Spaces:
Runtime error
Runtime error
Update app.py
Browse files
app.py
CHANGED
|
@@ -1,9 +1,10 @@
|
|
| 1 |
-
from dotenv import load_dotenv
|
| 2 |
import gradio as gr
|
| 3 |
import os
|
| 4 |
from llama_index.core import StorageContext, load_index_from_storage, VectorStoreIndex, SimpleDirectoryReader, ChatPromptTemplate, Settings
|
| 5 |
from llama_index.llms.huggingface import HuggingFaceInferenceAPI
|
| 6 |
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
|
|
|
|
| 7 |
import firebase_admin
|
| 8 |
from firebase_admin import db, credentials
|
| 9 |
import datetime
|
|
@@ -14,14 +15,13 @@ def select_random_name():
|
|
| 14 |
names = ['Clara', 'Lily']
|
| 15 |
return random.choice(names)
|
| 16 |
|
|
|
|
| 17 |
# Load environment variables
|
| 18 |
load_dotenv()
|
| 19 |
-
|
| 20 |
-
# Authenticate to Firebase
|
| 21 |
cred = credentials.Certificate("redfernstech-fd8fe-firebase-adminsdk-g9vcn-0537b4efd6.json")
|
| 22 |
firebase_admin.initialize_app(cred, {"databaseURL": "https://redfernstech-fd8fe-default-rtdb.firebaseio.com/"})
|
| 23 |
-
|
| 24 |
-
# Configure Llama index settings
|
| 25 |
Settings.llm = HuggingFaceInferenceAPI(
|
| 26 |
model_name="facebook/rag-token-nq",
|
| 27 |
tokenizer_name="facebook/rag-token-nq",
|
|
@@ -34,9 +34,9 @@ Settings.embed_model = HuggingFaceEmbedding(
|
|
| 34 |
model_name="BAAI/bge-small-en-v1.5"
|
| 35 |
)
|
| 36 |
|
| 37 |
-
# Define
|
| 38 |
PERSIST_DIR = "db"
|
| 39 |
-
PDF_DIRECTORY = 'data' #
|
| 40 |
|
| 41 |
# Ensure directories exist
|
| 42 |
os.makedirs(PDF_DIRECTORY, exist_ok=True)
|
|
@@ -46,7 +46,7 @@ os.makedirs(PERSIST_DIR, exist_ok=True)
|
|
| 46 |
current_chat_history = []
|
| 47 |
|
| 48 |
def data_ingestion_from_directory():
|
| 49 |
-
# Use SimpleDirectoryReader on the directory containing PDF files
|
| 50 |
documents = SimpleDirectoryReader(PDF_DIRECTORY).load_data()
|
| 51 |
storage_context = StorageContext.from_defaults()
|
| 52 |
index = VectorStoreIndex.from_documents(documents)
|
|
@@ -91,6 +91,14 @@ def handle_query(query):
|
|
| 91 |
|
| 92 |
return response
|
| 93 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 94 |
def predict(message, history):
|
| 95 |
logo_html = '''
|
| 96 |
<div class="circle-logo">
|
|
@@ -100,17 +108,17 @@ def predict(message, history):
|
|
| 100 |
response = handle_query(message)
|
| 101 |
response_with_logo = f'<div class="response-with-logo">{logo_html}<div class="response-text">{response}</div></div>'
|
| 102 |
return response_with_logo
|
| 103 |
-
|
| 104 |
def save_chat_message(session_id, message_data):
|
| 105 |
-
ref = db.reference(f'/chat_history/{session_id}')
|
| 106 |
ref.push().set(message_data)
|
| 107 |
|
|
|
|
| 108 |
def chat_interface(message, history):
|
| 109 |
try:
|
| 110 |
# Generate a unique session ID for this chat session
|
| 111 |
session_id = str(uuid.uuid4())
|
| 112 |
|
| 113 |
-
# Process the user message and generate a response
|
| 114 |
response = handle_query(message)
|
| 115 |
|
| 116 |
# Capture the message data
|
|
@@ -118,12 +126,13 @@ def chat_interface(message, history):
|
|
| 118 |
"sender": "user",
|
| 119 |
"message": message,
|
| 120 |
"response": response,
|
| 121 |
-
"timestamp": datetime.datetime.now().isoformat()
|
| 122 |
}
|
| 123 |
|
| 124 |
-
#
|
| 125 |
save_chat_message(session_id, message_data)
|
| 126 |
|
|
|
|
| 127 |
return response
|
| 128 |
except Exception as e:
|
| 129 |
return str(e)
|
|
@@ -131,34 +140,32 @@ def chat_interface(message, history):
|
|
| 131 |
# Custom CSS for styling
|
| 132 |
css = '''
|
| 133 |
.circle-logo {
|
| 134 |
-
|
| 135 |
-
|
| 136 |
-
|
| 137 |
-
|
| 138 |
-
|
| 139 |
-
|
| 140 |
-
|
| 141 |
-
|
| 142 |
-
|
| 143 |
-
|
| 144 |
-
|
| 145 |
-
|
| 146 |
-
|
| 147 |
-
|
| 148 |
-
|
| 149 |
-
|
| 150 |
-
|
| 151 |
-
|
| 152 |
-
|
| 153 |
display: none !important;
|
| 154 |
background-color: #F8D7DA;
|
| 155 |
}
|
| 156 |
-
|
| 157 |
'''
|
| 158 |
-
|
| 159 |
-
|
| 160 |
-
|
| 161 |
-
|
| 162 |
-
|
| 163 |
-
clear_btn=None, undo_btn=None, retry_btn=None
|
| 164 |
-
).launch()
|
|
|
|
| 1 |
+
for this code i want model from dotenv import load_dotenv
|
| 2 |
import gradio as gr
|
| 3 |
import os
|
| 4 |
from llama_index.core import StorageContext, load_index_from_storage, VectorStoreIndex, SimpleDirectoryReader, ChatPromptTemplate, Settings
|
| 5 |
from llama_index.llms.huggingface import HuggingFaceInferenceAPI
|
| 6 |
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
|
| 7 |
+
from sentence_transformers import SentenceTransformer
|
| 8 |
import firebase_admin
|
| 9 |
from firebase_admin import db, credentials
|
| 10 |
import datetime
|
|
|
|
| 15 |
names = ['Clara', 'Lily']
|
| 16 |
return random.choice(names)
|
| 17 |
|
| 18 |
+
# Example usage
|
| 19 |
# Load environment variables
|
| 20 |
load_dotenv()
|
| 21 |
+
# authenticate to firebase
|
|
|
|
| 22 |
cred = credentials.Certificate("redfernstech-fd8fe-firebase-adminsdk-g9vcn-0537b4efd6.json")
|
| 23 |
firebase_admin.initialize_app(cred, {"databaseURL": "https://redfernstech-fd8fe-default-rtdb.firebaseio.com/"})
|
| 24 |
+
# Configure the Llama index settings
|
|
|
|
| 25 |
Settings.llm = HuggingFaceInferenceAPI(
|
| 26 |
model_name="facebook/rag-token-nq",
|
| 27 |
tokenizer_name="facebook/rag-token-nq",
|
|
|
|
| 34 |
model_name="BAAI/bge-small-en-v1.5"
|
| 35 |
)
|
| 36 |
|
| 37 |
+
# Define the directory for persistent storage and data
|
| 38 |
PERSIST_DIR = "db"
|
| 39 |
+
PDF_DIRECTORY = 'data' # Changed to the directory containing PDFs
|
| 40 |
|
| 41 |
# Ensure directories exist
|
| 42 |
os.makedirs(PDF_DIRECTORY, exist_ok=True)
|
|
|
|
| 46 |
current_chat_history = []
|
| 47 |
|
| 48 |
def data_ingestion_from_directory():
|
| 49 |
+
# Use SimpleDirectoryReader on the directory containing the PDF files
|
| 50 |
documents = SimpleDirectoryReader(PDF_DIRECTORY).load_data()
|
| 51 |
storage_context = StorageContext.from_defaults()
|
| 52 |
index = VectorStoreIndex.from_documents(documents)
|
|
|
|
| 91 |
|
| 92 |
return response
|
| 93 |
|
| 94 |
+
# Example usage: Process PDF ingestion from directory
|
| 95 |
+
print("Processing PDF ingestion from directory:", PDF_DIRECTORY)
|
| 96 |
+
data_ingestion_from_directory()
|
| 97 |
+
|
| 98 |
+
# Define the function to handle predictions
|
| 99 |
+
"""def predict(message,history):
|
| 100 |
+
response = handle_query(message)
|
| 101 |
+
return response"""
|
| 102 |
def predict(message, history):
|
| 103 |
logo_html = '''
|
| 104 |
<div class="circle-logo">
|
|
|
|
| 108 |
response = handle_query(message)
|
| 109 |
response_with_logo = f'<div class="response-with-logo">{logo_html}<div class="response-text">{response}</div></div>'
|
| 110 |
return response_with_logo
|
|
|
|
| 111 |
def save_chat_message(session_id, message_data):
|
| 112 |
+
ref = db.reference(f'/chat_history/{session_id}') # Use the session ID to save chat data
|
| 113 |
ref.push().set(message_data)
|
| 114 |
|
| 115 |
+
# Define your Gradio chat interface function (replace with your actual logic)
|
| 116 |
def chat_interface(message, history):
|
| 117 |
try:
|
| 118 |
# Generate a unique session ID for this chat session
|
| 119 |
session_id = str(uuid.uuid4())
|
| 120 |
|
| 121 |
+
# Process the user message and generate a response (your chatbot logic)
|
| 122 |
response = handle_query(message)
|
| 123 |
|
| 124 |
# Capture the message data
|
|
|
|
| 126 |
"sender": "user",
|
| 127 |
"message": message,
|
| 128 |
"response": response,
|
| 129 |
+
"timestamp": datetime.datetime.now().isoformat() # Use a library like datetime
|
| 130 |
}
|
| 131 |
|
| 132 |
+
# Call the save function to store in Firebase with the generated session ID
|
| 133 |
save_chat_message(session_id, message_data)
|
| 134 |
|
| 135 |
+
# Return the bot response
|
| 136 |
return response
|
| 137 |
except Exception as e:
|
| 138 |
return str(e)
|
|
|
|
| 140 |
# Custom CSS for styling
|
| 141 |
css = '''
|
| 142 |
.circle-logo {
|
| 143 |
+
display: inline-block;
|
| 144 |
+
width: 40px;
|
| 145 |
+
height: 40px;
|
| 146 |
+
border-radius: 50%;
|
| 147 |
+
overflow: hidden;
|
| 148 |
+
margin-right: 10px;
|
| 149 |
+
vertical-align: middle;
|
| 150 |
+
}
|
| 151 |
+
.circle-logo img {
|
| 152 |
+
width: 100%;
|
| 153 |
+
height: 100%;
|
| 154 |
+
object-fit: cover;
|
| 155 |
+
}
|
| 156 |
+
.response-with-logo {
|
| 157 |
+
display: flex;
|
| 158 |
+
align-items: center;
|
| 159 |
+
margin-bottom: 10px;
|
| 160 |
+
}
|
| 161 |
+
footer {
|
| 162 |
display: none !important;
|
| 163 |
background-color: #F8D7DA;
|
| 164 |
}
|
| 165 |
+
label.svelte-1b6s6s {display: none}
|
| 166 |
'''
|
| 167 |
+
gr.ChatInterface(chat_interface,
|
| 168 |
+
css=css,
|
| 169 |
+
description="Clara",
|
| 170 |
+
clear_btn=None, undo_btn=None, retry_btn=None,
|
| 171 |
+
).launch()
|
|
|
|
|
|