Spaces:
Sleeping
Sleeping
File size: 2,085 Bytes
d0c1c22 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 |
import random
def random_response(message, history):
return random.choice(["Yes", "No"])
import time
import gradio as gr
def yes_man(message, history):
if message.endswith("?"):
return "Yes"
else:
return "Ask me anything!"
def echo(message, history, system_prompt, tokens):
response = f"System prompt: {system_prompt}\n Message: {message}."
for i in range(min(len(response), int(tokens))):
time.sleep(0.05)
yield response[: i+1]
# from langchain.chat_models import ChatOpenAI
# from langchain.schema import AIMessage, HumanMessage
# import openai
# import gradio as gr
# import os
# os.environ["OPENAI_API_KEY"] = "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" # Replace with your key
# llm = ChatOpenAI(temperature=1.0, model='gpt-3.5-turbo-0613')
# def predict(message, history):
# history_langchain_format = []
# for human, ai in history:
# history_langchain_format.append(HumanMessage(content=human))
# history_langchain_format.append(AIMessage(content=ai))
# history_langchain_format.append(HumanMessage(content=message))
# gpt_response = llm(history_langchain_format)
# return gpt_response.content
# gr.ChatInterface(predict).launch()
import openai
import gradio as gr
openai.api_key = "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" # Replace with your key
from langchain.chat_models import ChatOpenAI
from langchain.schema import AIMessage, HumanMessage
import openai
import gradio as gr
import os
os.environ["OPENAI_API_KEY"] = "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"
llm = ChatOpenAI(temperature=1.0, model='gpt-3.5-turbo-0613')
def predict(message, history):
history_langchain_format = []
for human, ai in history:
history_langchain_format.append(HumanMessage(content=human))
history_langchain_format.append(AIMessage(content=ai))
history_langchain_format.append(HumanMessage(content=message))
gpt_response = llm(history_langchain_format)
return gpt_response.content
gr.ChatInterface(predict).launch() |