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ecb55a5 994c038 ecb55a5 994c038 599fa2e 994c038 599fa2e 994c038 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 | import streamlit as st
import requests
import uuid
# 1. PAGE CONFIGURATION
st.set_page_config(page_title="Enterprise RAG Assistant", page_icon="🤖", layout="centered")
st.title("📚 Enterprise Document Assistant")
st.markdown("Upload a PDF to the knowledge base and ask questions about it.")
# 2. SESSION STATE INITIALIZATION (The Memory Bank)
if "user_id" not in st.session_state:
st.session_state.user_id = str(uuid.uuid4())
if "thread_id" not in st.session_state:
st.session_state.thread_id = str(uuid.uuid4())
if "messages" not in st.session_state:
st.session_state.messages = []
# 3. SIDEBAR: PDF UPLOAD (The Handoff to FastAPI)
with st.sidebar:
st.header("Document Ingestion")
uploaded_file = st.file_uploader("Upload a PDF", type="pdf")
if st.button("Process Document"):
if uploaded_file:
with st.spinner("Transmitting to backend..."):
# Package the file as multipart/form-data
files = {"file": (uploaded_file.name, uploaded_file.getvalue(), "application/pdf")}
payload_data = {"user_id": st.session_state.user_id}
# Send the POST request to your local FastAPI server
try:
response = requests.post(
"https://lightrt-pdf-rag.hf.space/upload",
files=files,
data=payload_data
)
if response.status_code == 200:
st.success("File uploaded! The AI is reading it in the background.")
else:
st.error(f"Upload failed: {response.text}")
except requests.exceptions.ConnectionError:
st.error("Cannot connect to backend. Is FastAPI running?")
else:
st.warning("Please select a file first.")
# 4. CHAT HISTORY RENDERING
for msg in st.session_state.messages:
# This creates a chat bubble. role is either 'user' or 'assistant'
with st.chat_message(msg["role"]):
st.markdown(msg["content"])
# 5. CHAT INPUT & BACKEND COMMUNICATION
if prompt := st.chat_input("Ask a question about your documents..."):
# Immediately render the user's new message to the UI
st.session_state.messages.append({"role": "user", "content": prompt})
with st.chat_message("user"):
st.markdown(prompt)
# Show a loading indicator while we wait for FastAPI and LangGraph
with st.chat_message("assistant"):
message_placeholder = st.empty()
message_placeholder.markdown("*(Thinking...)*")
# Prepare the JSON payload for FastAPI
payload = {
"message": prompt,
"user_id": st.session_state.user_id,
"thread_id": st.session_state.thread_id
}
try:
# Send the question to your LangGraph backend
chat_response = requests.post("https://lightrt-pdf-rag.hf.space/chat", json=payload)
if chat_response.status_code == 200:
# Extract the answer from the JSON response
answer = chat_response.json().get("response", "No response found.")
# Update the UI placeholder with the actual answer
message_placeholder.markdown(answer)
# Save the AI's answer to the session state memory
st.session_state.messages.append({"role": "assistant", "content": answer})
else:
message_placeholder.error(f"Error: {chat_response.text}")
except requests.exceptions.ConnectionError:
message_placeholder.error("Cannot connect to backend. Is FastAPI running?") |