Update app.py
Browse files
app.py
CHANGED
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from dotenv import load_dotenv
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import gradio as gr
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import os
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from llama_index.core import StorageContext, load_index_from_storage, VectorStoreIndex, SimpleDirectoryReader, ChatPromptTemplate, Settings
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from llama_index.llms.huggingface import HuggingFaceInferenceAPI
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from llama_index.embeddings.huggingface import HuggingFaceEmbedding
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import firebase_admin
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from firebase_admin import db, credentials
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import datetime
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@@ -14,14 +15,13 @@ def select_random_name():
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names = ['Clara', 'Lily']
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return random.choice(names)
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# Load environment variables
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load_dotenv()
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# Authenticate to Firebase
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cred = credentials.Certificate("redfernstech-fd8fe-firebase-adminsdk-g9vcn-0537b4efd6.json")
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firebase_admin.initialize_app(cred, {"databaseURL": "https://redfernstech-fd8fe-default-rtdb.firebaseio.com/"})
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# Configure Llama index settings
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Settings.llm = HuggingFaceInferenceAPI(
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model_name="facebook/rag-token-nq",
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tokenizer_name="facebook/rag-token-nq",
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model_name="BAAI/bge-small-en-v1.5"
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)
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# Define
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PERSIST_DIR = "db"
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PDF_DIRECTORY = 'data' #
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# Ensure directories exist
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os.makedirs(PDF_DIRECTORY, exist_ok=True)
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current_chat_history = []
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def data_ingestion_from_directory():
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# Use SimpleDirectoryReader on the directory containing PDF files
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documents = SimpleDirectoryReader(PDF_DIRECTORY).load_data()
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storage_context = StorageContext.from_defaults()
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index = VectorStoreIndex.from_documents(documents)
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return response
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def predict(message, history):
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logo_html = '''
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<div class="circle-logo">
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response = handle_query(message)
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response_with_logo = f'<div class="response-with-logo">{logo_html}<div class="response-text">{response}</div></div>'
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return response_with_logo
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def save_chat_message(session_id, message_data):
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ref = db.reference(f'/chat_history/{session_id}')
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ref.push().set(message_data)
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def chat_interface(message, history):
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try:
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# Generate a unique session ID for this chat session
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session_id = str(uuid.uuid4())
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# Process the user message and generate a response
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response = handle_query(message)
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# Capture the message data
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"sender": "user",
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"message": message,
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"response": response,
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"timestamp": datetime.datetime.now().isoformat()
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}
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#
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save_chat_message(session_id, message_data)
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return response
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except Exception as e:
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return str(e)
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@@ -131,34 +140,32 @@ def chat_interface(message, history):
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# Custom CSS for styling
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css = '''
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.circle-logo {
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display: none !important;
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background-color: #F8D7DA;
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}
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'''
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clear_btn=None, undo_btn=None, retry_btn=None
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).launch()
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for this code i want model from dotenv import load_dotenv
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import gradio as gr
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import os
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from llama_index.core import StorageContext, load_index_from_storage, VectorStoreIndex, SimpleDirectoryReader, ChatPromptTemplate, Settings
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from llama_index.llms.huggingface import HuggingFaceInferenceAPI
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from llama_index.embeddings.huggingface import HuggingFaceEmbedding
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from sentence_transformers import SentenceTransformer
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import firebase_admin
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from firebase_admin import db, credentials
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import datetime
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names = ['Clara', 'Lily']
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return random.choice(names)
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# Example usage
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# Load environment variables
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load_dotenv()
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# authenticate to firebase
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cred = credentials.Certificate("redfernstech-fd8fe-firebase-adminsdk-g9vcn-0537b4efd6.json")
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firebase_admin.initialize_app(cred, {"databaseURL": "https://redfernstech-fd8fe-default-rtdb.firebaseio.com/"})
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# Configure the Llama index settings
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Settings.llm = HuggingFaceInferenceAPI(
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model_name="facebook/rag-token-nq",
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tokenizer_name="facebook/rag-token-nq",
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model_name="BAAI/bge-small-en-v1.5"
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)
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# Define the directory for persistent storage and data
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PERSIST_DIR = "db"
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PDF_DIRECTORY = 'data' # Changed to the directory containing PDFs
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# Ensure directories exist
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os.makedirs(PDF_DIRECTORY, exist_ok=True)
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current_chat_history = []
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def data_ingestion_from_directory():
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# Use SimpleDirectoryReader on the directory containing the PDF files
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documents = SimpleDirectoryReader(PDF_DIRECTORY).load_data()
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storage_context = StorageContext.from_defaults()
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index = VectorStoreIndex.from_documents(documents)
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return response
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# Example usage: Process PDF ingestion from directory
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print("Processing PDF ingestion from directory:", PDF_DIRECTORY)
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data_ingestion_from_directory()
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# Define the function to handle predictions
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"""def predict(message,history):
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response = handle_query(message)
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return response"""
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def predict(message, history):
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logo_html = '''
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<div class="circle-logo">
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response = handle_query(message)
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response_with_logo = f'<div class="response-with-logo">{logo_html}<div class="response-text">{response}</div></div>'
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return response_with_logo
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def save_chat_message(session_id, message_data):
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ref = db.reference(f'/chat_history/{session_id}') # Use the session ID to save chat data
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ref.push().set(message_data)
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# Define your Gradio chat interface function (replace with your actual logic)
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def chat_interface(message, history):
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try:
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# Generate a unique session ID for this chat session
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session_id = str(uuid.uuid4())
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# Process the user message and generate a response (your chatbot logic)
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response = handle_query(message)
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# Capture the message data
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"sender": "user",
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"message": message,
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"response": response,
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"timestamp": datetime.datetime.now().isoformat() # Use a library like datetime
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}
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# Call the save function to store in Firebase with the generated session ID
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save_chat_message(session_id, message_data)
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# Return the bot response
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return response
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except Exception as e:
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return str(e)
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# Custom CSS for styling
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css = '''
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.circle-logo {
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display: inline-block;
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width: 40px;
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height: 40px;
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border-radius: 50%;
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overflow: hidden;
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margin-right: 10px;
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vertical-align: middle;
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}
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.circle-logo img {
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width: 100%;
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height: 100%;
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object-fit: cover;
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}
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.response-with-logo {
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display: flex;
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align-items: center;
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margin-bottom: 10px;
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}
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footer {
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display: none !important;
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background-color: #F8D7DA;
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}
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label.svelte-1b6s6s {display: none}
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'''
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gr.ChatInterface(chat_interface,
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css=css,
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description="Clara",
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clear_btn=None, undo_btn=None, retry_btn=None,
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).launch()
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