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import gradio as gr | |
import os | |
from dotenv import load_dotenv | |
from llama_index.core import StorageContext, load_index_from_storage, VectorStoreIndex, SimpleDirectoryReader, ChatPromptTemplate, Settings | |
from llama_index.llms.huggingface import HuggingFaceInferenceAPI | |
from llama_index.embeddings.huggingface import HuggingFaceEmbedding | |
from simple_salesforce import Salesforce, SalesforceLogin | |
import random | |
import datetime | |
import uuid | |
import json | |
# Load environment variables | |
load_dotenv() | |
# Configure the Llama index settings | |
Settings.llm = HuggingFaceInferenceAPI( | |
model_name="meta-llama/Meta-Llama-3-8B-Instruct", | |
tokenizer_name="meta-llama/Meta-Llama-3-8B-Instruct", | |
context_window=3000, | |
token=os.getenv("HF_TOKEN"), | |
max_new_tokens=512, | |
generate_kwargs={"temperature": 0.1}, | |
) | |
Settings.embed_model = HuggingFaceEmbedding( | |
model_name="BAAI/bge-small-en-v1.5" | |
) | |
# Define the directory for persistent storage and data | |
PERSIST_DIR = "db" | |
PDF_DIRECTORY = 'data' | |
# Ensure directories exist | |
os.makedirs(PDF_DIRECTORY, exist_ok=True) | |
os.makedirs(PERSIST_DIR, exist_ok=True) | |
# Variable to store current chat conversation | |
current_chat_history = [] | |
kkk = random.choice(['Clara', 'Lily']) | |
def data_ingestion_from_directory(): | |
# Use SimpleDirectoryReader on the directory containing the PDF files | |
documents = SimpleDirectoryReader(PDF_DIRECTORY).load_data() | |
storage_context = StorageContext.from_defaults() | |
index = VectorStoreIndex.from_documents(documents) | |
index.storage_context.persist(persist_dir=PERSIST_DIR) | |
def handle_query(query): | |
chat_text_qa_msgs = [ | |
( | |
"user", | |
""" | |
You are the Lily Redfernstech chatbot. Your goal is to provide accurate, professional, and helpful answers to user queries based on the company's data. Always ensure your responses are clear and concise. give response within 10-15 words only | |
{context_str} | |
Question: | |
{query_str} | |
""" | |
) | |
] | |
text_qa_template = ChatPromptTemplate.from_messages(chat_text_qa_msgs) | |
# Load index from storage | |
storage_context = StorageContext.from_defaults(persist_dir=PERSIST_DIR) | |
index = load_index_from_storage(storage_context) | |
# Use chat history to enhance response | |
context_str = "" | |
for past_query, response in reversed(current_chat_history): | |
if past_query.strip(): | |
context_str += f"User asked: '{past_query}'\nBot answered: '{response}'\n" | |
query_engine = index.as_query_engine(text_qa_template=text_qa_template, context_str=context_str) | |
answer = query_engine.query(query) | |
if hasattr(answer, 'response'): | |
response = answer.response | |
elif isinstance(answer, dict) and 'response' in answer: | |
response = answer['response'] | |
else: | |
response = "Sorry, I couldn't find an answer." | |
# Update current chat history | |
current_chat_history.append((query, response)) | |
return response | |
def save_chat_history(history): | |
# Save the chat history to a local file or Firebase | |
session_id = str(uuid.uuid4()) | |
chat_history_path = f"chat_history_{session_id}.json" | |
with open(chat_history_path, 'w') as f: | |
json.dump(history, f) | |
print(f"Chat history saved as {chat_history_path}") | |
# Save to Salesforce | |
save_to_salesforce(current_chat_history) | |
def save_to_salesforce(history): | |
username =os.getenv("username") | |
password =os.getenv("password") | |
security_token =os.getenv("security_token") | |
domain = 'test' | |
session_id, sf_instance = SalesforceLogin(username=username, password=password, security_token=security_token, domain=domain) | |
sf = Salesforce(instance=sf_instance, session_id=session_id) | |
for past_query, response in history: | |
data = { | |
'Name': 'Chat with user', | |
'Bot_Message__c': response, | |
'User_Message__c': past_query, | |
'Date__c': str(datetime.datetime.now().date()) | |
} | |
sf.Chat_History__c.create(data) | |
# Define the function to handle predictions | |
def predict(message, history): | |
logo_html = ''' | |
<div class="circle-logo"> | |
<img src="https://rb.gy/8r06eg" alt="FernAi"> | |
</div> | |
''' | |
response = handle_query(message) | |
response_with_logo = f'<div class="response-with-logo">{logo_html}<div class="response-text">{response}</div></div>' | |
# Save the updated history | |
save_chat_history(current_chat_history) | |
return response_with_logo | |
# Define your Gradio chat interface function | |
def chat_interface(message, history): | |
try: | |
# Process the user message and generate a response | |
response = handle_query(message) | |
# Update the history and save it | |
save_chat_history(current_chat_history) | |
# Return the bot response | |
return response | |
except Exception as e: | |
return str(e) | |
# Custom CSS for styling | |
css = ''' | |
.circle-logo { | |
display: inline-block; | |
width: 40px; | |
height: 40px; | |
border-radius: 50%; | |
overflow: hidden; | |
margin-right: 10px; | |
vertical-align: middle; | |
} | |
.circle-logo img { | |
width: 100%; | |
height: 100%; | |
object-fit: cover; | |
} | |
.response-with-logo { | |
display: flex; | |
align-items: center; | |
margin-bottom: 10px; | |
} | |
footer { | |
display: none !important; | |
background-color: #F8D7DA; | |
} | |
label.svelte-1b6s6s {display: none} | |
div.svelte-rk35yg {display: none;} | |
div.progress-text.svelte-z7cif2.meta-text {display: none;} | |
''' | |
demo = gr.ChatInterface(chat_interface, | |
css=css, | |
description="Lily", | |
clear_btn=None, undo_btn=None, retry_btn=None, | |
) | |
# Add a button to save chat history | |
gr.Button("Close Chat").click(fn=save_chat_history) | |
# Launch the interface | |
demo.launch() | |