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| 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() |