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| import gradio as gr | |
| from huggingface_hub import InferenceClient | |
| import requests | |
| from bs4 import BeautifulSoup | |
| from bs4.element import Comment | |
| def get_text_from_url(url): | |
| response = requests.get(url) | |
| soup = BeautifulSoup(response.text, 'html.parser') | |
| texts = soup.find_all(text=True) | |
| visible_texts = filter(tag_visible, texts) | |
| return u"\n".join(t.strip() for t in visible_texts) | |
| def tag_visible(element): | |
| if element.parent.name in ['style', 'script', 'head', 'title', 'meta', '[document]']: | |
| return False | |
| if isinstance(element, Comment): | |
| return False | |
| return True | |
| text_list = [] | |
| homepage_url = "https://sites.google.com/view/abhilashnandy/home/" | |
| extensions = ["", "about", "curriculum-vitae", "pmrf-profile-page", "publications"] | |
| for ext in extensions: | |
| url_text = get_text_from_url(homepage_url+ext) | |
| text_list.append(url_text) | |
| # Repeat for sub-links if necessary | |
| """ | |
| 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("QuantFactory/Meta-Llama-3-8B-Instruct-GGUF")#("HuggingFaceH4/zephyr-7b-beta") | |
| SYSTEM_MESSAGE = "You are a QA chatbot to answer queries on my homepage that has the following information -\n\n" + "\n\n".join(text_list) | |
| def respond( | |
| message = SYSTEM_MESSAGE, | |
| history: list[tuple[str, str]], | |
| system_message, | |
| max_tokens=200, | |
| temperature=0.7, | |
| top_p=0.95, | |
| ): | |
| 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="You are a friendly Chatbot.", label="System message"), | |
| # gr.Slider(minimum=1, maximum=8192, 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__": | |
| demo.launch() |