"role":"user","content":prompt}]
+    )
+    return response.choices[0].message["content"]

utils/memory_manager.py

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+from sentence_transformers import SentenceTransformer
+import chromadb
+
+# Initialize embedding model
+embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
+
+# Initialize ChromaDB
+client = chromadb.Client()
+collection = client.create_collection("memory")
+
+def save_to_memory(text, metadata=None):
+    embedding = embedding_model.encode(text).tolist()
+    collection.add(documents=[text], embeddings=[embedding], metadatas=[metadata or {}])
+
+def retrieve_memory(query, n_results=3):
+    embedding = embedding_model.encode(query).tolist()
+    results = collection.query(query_embeddings=[embedding], n_results=n_results)
+    return results
+
+def delete_memory(ids):
+    collection.delete(ids=ids)

utils/ocr.py

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+import pytesseract
+import cv2
+
+def extract_text_from_image(image_path):
+    img = cv2.imread(image_path)
+    text = pytesseract.image_to_string(img)
+    return text.strip()

utils/pdf_parser.py

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+import fitz  # PyMuPDF
+
+def extract_text_from_pdf(file_path):
+    text = ""
+    with fitz.open(file_path) as pdf:
+        for page in pdf:
+            text += page.get_text()
+    return text.strip()

utils/quiz_generator.py

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+import openai
+import os
+
+openai.api_key = os.getenv("OPENAI_API_KEY")
+
+def generate_quiz(text, num_questions=5):
+    prompt = f"Generate {num_questions} multiple choice questions from this content:\n{text}"
+    response = openai.ChatCompletion.create(
+        model="gpt-4",
+        messages=[{"role":"user","content":prompt}]
+    )
+    return response.choices[0].message["content"]