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| import gradio as gr | |
| import os | |
| import openai | |
| import gradio as gr | |
| from gradio import ChatInterface | |
| import time | |
| # Get the value of the openai_api_key from environment variable | |
| openai.api_key = os.getenv("OPENAI_API_KEY") | |
| # Import things that are needed generically from langchain | |
| from langchain import LLMMathChain, SerpAPIWrapper | |
| from langchain.agents import AgentType, initialize_agent, load_tools | |
| from langchain.chat_models import ChatOpenAI | |
| from langchain.tools import BaseTool, StructuredTool, Tool, tool | |
| from langchain.tools import MoveFileTool, format_tool_to_openai_function | |
| from langchain.schema import ( | |
| AIMessage, | |
| HumanMessage, | |
| SystemMessage | |
| ) | |
| from langchain.utilities import WikipediaAPIWrapper | |
| from langchain.tools import AIPluginTool | |
| # Question- how can one set up a system message for their Chatbot while using ChatInterface | |
| # Example system message : system = SystemMessage(content = "You are a helpful AI assistant") | |
| # driver | |
| def predict_langchain(user_input, chatbot): | |
| print(f"Chatbot : {chatbot}") | |
| chat = ChatOpenAI(temperature=1.0, streaming=True, model='gpt-3.5-turbo-0613') | |
| messages=[] | |
| for conv in chatbot: | |
| human = HumanMessage(content=conv[0]) | |
| ai = AIMessage(content=conv[1]) | |
| messages.append(human) | |
| messages.append(ai) | |
| messages.append(HumanMessage(content=user_input)) | |
| # getting gpt3.5's response | |
| gpt_response = chat(messages) | |
| return gpt_response.content | |
| def predict(inputs, chatbot): | |
| print(f"Chatbot : {chatbot}") | |
| messages = [] | |
| for conv in chatbot: | |
| user = conv[0] | |
| messages.append({"role": "user", "content":user }) | |
| if conv[1] is None: | |
| break | |
| assistant = conv[1] | |
| messages.append({"role": "assistant", "content":assistant}) | |
| # a ChatCompletion request | |
| response = openai.ChatCompletion.create( | |
| model='gpt-3.5-turbo', | |
| messages= messages, # example : [{'role': 'user', 'content': "What is life? Answer in three words."}], | |
| temperature=1.0, | |
| stream=True # for streaming the output to chatbot | |
| ) | |
| partial_message = "" | |
| for chunk in response: | |
| if len(chunk['choices'][0]['delta']) != 0: | |
| print(chunk['choices'][0]['delta']['content']) | |
| partial_message = partial_message + chunk['choices'][0]['delta']['content'] | |
| yield partial_message | |
| title = "🤖ChatGPT Interface" | |
| description = "Chatbots are a popular application of large language models. Using gradio, you can easily build a demo of your chatbot model and share that with your users, or try it yourself using an intuitive chatbot UI." | |
| examples = ["How are you?","What is love?"] | |
| gr.ChatInterface(predict, | |
| title=title, | |
| description=description, | |
| theme='gstaff/xkcd').queue().launch() |