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| ''' | |
| import subprocess | |
| subprocess.check_call(["pip", "install", "-q", "openai"]) | |
| subprocess.check_call(["pip", "install", "-q", "gradio", "transformers", "python-dotenv"]) | |
| import gradio as gr | |
| from transformers import TFAutoModelForCausalLM, AutoTokenizer | |
| import openai | |
| from dotenv import load_dotenv | |
| import os | |
| load_dotenv() # load environment variables from .env file | |
| api_key = os.getenv("OPENAI_API_KEY") # access the value of the OPENAI_API_KEY environment variable | |
| def openai_chat(prompt): | |
| if "who are you" in prompt.lower() or "your name" in prompt.lower() or "name" in prompt.lower(): | |
| return "My name is ChatSherman. How can I assist you today?" | |
| else: | |
| prompt = "I'm an AI chatbot named ChatSherman designed by a student named ShermanAI at the Department of Electronic and Information Engineering at The Hong Kong Polytechnic University to help you with your engineering questions. Also, I can assist with a wide range of topics and questions." + prompt | |
| completions = openai.Completion.create(engine="text-davinci-003", prompt=prompt, max_tokens=1024, n=1, temperature=0.5,) | |
| message = completions.choices[0].text | |
| return message.strip() | |
| def chatbot(talk_to_chatsherman, history=[]): | |
| output = openai_chat(talk_to_chatsherman) | |
| history.append((talk_to_chatsherman, output)) | |
| return history, history | |
| title = "ChatSherman" | |
| description = "This is an AI chatbot powered by ShermanAI. Enter your question below to get started." | |
| examples = [ | |
| ["What is ChatSherman, and how does it work?", []], | |
| ["Is my personal information and data safe when I use the ChatSherman chatbot?", []], | |
| ["What are some common applications of deep learning in engineering?", []] | |
| ] | |
| inputs = [gr.inputs.Textbox(label="Talk to ChatSherman: "), "state"] | |
| outputs = ["chatbot", "state"] | |
| interface = gr.Interface(fn=chatbot, inputs=inputs, outputs=outputs, title=title, description=description, examples=examples) | |
| interface.launch(debug=True) | |
| ''' | |
| python -m pip install --upgrade pip | |
| import subprocess | |
| subprocess.check_call(["pip", "install", "-q", "openai"]) | |
| subprocess.check_call(["pip", "install", "-q", "gradio", "transformers", "python-dotenv"]) | |
| import openai | |
| import gradio as gr | |
| openai.api_key = "OPENAI_API_KEY" | |
| def predict(message, history): | |
| history_openai_format = [] | |
| for human, assistant in history: | |
| history_openai_format.append({"role": "user", "content": human }) | |
| history_openai_format.append({"role": "assistant", "content":assistant}) | |
| history_openai_format.append({"role": "user", "content": message}) | |
| response = openai.ChatCompletion.create( | |
| model='gpt-3.5-turbo', | |
| messages= history_openai_format, | |
| temperature=1.0, | |
| stream=True | |
| ) | |
| partial_message = "" | |
| for chunk in response: | |
| if len(chunk['choices'][0]['delta']) != 0: | |
| partial_message = partial_message + chunk['choices'][0]['delta']['content'] | |
| yield partial_message | |
| gr.ChatInterface(predict).queue().launch() |