app.py

import os
from getpass import getpass

openai_api_key = os.getenv('OPENAI_API_KEY')
openai_api_key = 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

# Load initial documents
documents = SimpleDirectoryReader("new_file").load_data()

from llama_index.core import VectorStoreIndex, StorageContext
from llama_index.vector_stores.qdrant import QdrantVectorStore
import qdrant_client

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"
)

from llama_index.core.memory import ChatMemoryBuffer

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,
           use the schema fields to generate appropriate and valid json queries"""
    ),
)

import gradio as gr

def chat_with_ai(user_input, chat_history):
    response = chat_engine.chat(user_input)
    references = response.source_nodes
    ref, pages = [], []
    for i in range(len(references)):
        if references[i].metadata['file_name'] not in ref:
            ref.append(references[i].metadata['file_name'])
    complete_response = str(response) + "\n\n"
    if ref != [] or pages != []:
        chat_history.append((user_input, complete_response))
        ref = []
    elif ref == [] or pages == []:
        chat_history.append((user_input, str(response)))
        
    return chat_history, ""

def clear_history():
    return [], ""

def upload_file(file):
    if file is None:
        return "No file uploaded!"
    
    if isinstance(file, list):
        file = file[0]

    if hasattr(file, 'name'):
        file_name = file.name
    elif isinstance(file, dict):
        file_name = file.get("name", "uploaded_file")
    else:
        file_name = "uploaded_file"
    

    if not os.path.exists("new_file"):
        os.makedirs("new_file")
    

    file_path = os.path.join("new_file", file_name)
    if hasattr(file, "read"):
        content = file.read()
    elif isinstance(file, dict) and "data" in file:
        content = file["data"]
    else:
        return "Uploaded file format not recognized."
    
    with open(file_path, "wb") as f:
        f.write(content)
    
    return f"File {file_name} uploaded successfully!"


def gradio_chatbot():
    with gr.Blocks() as demo:
        gr.Markdown("# Chat Interface for LlamaIndex")

        chatbot = gr.Chatbot(label="LlamaIndex Chatbot")
        user_input = gr.Textbox(
            placeholder="Ask a question...", label="Enter your question"
        )

        submit_button = gr.Button("Send")
        btn_clear = gr.Button("Delete Context")

        # Add a file upload component
        file_upload = gr.File(label="Upload a file")

        # Add a button to handle file upload
        upload_button = gr.Button("Upload File")

        chat_history = gr.State([])

        # Define the file upload action
        upload_button.click(upload_file, inputs=file_upload, outputs=user_input)

        # Define the chat interaction
        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(fn=clear_history, outputs=[chatbot, user_input])

    return demo

gradio_chatbot().launch(debug=True)