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import sys
import os
import time
import streamlit as st
import plotly.express as px
from datetime import datetime
from dotenv import load_dotenv
# Import the LLM Integration Model
from src.cag.generation_model import LLMIntegration
# Load environment variables and secrets
load_dotenv()
class CAGLLM:
def __init__(self,query,response):
self.query = query
self.response = response
def process_cag_llm(self):
# Initialize LLM Integration with API Key
llm_system = LLMIntegration()
# Cache statistics and tracking initialization
if "cache_hits" not in st.session_state:
st.session_state.cache_hits = 0
st.session_state.cache_misses = 0
st.session_state.response_times = []
st.session_state.query_timestamps = []
st.session_state.history = []
# st.set_page_config(
# page_title="CAG Chatbot",
# layout="wide",
# page_icon="🧀",
# initial_sidebar_state="expanded"
# )
# CSS for Styling Graph
st.markdown(
"""
<style>
body { font-family: 'Arial', sans-serif; }
.stTextInput, .stButton { border-radius: 8px; }
.stProgress > div > div { border-radius: 20px; }
.custom-link { color: #1f77b4; text-decoration: none; font-weight: bold; transition: color 0.3s ease-in-out; }
.custom-link:hover { color: #ff4b4b; }
.fixed-graph-container { max-height: 300px !important; overflow-y: auto; }
</style>
""",
unsafe_allow_html=True
)
# Page Title and Description
st.title("💡 Cache Augmented Generation (CAG) Chatbot")
st.write("**A chatbot with enhanced responses powered by smart caching.**")
# Layout Columns: Configurator | Chat | Statistics
col1, col2, col3 = st.columns([1.2, 2, 1.2])
# 🛠️ **Configurator Section (Left Panel)**
with col1:
st.header("⚙️ Configurator")
cache_size = st.slider("🗄️ Cache Size", min_value=50, max_value=500, value=100)
similarity_threshold = st.slider("📈 Similarity Threshold", min_value=0.5, max_value=1.0, value=0.8)
clear_cache = st.button("🧹 Clear Cache")
if clear_cache:
llm_system.cache_manager.clear_cache()
st.session_state.cache_hits = 0
st.session_state.cache_misses = 0
st.session_state.response_times = []
st.session_state.query_timestamps = []
st.session_state.history = []
st.success("✅ Cache cleared successfully!")
# 📦 **Cache Content Section**
with st.expander("📦 **View Cache Content**"):
if llm_system.cache_manager.cache:
for key, value in llm_system.cache_manager.cache.items():
st.write(f"**Query:** {key}")
st.write(f"**Response:** {value['response']}")
st.write(f"**Timestamp:** {datetime.fromtimestamp(value['timestamp']).strftime('%Y-%m-%d %H:%M:%S')}")
st.write("---")
else:
st.write("🗑️ Cache is currently empty.")
# 💬 **Chat Interaction Section (Middle Panel)**
with col2:
st.header("💬 Chat with CAG")
query = self.query
if self.query:
start_time = time.time()
# Step 1: Check Cache
st.info("⏳ Checking Cache...")
cached_response = llm_system.cache_manager.get_from_cache(llm_system.cache_manager.normalize_key(query))
if cached_response:
# Step 2: If Cache Hit, Return
st.success("✅ Cache Hit! Returning cached response.")
response = cached_response
st.session_state.cache_hits += 1
else:
# Step 3: If Cache Miss, Query LLM
st.warning("❌ Cache Miss. Fetching from LLM...")
response = llm_system.generate_response(query,self.response)
st.session_state.cache_misses += 1
# Response Time and Save Data
response_time = time.time() - start_time
st.session_state.response_times.append(response_time)
st.session_state.query_timestamps.append(datetime.now().strftime('%H:%M:%S'))
st.session_state.history.append({"query": query, "response": response, "time": response_time})
# 🎯 Chat Response
st.success(f"**🗨️ {response}**")
st.info(f"⏱️ **Response Time:** {response_time:.2f} seconds")
# 📜 **Query History Section**
with st.expander("🕰️ **Query History**"):
for entry in st.session_state.history[-10:]:
st.write(f"**Query:** {entry['query']}")
st.write(f"**Response:** {entry['response']}")
st.write(f"⏱️ **Time Taken:** {entry['time']:.2f} seconds")
st.write("---")
# 📊 **Cache Statistics Section (Right Panel)**
with col3:
st.header("📊 Cache Statistics")
# Real-Time Metrics
col1_stat, col2_stat, col3_stat = st.columns(3)
col1_stat.metric("✅ Hits", st.session_state.cache_hits)
col2_stat.metric("❌ Misses", st.session_state.cache_misses)
col3_stat.metric("📦 Cache Size", len(llm_system.cache_manager.cache))
# Cache Hit/Miss Ratio
total_queries = st.session_state.cache_hits + st.session_state.cache_misses
hit_ratio = (st.session_state.cache_hits / total_queries) * 100 if total_queries > 0 else 0
miss_ratio = (st.session_state.cache_misses / total_queries) * 100 if total_queries > 0 else 0
st.progress(hit_ratio / 100, text=f"✅ Cache Hit Ratio: {hit_ratio:.2f}%")
st.progress(miss_ratio / 100, text=f"❌ Cache Miss Ratio: {miss_ratio:.2f}%")
# 📈 **Response Time Graph**
if st.session_state.response_times:
st.markdown('<div class="fixed-graph-container">', unsafe_allow_html=True)
fig = px.line(
x=st.session_state.query_timestamps,
y=st.session_state.response_times,
title="📈 Response Time Trend",
labels={"x": "Timestamp", "y": "Response Time (s)"}
)
st.plotly_chart(fig, use_container_width=True)
st.markdown('</div>', unsafe_allow_html=True)
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