File size: 8,779 Bytes
ce79822 f6d17ee d567d9e f6d17ee ce79822 f6d17ee ce79822 f6d17ee ce79822 f6d17ee d567d9e f6d17ee d567d9e ce79822 d567d9e f6d17ee d567d9e f6d17ee ce79822 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 | from flask import Flask, request, jsonify, render_template
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_community.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.vectorstores import FAISS
from langchain_google_genai import ChatGoogleGenerativeAI
import os
import json
app = Flask(__name__)
# Create templates directory if it doesn't exist
os.makedirs('templates', exist_ok=True)
# Create HTML template for the chatbot interface
with open('templates/index.html', 'w') as f:
f.write('''
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Cirrhosis Toolkit Chatbot</title>
<style>
body {
font-family: Arial, sans-serif;
margin: 0;
padding: 0;
display: flex;
flex-direction: column;
height: 100vh;
background-color: #f5f5f5;
}
.header {
background-color: #2c3e50;
color: white;
text-align: center;
padding: 1rem;
}
.chat-container {
flex: 1;
display: flex;
flex-direction: column;
max-width: 800px;
margin: 0 auto;
padding: 1rem;
width: 100%;
box-sizing: border-box;
}
.messages {
flex: 1;
overflow-y: auto;
padding: 1rem;
background-color: white;
border-radius: 8px;
box-shadow: 0 2px 10px rgba(0,0,0,0.1);
margin-bottom: 1rem;
}
.message {
margin-bottom: 1rem;
padding: 0.8rem;
border-radius: 8px;
max-width: 80%;
}
.user-message {
background-color: #3498db;
color: white;
align-self: flex-end;
margin-left: auto;
}
.bot-message {
background-color: #ecf0f1;
color: #333;
align-self: flex-start;
}
.input-area {
display: flex;
gap: 0.5rem;
}
#user-input {
flex: 1;
padding: 0.8rem;
border: 1px solid #ddd;
border-radius: 4px;
font-size: 1rem;
}
button {
padding: 0.8rem 1.5rem;
background-color: #2c3e50;
color: white;
border: none;
border-radius: 4px;
cursor: pointer;
font-size: 1rem;
}
button:hover {
background-color: #1a252f;
}
.loading {
display: none;
text-align: center;
margin: 1rem 0;
}
.loading-dots {
display: inline-block;
}
.loading-dots::after {
content: '.';
animation: dots 1.5s steps(5, end) infinite;
}
@keyframes dots {
0%, 20% { content: '.'; }
40% { content: '..'; }
60% { content: '...'; }
80%, 100% { content: ''; }
}
</style>
</head>
<body>
<div class="header">
<h1>Cirrhosis Toolkit Assistant</h1>
</div>
<div class="chat-container">
<div class="messages" id="chat-messages">
<div class="message bot-message">
Hello! I'm your Cirrhosis Toolkit assistant. Ask me any questions about cirrhosis management and treatment.
</div>
</div>
<div class="loading" id="loading">
Thinking<span class="loading-dots"></span>
</div>
<div class="input-area">
<input type="text" id="user-input" placeholder="Ask a question..." autocomplete="off">
<button id="send-btn">Send</button>
</div>
</div>
<script>
const chatMessages = document.getElementById('chat-messages');
const userInput = document.getElementById('user-input');
const sendBtn = document.getElementById('send-btn');
const loadingIndicator = document.getElementById('loading');
// Function to add a message to the chat
function addMessage(message, isUser = false) {
const messageDiv = document.createElement('div');
messageDiv.className = `message ${isUser ? 'user-message' : 'bot-message'}`;
messageDiv.textContent = message;
chatMessages.appendChild(messageDiv);
chatMessages.scrollTop = chatMessages.scrollHeight;
}
// Function to send user query to backend
async function sendQuery(query) {
loadingIndicator.style.display = 'block';
try {
const response = await fetch('/query', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
},
body: JSON.stringify({ query }),
});
const data = await response.json();
if (response.ok) {
addMessage(data.response);
} else {
addMessage(`Error: ${data.error || 'Something went wrong'}`);
}
} catch (error) {
addMessage(`Error: ${error.message}`);
} finally {
loadingIndicator.style.display = 'none';
}
}
// Event listener for send button
sendBtn.addEventListener('click', () => {
const query = userInput.value.trim();
if (query) {
addMessage(query, true);
userInput.value = '';
sendQuery(query);
}
});
// Event listener for Enter key
userInput.addEventListener('keypress', (e) => {
if (e.key === 'Enter') {
const query = userInput.value.trim();
if (query) {
addMessage(query, true);
userInput.value = '';
sendQuery(query);
}
}
});
// Focus input on page load
userInput.focus();
</script>
</body>
</html>
''')
# Set your Google API key
os.environ["GOOGLE_API_KEY"] = "AIzaSyCOsco3wW-yHA074FTp-Mbz8NgUptGUY_8" # Replace with your actual API key
# Use a lightweight embedding model to reduce memory usage
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
# Google Gemini LLM setup
llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash-lite", temperature=0.7)
# FAISS index path (stored on disk)
FAISS_INDEX_PATH = "faiss_index"
# PDF path
PDF_PATH = "CirrhosisToolkit.pdf"
def process_pdf(pdf_path):
"""Processes the PDF and stores FAISS index on disk to save memory."""
loader = PyPDFLoader(pdf_path)
documents = loader.lazy_load() # Lazy load to avoid high memory usage
# Reduce chunk size to save memory
text_splitter = RecursiveCharacterTextSplitter(chunk_size=150, chunk_overlap=30)
texts = text_splitter.split_documents(documents)
# Store FAISS on disk to avoid keeping everything in RAM
vectordb = FAISS.from_documents(texts, embeddings)
vectordb.save_local(FAISS_INDEX_PATH)
# Load FAISS index if available, else process the PDF
if os.path.exists(FAISS_INDEX_PATH):
vectordb = FAISS.load_local(FAISS_INDEX_PATH, embeddings, allow_dangerous_deserialization=True)
else:
process_pdf(PDF_PATH)
vectordb = FAISS.load_local(FAISS_INDEX_PATH, embeddings, allow_dangerous_deserialization=True)
@app.route('/')
def index():
"""Renders the chatbot frontend."""
return render_template('index.html')
@app.route('/query', methods=['POST'])
def query_pdf():
"""Handles user queries and retrieves relevant document context."""
if vectordb is None:
return jsonify({'error': 'No PDF processed yet'}), 400
data = request.get_json()
query_text = data.get("query", "")
if not query_text:
return jsonify({'error': 'No query provided'}), 400
# Perform similarity search with reduced results (k=2) to save memory
results = vectordb.similarity_search(query_text, k=2)
context = "\n".join([res.page_content for res in results])
# Generate response with Gemini
prompt = f"Using the following document context, answer the query concisely.\n\nContext:\n{context}\n\nQuery: {query_text}"
gemini_response = llm.invoke(prompt)
return jsonify({'response': gemini_response.content})
if __name__ == '__main__':
app.run(debug=True) |