upload code

app.py

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+import io
+import streamlit as st
+from PyPDF2 import PdfReader
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.chains.question_answering import load_qa_chain
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_community.vectorstores import FAISS
+from langchain_community.llms import HuggingFacePipeline
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+
+
+# Global variables
+knowledge_base = None
+qa_chain = None
+
+# PDF 파일 로드 및 텍스트 추출
+def load_pdf(pdf_file):
+    pdf_reader = PdfReader(pdf_file)
+    text = "".join(page.extract_text() for page in pdf_reader.pages)
+    return text
+
+# 텍스트를 청크로 분할
+def split_text(text):
+    text_splitter = CharacterTextSplitter(
+        separator="\n", 
+        chunk_size=1000, 
+        chunk_overlap=200, 
+        length_function=len
+    )
+    return text_splitter.split_text(text)
+
+# FAISS 벡터 저장소 생성
+def create_knowledge_base(chunks):
+    model_name = "sentence-transformers/all-mpnet-base-v2"  # 임베딩 모델을 명시
+    embeddings = HuggingFaceEmbeddings(model_name=model_name)
+    return FAISS.from_texts(chunks, embeddings)
+
+# Hugging Face 모델 로드
+def load_model():
+    model_name = "halyn/gemma2-2b-it-finetuned-paperqa"  # 텍스트 생성 모델 사용
+    tokenizer = AutoTokenizer.from_pretrained(model_name, clean_up_tokenization_spaces=False)
+    model = AutoModelForCausalLM.from_pretrained(model_name)
+    return pipeline("text-generation", model=model, tokenizer=tokenizer, max_new_tokens=150, temperature=0.1)
+
+# QA 체인 설정
+def setup_qa_chain():
+    global qa_chain
+    try:
+        pipe = load_model()  
+    except Exception as e:
+        print(f"Error loading model: {e}")
+        return
+    llm = HuggingFacePipeline(pipeline=pipe)
+    qa_chain = load_qa_chain(llm, chain_type="stuff")
+    
+# 메인 페이지 UI
+def main_page():
+    st.title("Welcome to GemmaPaperQA")
+    st.subheader("Upload Your Paper")
+
+    paper = st.file_uploader("Upload Here!", type="pdf", label_visibility="hidden")
+    if paper:
+        st.write(f"Upload complete! File name: {paper.name}")
+        # 파일 크기 확인
+        file_size = paper.size  # 파일 크기를 파일 포인터 이동 없이 확인
+        if file_size > 10 * 1024 * 1024:  # 10MB 제한
+            st.error("File is too large! Please upload a file smaller than 10MB.")
+            return
+
+        # 중간 확인 절차 - PDF 내용 미리보기
+        with st.spinner('Processing PDF...'):
+            try:
+                paper.seek(0)  # 파일 읽기 포인터를 처음으로 되돌림
+                contents = paper.read()
+                pdf_file = io.BytesIO(contents)
+                text = load_pdf(pdf_file)
+
+                # 텍스트가 추출되지 않을 경우 에러 처리
+                if len(text.strip()) == 0:
+                    st.error("The PDF appears to have no extractable text. Please check the file and try again.")
+                    return
+
+                st.text_area("Preview of extracted text", text[:1000], height=200)
+                st.write(f"Total characters extracted: {len(text)}")
+                global knowledge_base
+                if st.button("Proceed with this file"):
+                    chunks = split_text(text)
+                    knowledge_base = create_knowledge_base(chunks)
+                    
+                    if knowledge_base is None:
+                        st.error("Failed to create knowledge base.")
+                        return
+                    
+                    setup_qa_chain()
+
+                    st.session_state.paper_name = paper.name[:-4]
+                    st.session_state.page = "chat"
+                    st.success("PDF successfully processed! You can now ask questions.")
+
+            except Exception as e:
+                st.error(f"Failed to process the PDF: {str(e)}")
+
+
+# 채팅 페이지 UI
+def chat_page():
+    st.title(f"Ask anything about {st.session_state.paper_name}")
+
+    if "messages" not in st.session_state:
+        st.session_state.messages = []
+
+    for message in st.session_state.messages:
+        with st.chat_message(message["role"]):
+            st.markdown(message["content"])
+
+    if prompt := st.chat_input("Chat here!"):
+        st.session_state.messages.append({"role": "user", "content": prompt})
+
+        with st.chat_message("user"):
+            st.markdown(prompt)
+
+        response = get_response_from_model(prompt)
+
+        with st.chat_message("assistant"):
+            st.markdown(response)
+
+        st.session_state.messages.append({"role": "assistant", "content": response})
+
+    if st.button("Go back to main page"):
+        st.session_state.page = "main"
+
+# 모델 응답 처리
+def get_response_from_model(prompt):
+    try:
+        global knowledge_base, qa_chain
+        if not knowledge_base:
+            return "No PDF has been uploaded yet."
+        if not qa_chain:
+            return "QA chain is not initialized."
+
+        docs = knowledge_base.similarity_search(prompt)
+        response = qa_chain.run(input_documents=docs, question=prompt)
+
+        if "Helpful Answer:" in response:
+            response = response.split("Helpful Answer:")[1].strip()
+
+        return response
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+# 페이지 설정
+if "page" not in st.session_state:
+    st.session_state.page = "main"
+
+if "paper_name" not in st.session_state:
+    st.session_state.paper_name = ""
+
+# 페이지 렌더링
+if st.session_state.page == "main":
+    main_page()
+elif st.session_state.page == "chat":
+    chat_page()

requirements.txt

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+streamlit
+PyPDF2
+langchain-huggingface
+langchain==0.3.1
+langchain-community==0.3.1
+langchain-core==0.3.8
+langchain-text-splitters==0.3.0
+transformers==4.45.1
+torch==2.4.1
+faiss-cpu==1.8.0.post1
+requests==2.32.3
+huggingface-hub==0.25.1
+sentence-transformers==3.1.1
+peft==0.2.0