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from dotenv import load_dotenv
from openai import OpenAI
import json
import os
import requests
from pypdf import PdfReader
import gradio as gr
from huggingface_hub import InferenceClient
load_dotenv(override=True)
"""
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
"""
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
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
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 professional and engaging, but also friendly and approachable.
You are given a context of a book and a question and the conversation history.
You need to answer the question based on the context and the conversation history.
You should be 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",
messages=messages,
)
return response.choices[0].message.content
if __name__ == "__main__":
harold = Harold()
gr.ChatInterface(harold.chat, type="messages").launch()
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