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be454f3 | 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 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 | """Streamlit UI for Agentic RAG Chatbot"""
import streamlit as st
import asyncio
import uuid
from typing import Dict, Any, List
from agents.base_agent import BaseAgent
from core.mcp import MCPMessage, MessageType, message_bus
class UIAgent(BaseAgent):
"""UI Agent for handling Streamlit interface"""
def __init__(self):
super().__init__("UI")
self.responses = {}
self.errors = {}
async def handle_message(self, message: MCPMessage) -> None:
"""Handle incoming messages from other agents"""
trace_id = message.trace_id
if message.type == MessageType.FINAL_RESPONSE:
self.responses[trace_id] = message.payload
elif message.type == MessageType.ERROR:
self.errors[trace_id] = message.payload
async def upload_document(self, file_name: str, file_data: bytes) -> str:
"""Upload document to ingestion agent"""
trace_id = str(uuid.uuid4())
await self.send_message(
receiver="IngestionAgent",
msg_type=MessageType.DOC_UPLOADED,
payload={
"file_name": file_name,
"file_data": file_data
},
trace_id=trace_id
)
return trace_id
async def ask_question(self, query: str) -> str:
"""Send user query to LLM response agent"""
trace_id = str(uuid.uuid4())
await self.send_message(
receiver="LLMResponseAgent",
msg_type=MessageType.USER_QUERY,
payload={
"query": query
},
trace_id=trace_id
)
return trace_id
def get_response(self, trace_id: str) -> Dict[str, Any]:
"""Get response for a trace ID"""
return self.responses.get(trace_id)
def get_error(self, trace_id: str) -> Dict[str, Any]:
"""Get error for a trace ID"""
return self.errors.get(trace_id)
class StreamlitApp:
"""Main Streamlit application"""
def __init__(self):
self.ui_agent = UIAgent()
self.init_session_state()
def init_session_state(self):
"""Initialize Streamlit session state"""
if 'messages' not in st.session_state:
st.session_state.messages = []
if 'uploaded_files' not in st.session_state:
st.session_state.uploaded_files = []
if 'agents_initialized' not in st.session_state:
st.session_state.agents_initialized = False
def initialize_agents(self):
"""Initialize all agents"""
if not st.session_state.agents_initialized:
from agents.ingestion_agent import IngestionAgent
from agents.retrieval_agent import RetrievalAgent
from agents.llm_response_agent import LLMResponseAgent
# Create agents
st.session_state.ingestion_agent = IngestionAgent()
st.session_state.retrieval_agent = RetrievalAgent()
st.session_state.llm_response_agent = LLMResponseAgent()
st.session_state.agents_initialized = True
def render_sidebar(self):
"""Render sidebar with file upload and settings"""
st.sidebar.title("📁 Document Upload")
uploaded_files = st.sidebar.file_uploader(
"Choose files",
type=['pdf', 'pptx', 'csv', 'docx', 'txt', 'md'],
accept_multiple_files=True
)
if uploaded_files:
for uploaded_file in uploaded_files:
if uploaded_file.name not in [f['name'] for f in st.session_state.uploaded_files]:
# Process file upload
file_data = uploaded_file.read()
# Upload to ingestion agent
trace_id = asyncio.run(self.ui_agent.upload_document(
uploaded_file.name, file_data
))
# Add to session state
st.session_state.uploaded_files.append({
'name': uploaded_file.name,
'size': len(file_data),
'trace_id': trace_id
})
st.sidebar.success(f"✅ {uploaded_file.name} uploaded")
# Display uploaded files
if st.session_state.uploaded_files:
st.sidebar.subheader("📄 Uploaded Files")
for file_info in st.session_state.uploaded_files:
st.sidebar.text(f"• {file_info['name']}")
# Settings
st.sidebar.subheader("⚙️ Settings")
if st.sidebar.button("Clear All Documents"):
st.session_state.uploaded_files = []
st.session_state.retrieval_agent.clear_store()
st.sidebar.success("Documents cleared")
def render_chat_interface(self):
"""Render main chat interface"""
st.title("🤖 Agentic RAG Chatbot")
st.markdown("Upload documents and ask questions about their content!")
# Display chat messages
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
# Display sources if available
if message.get("sources"):
with st.expander("📚 Sources"):
for source in message["sources"]:
source_text = f"**{source['document']}**"
if 'page' in source:
source_text += f" (Page {source['page']})"
elif 'slide' in source:
source_text += f" (Slide {source['slide']})"
elif 'row' in source:
source_text += f" (Row {source['row']})"
elif 'paragraph' in source:
source_text += f" (Paragraph {source['paragraph']})"
st.markdown(source_text)
# Chat input
if prompt := st.chat_input("Ask a question about your documents..."):
# Add user message
st.session_state.messages.append({"role": "user", "content": prompt})
# Display user message
with st.chat_message("user"):
st.markdown(prompt)
# Process question
with st.chat_message("assistant"):
with st.spinner("Thinking..."):
trace_id = asyncio.run(self.ui_agent.ask_question(prompt))
# Wait for response
response = None
error = None
max_attempts = 50
attempts = 0
while attempts < max_attempts:
response = self.ui_agent.get_response(trace_id)
error = self.ui_agent.get_error(trace_id)
if response or error:
break
asyncio.run(asyncio.sleep(0.1))
attempts += 1
if error:
error_msg = f"❌ Error: {error.get('error', 'Unknown error')}"
st.error(error_msg)
st.session_state.messages.append({
"role": "assistant",
"content": error_msg
})
elif response:
answer = response.get('answer', 'No answer generated')
sources = response.get('source_info', [])
st.markdown(answer)
# Display sources
if sources:
with st.expander("📚 Sources"):
for source in sources:
source_text = f"**{source['document']}**"
if 'page' in source:
source_text += f" (Page {source['page']})"
elif 'slide' in source:
source_text += f" (Slide {source['slide']})"
elif 'row' in source:
source_text += f" (Row {source['row']})"
elif 'paragraph' in source:
source_text += f" (Paragraph {source['paragraph']})"
st.markdown(source_text)
st.session_state.messages.append({
"role": "assistant",
"content": answer,
"sources": sources
})
else:
timeout_msg = "⏰ Request timed out. Please try again."
st.error(timeout_msg)
st.session_state.messages.append({
"role": "assistant",
"content": timeout_msg
})
def run(self):
"""Run the Streamlit application"""
st.set_page_config(
page_title="Agentic RAG Chatbot",
page_icon="🤖",
layout="wide"
)
# CSS injection for black background and green text
st.markdown(
'''
<style>
html, body, [class*="st-"], [class*="css-"] {
background-color: #000000;
color: #00FF00; /* Retain green for general text */
}
h1 { /* Target for Streamlit titles */
color: red;
font-weight: bold; /* Make it thick */
}
</style>
''',
unsafe_allow_html=True
)
self.initialize_agents()
self.render_sidebar()
self.render_chat_interface()
# Create and run the app
if __name__ == "__main__":
app = StreamlitApp()
app.run() |