Hi everyone.
I am trying to build a basicchatbot with streaming.
from the documentation we have the following code.
import gradio as gr
import random
import time
with gr.Blocks() as demo:
chatbot = gr.Chatbot()
msg = gr.Textbox()
clear = gr.Button("Clear")
def respond(message, chat_history):
bot_message = random.choice(["How are you?", "I love you", "I'm very hungry"])
chat_history.append((message, bot_message))
time.sleep(2)
return "", chat_history
msg.submit(respond, [msg, chatbot], [msg, chatbot])
clear.click(lambda: None, None, chatbot, queue=False)
if __name__ == "__main__":
demo.launch()
and I wrote this function
import os
os.environ["OPENAI_API_KEY"] = ' dsdsds'
from pathlib import Path
from llama_index import LLMPredictor, GPTVectorStoreIndex, PromptHelper, ServiceContext
from llama_index import StorageContext, load_index_from_storage
from langchain.chat_models import ChatOpenAI
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
llm_predictor = LLMPredictor(
llm=ChatOpenAI(
streaming=True,
callbacks=[StreamingStdOutCallbackHandler()],
temperature=0.7,
model_name="gpt-3.5-turbo",
max_tokens=1000
)
)
max_input_size = 4096
max_chunk_overlap = 0.2
chunk_size_limit = 60
num_outputs = 1000
prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit)
service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor)
storage_context = StorageContext.from_defaults(persist_dir="./storage")
index = load_index_from_storage(storage_context, service_context=service_context)
question = 'how many times has my son watched the curious george movie?'
query_engine = index.as_query_engine()
response = query_engine.query(question)
#print(response)
How can I combine them together to make a chatbot with Streaming?
I tried the following code:
import os
os.environ["OPENAI_API_KEY"] = 'dsdsdsddsdsd'
from pathlib import Path
from llama_index import LLMPredictor, GPTVectorStoreIndex, PromptHelper, ServiceContext
from llama_index import StorageContext, load_index_from_storage
from langchain.chat_models import ChatOpenAI
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
import gradio as gr
import random
import time
llm_predictor = LLMPredictor(
llm=ChatOpenAI(
streaming=True,
callbacks=[StreamingStdOutCallbackHandler()],
temperature=0.7,
model_name="gpt-3.5-turbo",
max_tokens=2000
))
max_input_size = 4096
max_chunk_overlap = 0.2
chunk_size_limit = 600
num_outputs = 2000
prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit)
service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor)
storage_context = StorageContext.from_defaults(persist_dir="./storage")
index = load_index_from_storage(storage_context, service_context=service_context)
query_engine = index.as_query_engine()
with gr.Blocks() as demo:
chatbot = gr.Chatbot()
msg = gr.Textbox()
clear = gr.Button("Clear")
def user(user_message, history):
return gr.update(value="", interactive=False), history + [[user_message, None]]
def bot(history):
bot_message = query_engine.query(history[len(history)-1][0])
history[-1][1] = ""
for character in bot_message:
history[-1][1] += character
time.sleep(0.05)
yield history
response = msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
bot, chatbot, chatbot
)
response.then(lambda: gr.update(interactive=True), None, [msg], queue=False)
clear.click(lambda: None, None, chatbot, queue=False)
demo.queue()
demo.launch()
but it says
TypeError: ‘Response’ object is not iterable
Any kind or hint is greatly appreciated
