Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,129 @@
-import altair as alt
-import numpy as np
-import pandas as pd
+# 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
 
-"""
-# Welcome to Streamlit!
+# 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)
 
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+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)
 
-In the meantime, below is an example of what you can do with just a few lines of code:
+    # 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)
 
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+    # 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}")