Upload streamlit.py

streamlit.py

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+import os
+import FreshStart_deploy.streamlit as st
+from langchain_google_genai import GoogleGenerativeAIEmbeddings, ChatGoogleGenerativeAI
+from langchain_chroma import Chroma
+from langchain.chains import create_history_aware_retriever, create_retrieval_chain
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain_core.messages import AIMessage, HumanMessage, BaseMessage
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langgraph.checkpoint.memory import MemorySaver
+from langgraph.graph import START, StateGraph
+from typing import Sequence
+from typing_extensions import Annotated, TypedDict
+from langchain_community.document_loaders import PyPDFLoader
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+
+from dotenv import load_dotenv
+
+# Configure API key
+load_dotenv()
+api_key = os.getenv("GOOGLE_API_KEY")
+os.environ["GOOGLE_API_KEY"] = api_key
+
+# Initialize the Google Generative AI model
+gemini_embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
+model = ChatGoogleGenerativeAI(model="gemini-1.0-pro", convert_system_message_to_human=True)
+
+# Load the document
+document_loader = PyPDFLoader("/Users/maryam/Documents/UWF/our/chatbot/22_studenthandbook-22-23_f2.pdf")
+doc = document_loader.load()
+
+# Split documents
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+splits = text_splitter.split_documents(doc)
+
+# Create a vector store and retriever
+vectorstore = Chroma.from_documents(documents=splits, embedding=gemini_embeddings)
+retriever = vectorstore.as_retriever()
+
+# Set up prompts
+contextualize_q_system_prompt = (
+    "Given a chat history and the latest user question "
+    "which might reference context in the chat history, "
+    "formulate a standalone question which can be understood "
+    "without the chat history."
+)
+contextualize_q_prompt = ChatPromptTemplate.from_messages(
+    [
+        ("system", contextualize_q_system_prompt),
+        MessagesPlaceholder("chat_history"),
+        ("human", "{input}"),
+    ]
+)
+history_aware_retriever = create_history_aware_retriever(model, retriever, contextualize_q_prompt)
+
+# Create the question-answer chain
+system_prompt = (
+    "You are an assistant for question-answering tasks. "
+    "Use the following pieces of retrieved context to answer "
+    "the question. If you don't know the answer, say that you "
+    "don't know."
+    "\n\n"
+    "{context}"
+)
+qa_prompt = ChatPromptTemplate.from_messages(
+    [
+        ("system", system_prompt),
+        MessagesPlaceholder("chat_history"),
+        ("human", "{input}"),
+    ]
+)
+question_answer_chain = create_stuff_documents_chain(model, qa_prompt)
+rag_chain = create_retrieval_chain(history_aware_retriever, question_answer_chain)
+
+# State management with LangGraph
+class State(TypedDict):
+    input: str
+    chat_history: Annotated[Sequence[BaseMessage], "add_messages"]
+    context: str
+    answer: str
+
+def call_model(state: State):
+    response = rag_chain.invoke(state)
+    return {
+        "chat_history": [
+            HumanMessage(state["input"]),
+            AIMessage(response["answer"]),
+        ],
+        "context": response["context"],
+        "answer": response["answer"],
+    }
+
+workflow = StateGraph(state_schema=State)
+workflow.add_edge(START, "model")
+workflow.add_node("model", call_model)
+memory = MemorySaver()
+app = workflow.compile(checkpointer=memory)
+
+# Streamlit User Interface
+st.title("Custom Question-Answering Chatbot")
+st.write("Ask questions based on the loaded document.")
+
+# Maintain chat history using Streamlit session state
+if "chat_history" not in st.session_state:
+    st.session_state.chat_history = []
+
+# User input section
+user_input = st.text_input("Enter your question here:")
+
+# Submit button
+if st.button("Submit"):
+    if user_input:
+        # Prepare state and invoke the model
+        state = {"input": user_input, "chat_history": st.session_state.chat_history, "context": "", "answer": ""}
+        config = {"configurable": {"thread_id": "246"}}
+        
+        result = app.invoke(state, config=config)
+        
+        # Display response and update chat history
+        st.session_state.chat_history.append(HumanMessage(user_input))
+        st.session_state.chat_history.append(AIMessage(result["answer"]))
+        
+        st.write("Chatbot:", result["answer"])
+    else:
+        st.write("Please enter a question.")
+