Delete pdf_processor.py

pdf_processor.py

@@ -1,120 +0,0 @@
-import PyPDF2
-import os
-from typing import List, Optional
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_openai import OpenAIEmbeddings
-from langchain.vectorstores import FAISS
-import pickle
-
-class PDFProcessor:
-    """Process PDF files and create searchable vector database"""
-    
-    def __init__(self, pdf_path: str = "Health Tech Hub Copenhagen.pdf"):
-        self.pdf_path = pdf_path
-        self.vector_store = None
-        self.text_chunks = []
-        
-    def extract_text_from_pdf(self) -> str:
-        """Extract text content from PDF file"""
-        if not os.path.exists(self.pdf_path):
-            raise FileNotFoundError(f"PDF file not found: {self.pdf_path}")
-        
-        text = ""
-        try:
-            with open(self.pdf_path, 'rb') as file:
-                pdf_reader = PyPDF2.PdfReader(file)
-                for page_num in range(len(pdf_reader.pages)):
-                    page = pdf_reader.pages[page_num]
-                    text += page.extract_text() + "\n"
-            
-            print(f"✅ Successfully extracted text from {self.pdf_path}")
-            return text
-            
-        except Exception as e:
-            print(f"❌ Error extracting text from PDF: {e}")
-            raise
-    
-    def split_text_into_chunks(self, text: str, chunk_size: int = 1000, chunk_overlap: int = 200) -> List[str]:
-        """Split text into smaller chunks for better processing"""
-        text_splitter = RecursiveCharacterTextSplitter(
-            chunk_size=chunk_size,
-            chunk_overlap=chunk_overlap,
-            length_function=len,
-        )
-        
-        chunks = text_splitter.split_text(text)
-        self.text_chunks = chunks
-        print(f"✅ Split text into {len(chunks)} chunks")
-        return chunks
-    
-    def create_vector_store(self, chunks: List[str]) -> FAISS:
-        """Create a vector store from text chunks"""
-        try:
-            embeddings = OpenAIEmbeddings()
-            vector_store = FAISS.from_texts(chunks, embeddings)
-            self.vector_store = vector_store
-            print("✅ Vector store created successfully")
-            return vector_store
-            
-        except Exception as e:
-            print(f"❌ Error creating vector store: {e}")
-            raise
-    
-    def search_similar_content(self, query: str, k: int = 3) -> List[str]:
-        """Search for similar content in the PDF"""
-        if not self.vector_store:
-            raise ValueError("Vector store not initialized. Call process_pdf() first.")
-        
-        try:
-            results = self.vector_store.similarity_search(query, k=k)
-            return [doc.page_content for doc in results]
-            
-        except Exception as e:
-            print(f"❌ Error searching content: {e}")
-            return []
-    
-    def process_pdf(self) -> bool:
-        """Complete PDF processing pipeline"""
-        try:
-            print(f"🔄 Processing PDF: {self.pdf_path}")
-            
-            # Extract text
-            text = self.extract_text_from_pdf()
-            
-            # Split into chunks
-            chunks = self.split_text_into_chunks(text)
-            
-            # Create vector store
-            self.create_vector_store(chunks)
-            
-            print("✅ PDF processing completed successfully")
-            return True
-            
-        except Exception as e:
-            print(f"❌ PDF processing failed: {e}")
-            return False
-    
-    def save_vector_store(self, filepath: str = "vector_store.pkl"):
-        """Save vector store to file"""
-        if self.vector_store:
-            try:
-                with open(filepath, 'wb') as f:
-                    pickle.dump(self.vector_store, f)
-                print(f"✅ Vector store saved to {filepath}")
-            except Exception as e:
-                print(f"❌ Error saving vector store: {e}")
-    
-    def load_vector_store(self, filepath: str = "vector_store.pkl") -> bool:
-        """Load vector store from file"""
-        try:
-            if os.path.exists(filepath):
-                with open(filepath, 'rb') as f:
-                    self.vector_store = pickle.load(f)
-                print(f"✅ Vector store loaded from {filepath}")
-                return True
-            else:
-                print(f"⚠️ Vector store file not found: {filepath}")
-                return False
-        except Exception as e:
-            print(f"❌ Error loading vector store: {e}")
-            return False