Delete src

src/streamlit_app.py

@@ -1,129 +0,0 @@
-# app.py
-import os
-import streamlit as st
-from dotenv import load_dotenv
-from langchain.docstore.document import Document
-from langchain_community.retrievers import BM25Retriever
-from langchain.tools import Tool
-from langgraph.graph.message import add_messages
-from langgraph.graph import START, StateGraph
-from langgraph.prebuilt import ToolNode, tools_condition
-from langchain_core.messages import AnyMessage, HumanMessage
-from langchain_groq import ChatGroq
-from typing import TypedDict, Annotated
-import fitz  # PyMuPDF
-
-# Load .env vars
-load_dotenv()
-os.environ["GROQ_API_KEY"] = os.getenv("GROQ_API_KEY")
-groq_api_key = os.getenv("GROQ_API_KEY")
-
-# --- PDF uploader and parser ---
-def parse_pdfs(uploaded_files):
-    pdf_docs = []
-    for uploaded_file in uploaded_files:
-        with fitz.open(stream=uploaded_file.read(), filetype="pdf") as doc:
-            text = ""
-            for page in doc:
-                text += page.get_text()
-            pdf_docs.append(Document(page_content=text, metadata={"source": uploaded_file.name}))
-    return pdf_docs
-
-# --- Guest info retrieval ---
-def build_retriever(all_docs):
-    return BM25Retriever.from_documents(all_docs)
-
-def extract_text(query: str, retriever):
-    results = retriever.invoke(query)
-    if results:
-        return "\n\n".join([doc.page_content for doc in results[:3]])
-    else:
-        return "لم يتم العثور على معلومات مطابقة في الملفات."
-
-
-# --- Streamlit UI ---
-st.set_page_config(page_title="NINU Agent", page_icon="🏛️")
-st.title("🏛️ NINU - Guest & PDF Assistant")
-
-st.markdown("** Hint:** NINU can help summarize lectures and quiz you step-by-step in simple English.")
-
-# Initialize session state to hold conversation history
-if "conversation_history" not in st.session_state:
-    st.session_state.conversation_history = []
-
-
-
-# User input area
-query = st.text_area("📝 اكتب سؤالك أو كمل مذاكرتك هنا:")
-
-uploaded_files = st.file_uploader("📄 ارفع ملفات PDF للمحاضرات", type=["pdf"], accept_multiple_files=True)
-
-if st.button("Ask NINU") and query:
-    # 1. Parse PDF
-    user_docs = parse_pdfs(uploaded_files) if uploaded_files else []
-    bm25_retriever = build_retriever(user_docs)
-
-    # 2. Create Tool
-    NINU_tool = Tool(
-        name="NINU_Lec_retriever",
-        func=lambda q: extract_text(q, bm25_retriever),
-        description="Retrieves content from uploaded PDFs based on a query."
-    )
-
-    # 3. Create LLM with tools
-    llm = ChatGroq(model="deepseek-r1-distill-llama-70b", groq_api_key=groq_api_key)
-    tools = [NINU_tool]
-    llm_with_tools = llm.bind_tools(tools)
-
-    class AgentState(TypedDict):
-        messages: Annotated[list[AnyMessage], add_messages]
-
-    def assistant(state: AgentState):
-        return {
-            "messages": [llm_with_tools.invoke(state["messages"])]
-        }
-
-    # 4. Build Agent Graph
-    builder = StateGraph(AgentState)
-    builder.add_node("assistant", assistant)
-    builder.add_node("tools", ToolNode(tools))
-    builder.add_edge(START, "assistant")
-    builder.add_conditional_edges("assistant", tools_condition)
-    builder.add_edge("tools", "assistant")
-    NINU = builder.compile()
-
-    # 5. Prepare full conversation messages
-    if len(st.session_state.conversation_history) == 0:
-        # Add the custom prompt first
-        intro_prompt = """
-I uploaded a lecture PDF. I want you to study it with me step by step.
-
-- Summarize the lecture part by part.
-- Explain each part in very simple English like you're teaching a friend.
-- After each part, ask me 2-3 MCQ questions in English.
-- Wait for my answer before moving to the next part.
-- If I answer incorrectly, explain why.
-
-Let's begin! 💪
-"""
-        st.session_state.conversation_history.append(HumanMessage(content=intro_prompt))
-
-    # Add the new user message
-    st.session_state.conversation_history.append(HumanMessage(content=query))
-
-    # 6. Invoke agent with full conversation
-    response = NINU.invoke({"messages": st.session_state.conversation_history})
-
-    # 7. Add assistant response to history
-    assistant_reply = response["messages"][-1]
-    st.session_state.conversation_history.append(assistant_reply)
-
-    # 8. Show output
-    st.markdown("###  NINU's Response:")
-    st.write(assistant_reply.content)
-
-    # 9. Show full conversation history (optional)
-    with st.expander("🧾 Show full conversation history"):
-        for msg in st.session_state.conversation_history:
-            role = " You" if msg.type == "human" else " NINU"
-            st.markdown(f"**{role}:** {msg.content}")