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

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 appriopriate and valid json queries"""
    ),
)

# def is_greeting(user_input):

#     greetings = ["hello", "hi", "hey", "good morning", "good afternoon", "good evening", "greetings"]
#     user_input_lower = user_input.lower().strip()
#     return any(greet in user_input_lower for greet in greetings)
# def is_bye(user_input):

#     greetings = ["thanks", "thanks you", "thanks a lot", "good answer", "good bye", "bye bye"]
#     user_input_lower = user_input.lower().strip()
#     return any(greet in user_input_lower for greet in greetings)
import gradio as gr
def chat_with_ai(user_input, chat_history):
    # if is_greeting(user_input):
    #     response = 'hi, how can i help you?'
    #     chat_history.append((user_input, response))
    #     return chat_history, ""
    # elif is_bye(user_input):
    #     response = "you're wlocome"
    #     chat_history.append((user_input, response))
    #     return 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'])
      # pages.append(references[i].metadata['page_label'])
    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 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") 


        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(fn=clear_history, outputs=[chatbot, user_input])  

    return demo

gradio_chatbot().launch(debug=True)