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.env

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+GROQ_API_KEY="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"
+LANGFUSE_PUBLIC_KEY="pk-lf-2d11058c-faea-457d-8187-50adb4adf9df"
+LANGFUSE_PRIVATE_KEY="sk-lf-6238da13-7995-49f6-be87-7a6b0063abf3"
+HUGGINGFACE_API_TOKEN="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"
+SERPAPI_API_KEY="46b4f7315c732276e79ac17d4f5881594703fc2b8f9ae267c4763ec5c1d39d0a"
+TELEGRAM_BOT_TOKEN= "7836025598:AAHIY1azhByyvTnojr3gLC1wmL0TgA-OO7w"
+
+

Agent.py

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+# 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 langchain.utilities import SerpAPIWrapper
+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 environment variables
+load_dotenv()
+os.environ["GROQ_API_KEY"] = os.getenv("GROQ_API_KEY")
+groq_api_key = os.getenv("GROQ_API_KEY")
+serpapi_api_key = os.getenv("SERPAPI_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 & Web Assistant")
+
+st.markdown("** Hint:** NINU can help summarize lectures, answer questions from PDFs, and search the web interactively.")
+
+if "conversation_history" not in st.session_state:
+    st.session_state.conversation_history = []
+
+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:
+    # Parse PDFs if uploaded
+    user_docs = parse_pdfs(uploaded_files) if uploaded_files else []
+    bm25_retriever = build_retriever(user_docs) if user_docs else None
+
+    # Tool for PDF retrieval (if PDFs uploaded)
+    def pdf_tool_func(q):
+        if bm25_retriever:
+            return extract_text(q, bm25_retriever)
+        else:
+            return "لا توجد ملفات PDF مرفوعة للبحث."
+
+    NINU_tool = Tool(
+        name="NINU_Lec_retriever",
+        func=pdf_tool_func,
+        description="Retrieves content from uploaded PDFs based on a query."
+    )
+
+    # Tool for Web search using SerpAPI
+    serpapi = SerpAPIWrapper(serpapi_api_key=serpapi_api_key)
+    SerpAPI_tool = Tool(
+        name="WebSearch",
+        func=serpapi.run,
+        description="Searches the web for recent information."
+    )
+
+    # Combine tools
+    tools = [NINU_tool, SerpAPI_tool]
+
+    # Create LLM and bind tools
+    llm = ChatGroq(model="deepseek-r1-distill-llama-70b", groq_api_key=groq_api_key)
+    llm_with_tools = llm.bind_tools(tools)
+
+    # Define Agent state and assistant function
+    class AgentState(TypedDict):
+        messages: Annotated[list[AnyMessage], add_messages]
+
+    def assistant(state: AgentState):
+        return {
+            "messages": [llm_with_tools.invoke(state["messages"])]
+        }
+
+    # Build the StateGraph agent
+    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()
+
+    # Add intro prompt if first message
+    if len(st.session_state.conversation_history) == 0:
+        intro_prompt = """
+You are a general AI assistant with access to two tools:
+
+1. NINU_Lec_retriever: retrieves content from uploaded PDFs based on a query.
+2. WebSearch: performs web searches to answer questions about current events or general knowledge.
+
+Based on the user's query, decide whether to use NINU_Lec_retriever, WebSearch, or both.
+
+When answering, report your thoughts and finish your answer with the following template:
+FINAL ANSWER: [YOUR FINAL ANSWER].
+
+YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings.
+
+If you are asked for a number, don't use commas or units (like $, %, etc.) unless specified.
+
+If you are asked for a string, avoid articles, abbreviations, and write digits in plain text unless specified.
+"""
+        st.session_state.conversation_history.append(HumanMessage(content=intro_prompt))
+
+    # Add user query
+    st.session_state.conversation_history.append(HumanMessage(content=query))
+
+    # Invoke the agent
+    response = NINU.invoke({"messages": st.session_state.conversation_history})
+
+    # Append assistant reply to conversation history
+    assistant_reply = response["messages"][-1]
+    st.session_state.conversation_history.append(assistant_reply)
+
+    # Show assistant reply
+    st.markdown("###  NINU's Response:")
+    st.write(assistant_reply.content)
+
+    # 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}")