Create app.py

app.py

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+import os
+import streamlit as st
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.vectorstores import Chroma
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.chat_models import ChatOpenAI
+from langchain.chains import ConversationalRetrievalChain, ConversationChain
+from langchain.memory import ConversationBufferMemory
+from langchain.document_loaders import PyPDFLoader
+
+def create_sidebar():
+    with st.sidebar:
+        st.title("PDF Chat")
+        st.markdown("### Quick Demo of RAG")
+        
+        api_key = st.text_input("OpenAI API Key:", type="password")
+        
+        st.markdown("""
+        ### Tools Used
+        • OpenAI
+        • LangChain
+        • ChromaDB
+        
+        ### Steps
+        1. Add API key
+        2. Upload PDF
+        3. Chat!
+        """)
+        
+        return api_key
+
+def save_uploaded_file(uploaded_file, path='./uploads/'):
+    os.makedirs(path, exist_ok=True)
+    file_path = os.path.join(path, uploaded_file.name)
+    with open(file_path, "wb") as f:
+        f.write(uploaded_file.getbuffer())
+    return file_path
+
+@st.cache_data
+def load_texts_from_papers(papers):
+    all_texts = []
+    for paper in papers:
+        file_path = save_uploaded_file(paper)
+        loader = PyPDFLoader(file_path)
+        documents = loader.load()
+        text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+        texts = text_splitter.split_documents(documents)
+        all_texts.extend(texts)
+        os.remove(file_path)
+    return all_texts
+
+@st.cache_resource
+def initialize_vectorstore():
+    embedding = OpenAIEmbeddings(openai_api_key=st.session_state.api_key)
+    vectorstore = Chroma(embedding_function=embedding, persist_directory="db")
+    return vectorstore
+
+def main():
+    st.set_page_config(page_title="PDF Chat", layout="wide")
+
+    # Get API key from sidebar
+    api_key = create_sidebar()
+    
+    if api_key:
+        st.session_state.api_key = api_key
+
+    st.title("Chat with PDF")
+    papers = st.file_uploader("Upload PDFs", type=["pdf"], accept_multiple_files=True)
+
+    if "messages" not in st.session_state:
+        st.session_state.messages = []
+
+    if not api_key:
+        st.warning("Please enter your OpenAI API key")
+        return
+
+    try:
+        vectorstore = initialize_vectorstore()
+        texts = load_texts_from_papers(papers) if papers else []
+        
+        if texts:
+            vectorstore.add_documents(texts)
+            qa_chain = ConversationalRetrievalChain.from_llm(
+                ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo"),
+                vectorstore.as_retriever(),
+                memory=ConversationBufferMemory(
+                    memory_key="chat_history",
+                    return_messages=True
+                )
+            )
+        else:
+            memory = ConversationBufferMemory(memory_key="chat_history")
+            qa_chain = ConversationChain(
+                llm=ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo"),
+                memory=memory
+            )
+        
+        # Chat interface
+        for message in st.session_state.messages:
+            with st.chat_message(message["role"]):
+                st.markdown(message["content"])
+        
+        if prompt := st.chat_input("Ask about your PDFs"):
+            st.session_state.messages.append({"role": "user", "content": prompt})
+            with st.chat_message("user"):
+                st.markdown(prompt)
+            
+            with st.chat_message("assistant"):
+                try:
+                    if texts:
+                        result = qa_chain({"question": prompt})
+                        response = result["answer"]
+                    else:
+                        result = qa_chain.predict(input=prompt)
+                        response = result
+                    
+                    st.session_state.messages.append({"role": "assistant", "content": response})
+                    st.markdown(response)
+                    
+                except Exception as e:
+                    st.error(f"Error: {str(e)}")
+                
+    except Exception as e:
+        st.error(f"Error: {str(e)}")
+
+if __name__ == "__main__":
+    main()