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import os
import shutil
import gradio as gr
import qdrant_client
from getpass import getpass
openai_api_key = os.getenv('OPENAI_API_KEY')
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.core import Settings
Settings.llm = OpenAI(model="gpt-3.5-turbo", temperature=0.4)
Settings.embed_model = OpenAIEmbedding(model="text-embedding-ada-002")
from llama_index.core import SimpleDirectoryReader, VectorStoreIndex, StorageContext
from llama_index.vector_stores.qdrant import QdrantVectorStore
from llama_index.core.memory import ChatMemoryBuffer
chat_engine = None
index = None
query_engine = None
memory = None
client = None
vector_store = None
storage_context = None
def process_upload(files):
upload_dir = "uploaded_files"
if not os.path.exists(upload_dir):
os.makedirs(upload_dir)
for file_path in files:
file_name = os.path.basename(file_path)
dest = os.path.join(upload_dir, file_name)
if not os.path.exists(dest):
shutil.copy(file_path, dest)
documents = SimpleDirectoryReader(upload_dir).load_data()
global client, vector_store, storage_context, index, query_engine, memory, chat_engine
client = qdrant_client.QdrantClient(location=":memory:")
vector_store = QdrantVectorStore(
collection_name="paper",
client=client,
enable_hybrid=True,
batch_size=20,
)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)
query_engine = index.as_query_engine(vector_store_query_mode="hybrid")
memory = ChatMemoryBuffer.from_defaults(token_limit=3000)
chat_engine = index.as_chat_engine(
chat_mode="context",
memory=memory,
system_prompt=(
"You are an AI assistant who answers the user questions"
),
)
return "Documents uploaded and index built successfully!"
def chat_with_ai(user_input, chat_history):
global chat_engine
if chat_engine is None:
return chat_history, "Please upload documents first."
response = chat_engine.chat(user_input)
references = response.source_nodes
ref, pages = [], []
for node in references:
file_name = node.metadata.get('file_name')
if file_name and file_name not in ref:
ref.append(file_name)
complete_response = str(response) + "\n\n"
if ref or pages:
chat_history.append((user_input, complete_response))
else:
chat_history.append((user_input, str(response)))
return chat_history, ""
def clear_history():
return [], ""
def gradio_interface():
with gr.Blocks() as demo:
gr.Markdown("# AI Assistant")
with gr.Tab("Upload Documents"):
gr.Markdown("Upload PDF, Excel, CSV, DOC/DOCX, or TXT files below:")
# The file upload widget: we specify allowed file types.
file_upload = gr.File(
label="Upload Files",
file_count="multiple",
file_types=[".pdf", ".csv", ".txt", ".xlsx", ".xls", ".doc", ".docx"],
type="filepath"
)
upload_status = gr.Textbox(label="Upload Status", interactive=False)
upload_button = gr.Button("Process Upload")
upload_button.click(process_upload, inputs=file_upload, outputs=upload_status)
with gr.Tab("Chat"):
chatbot = gr.Chatbot(label="Chatbot Assistant")
user_input = gr.Textbox(
placeholder="Ask a question...", label="Enter your question"
)
submit_button = gr.Button("Send")
btn_clear = gr.Button("Restart")
# A State to hold the chat history.
chat_history = gr.State([])
submit_button.click(chat_with_ai, inputs=[user_input, chat_history], outputs=[chatbot, user_input])
user_input.submit(chat_with_ai, inputs=[user_input, chat_history], outputs=[chatbot, user_input])
btn_clear.click(clear_history, outputs=[chatbot, user_input])
return demo
# Launch the Gradio app.
gradio_interface().launch(debug=True)
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