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| import gradio as gr | |
| from openai import OpenAI | |
| import json | |
| import os | |
| import requests | |
| from pypdf import PdfReader | |
| import gradio as gr | |
| from huggingface_hub import InferenceClient | |
| from huggingface_hub import login | |
| """ | |
| For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference | |
| """ | |
| login(token=os.getenv("HF_TOKEN")) | |
| client = InferenceClient("HuggingFaceH4/zephyr-7b-beta") | |
| class Harold: | |
| def __init__(self): | |
| self.openai_client = OpenAI(api_key=os.getenv("OPENAI_API_KEY")) | |
| self.name = "Harold" | |
| reader = PdfReader("data/Living-Playbook.pdf") | |
| self.text = "" | |
| for page in reader.pages: | |
| text = page.extract_text() | |
| if text: | |
| self.text += text | |
| def system_prompt(self): | |
| system_prompt = f""" | |
| You are acting as {self.name}, a helpful assistant. | |
| You are answering questions and having discussions about the contents of the book "Living Playbook". | |
| Be friendly and approachable but also consise and to the point. If you don't know the answer, say so. | |
| You might be asked to explain a concept or idea in the book and describe a purpose of a game. You should be able to do this. | |
| """ | |
| system_prompt += f""" | |
| Here is the context of the book: | |
| {self.text} | |
| """ | |
| return system_prompt | |
| def chat(self, message, history): | |
| messages = [{"role": "system", "content": self.system_prompt()}] + history + [{"role": "user", "content": message}] | |
| response = self.openai_client.chat.completions.create( | |
| model="gpt-4o-mini", | |
| messages=messages, | |
| ) | |
| return response.choices[0].message.content | |
| def respond( | |
| message, | |
| history: list[tuple[str, str]], | |
| system_message, | |
| max_tokens, | |
| temperature, | |
| top_p, | |
| ): | |
| messages = [{"role": "system", "content": system_message}] | |
| for val in history: | |
| if val[0]: | |
| messages.append({"role": "user", "content": val[0]}) | |
| if val[1]: | |
| messages.append({"role": "assistant", "content": val[1]}) | |
| messages.append({"role": "user", "content": message}) | |
| response = "" | |
| for message in client.chat_completion( | |
| messages, | |
| max_tokens=max_tokens, | |
| stream=True, | |
| temperature=temperature, | |
| top_p=top_p, | |
| ): | |
| token = message.choices[0].delta.content | |
| response += token | |
| yield response | |
| """ | |
| For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface | |
| """ | |
| demo = gr.ChatInterface( | |
| respond, | |
| additional_inputs=[ | |
| gr.Textbox(value="Hello, You are a friendly Chatbot.", label="System message"), | |
| gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"), | |
| gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), | |
| gr.Slider( | |
| minimum=0.1, | |
| maximum=1.0, | |
| value=0.95, | |
| step=0.05, | |
| label="Top-p (nucleus sampling)", | |
| ), | |
| ], | |
| ) | |
| if __name__ == "__main__": | |
| harold = Harold() | |
| gr.ChatInterface(harold.chat, type="messages").launch() | |