import gradio as gr from PyPDF2 import PdfReader from bs4 import BeautifulSoup import openai import traceback import requests from io import BytesIO from transformers import AutoTokenizer import json from datetime import datetime import os from openai import OpenAI import re # Cache for tokenizers to avoid reloading tokenizer_cache = {} # Global variables for providers PROVIDERS = { "SambaNova": { "name": "SambaNova", "logo": "https://venturebeat.com/wp-content/uploads/2020/02/SambaNovaLogo_H_F.jpg", "endpoint": "https://api.sambanova.ai/v1/", "api_key_env_var": "SAMBANOVA_API_KEY", "models": [ "Meta-Llama-3.1-70B-Instruct", ], "type": "tuples", "max_total_tokens": "50000", }, "Hyperbolic": { "name": "hyperbolic", "logo": "https://www.nftgators.com/wp-content/uploads/2024/07/Hyperbolic.jpg", "endpoint": "https://api.hyperbolic.xyz/v1", "api_key_env_var": "HYPERBOLIC_API_KEY", "models": [ "meta-llama/Llama-3.3-70B-Instruct", "meta-llama/Meta-Llama-3.1-405B-Instruct", ], "type": "tuples", "max_total_tokens": "50000", }, } # Functions for paper fetching def fetch_paper_info_neurips(paper_id): url = f"https://openreview.net/forum?id={paper_id}" response = requests.get(url) if response.status_code != 200: return None, None, None html_content = response.content soup = BeautifulSoup(html_content, 'html.parser') # Extract title title_tag = soup.find('h2', class_='citation_title') title = title_tag.get_text(strip=True) if title_tag else 'Title not found' # Extract authors authors = [] author_div = soup.find('div', class_='forum-authors') if author_div: author_tags = author_div.find_all('a') authors = [tag.get_text(strip=True) for tag in author_tags] author_list = ', '.join(authors) if authors else 'Authors not found' # Extract abstract abstract_div = soup.find('strong', text='Abstract:') if abstract_div: abstract_paragraph = abstract_div.find_next_sibling('div') abstract = abstract_paragraph.get_text(strip=True) if abstract_paragraph else 'Abstract not found' else: abstract = 'Abstract not found' link = f"https://openreview.net/forum?id={paper_id}" return title, author_list, f"**Abstract:** {abstract}\n\n[View on OpenReview]({link})" def fetch_paper_content_neurips(paper_id): try: url = f"https://openreview.net/pdf?id={paper_id}" response = requests.get(url) response.raise_for_status() pdf_content = BytesIO(response.content) reader = PdfReader(pdf_content) text = "" for page in reader.pages: text += page.extract_text() return text except: return None def fetch_paper_content_arxiv(paper_id): try: url = f"https://arxiv.org/pdf/{paper_id}.pdf" response = requests.get(url) response.raise_for_status() pdf_content = BytesIO(response.content) reader = PdfReader(pdf_content) text = "" for page in reader.pages: text += page.extract_text() return text except Exception as e: print(f"Error fetching paper content: {e}") return None def fetch_paper_info_paperpage(paper_id_value): def extract_paper_id(input_string): if re.fullmatch(r'\d+\.\d+', input_string.strip()): return input_string.strip() match = re.search(r'https://huggingface\.co/papers/(\d+\.\d+)', input_string) if match: return match.group(1) return input_string.strip() paper_id_value = extract_paper_id(paper_id_value) url = f"https://huggingface.co/api/papers/{paper_id_value}?field=comments" response = requests.get(url) if response.status_code != 200: return None, None, None paper_info = response.json() title = paper_info.get('title', 'No Title') authors_list = [author.get('name', 'Unknown') for author in paper_info.get('authors', [])] authors = ', '.join(authors_list) summary = paper_info.get('summary', 'No Summary') num_comments = len(paper_info.get('comments', [])) num_upvotes = paper_info.get('upvotes', 0) link = f"https://huggingface.co/papers/{paper_id_value}" details = f"{summary}
👍{num_comments} 💬{num_upvotes}
View on 🤗 hugging face" return title, authors, details def fetch_paper_content_paperpage(paper_id_value): def extract_paper_id(input_string): if re.fullmatch(r'\d+\.\d+', input_string.strip()): return input_string.strip() match = re.search(r'https://huggingface\.co/papers/(\d+\.\d+)', input_string) if match: return match.group(1) return input_string.strip() paper_id_value = extract_paper_id(paper_id_value) text = fetch_paper_content_arxiv(paper_id_value) return text PAPER_SOURCES = { "neurips": { "fetch_info": fetch_paper_info_neurips, "fetch_pdf": fetch_paper_content_neurips }, "paper_page": { "fetch_info": fetch_paper_info_paperpage, "fetch_pdf": fetch_paper_content_paperpage } } def create_chat_interface(provider_dropdown, model_dropdown, paper_content, hf_token_input, default_type, provider_max_total_tokens): def get_fn(message, history, paper_content_value, hf_token_value, provider_name_value, model_name_value, max_total_tokens): provider_info = PROVIDERS[provider_name_value] endpoint = provider_info['endpoint'] api_key_env_var = provider_info['api_key_env_var'] max_total_tokens = int(max_total_tokens) tokenizer_key = f"{provider_name_value}_{model_name_value}" if tokenizer_key not in tokenizer_cache: tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-1B-Instruct", token=os.environ.get("HF_TOKEN")) tokenizer_cache[tokenizer_key] = tokenizer else: tokenizer = tokenizer_cache[tokenizer_key] if paper_content_value: context = f"The discussion is about the following paper:\n{paper_content_value}\n\n" else: context = "" context_tokens = tokenizer.encode(context) context_token_length = len(context_tokens) messages = [] message_tokens_list = [] total_tokens = context_token_length for user_msg, assistant_msg in history: user_tokens = tokenizer.encode(user_msg) messages.append({"role": "user", "content": user_msg}) message_tokens_list.append(len(user_tokens)) total_tokens += len(user_tokens) if assistant_msg: assistant_tokens = tokenizer.encode(assistant_msg) messages.append({"role": "assistant", "content": assistant_msg}) message_tokens_list.append(len(assistant_tokens)) total_tokens += len(assistant_tokens) message_tokens = tokenizer.encode(message) messages.append({"role": "user", "content": message}) message_tokens_list.append(len(message_tokens)) total_tokens += len(message_tokens) if total_tokens > max_total_tokens: available_tokens = max_total_tokens - (total_tokens - context_token_length) if available_tokens > 0: truncated_context_tokens = context_tokens[:available_tokens] context = tokenizer.decode(truncated_context_tokens) context_token_length = available_tokens total_tokens = total_tokens - len(context_tokens) + context_token_length else: context = "" total_tokens -= context_token_length context_token_length = 0 while total_tokens > max_total_tokens and len(messages) > 1: removed_message = messages.pop(0) removed_tokens = message_tokens_list.pop(0) total_tokens -= removed_tokens final_messages = [] if context: final_messages.append({"role": "system", "content": f"{context}"}) final_messages.extend(messages) api_key = hf_token_value or os.environ.get(api_key_env_var) if not api_key: raise ValueError("API token is not provided.") client = OpenAI( base_url=endpoint, api_key=api_key, ) try: completion = client.chat.completions.create( model=model_name_value, messages=final_messages, stream=True, ) response_text = "" for chunk in completion: delta = chunk.choices[0].delta.content or "" response_text += delta yield response_text except json.JSONDecodeError as e: yield f"JSON decoding error: {e.msg}" except openai.OpenAIError as openai_err: yield f"OpenAI error: {openai_err}" except Exception as ex: yield f"Unexpected error: {ex}" chatbot = gr.Chatbot(label="Chatbot", scale=1, height=400, autoscroll=True) chat_interface = gr.ChatInterface( fn=get_fn, chatbot=chatbot, additional_inputs=[paper_content, hf_token_input, provider_dropdown, model_dropdown, provider_max_total_tokens], type="tuples", ) return chat_interface, chatbot def paper_chat_tab(paper_id, paper_from, paper_central_df): # First row with two columns with gr.Row(): # Left column: Paper selection and display with gr.Column(scale=1): gr.Markdown("### Select a Paper") todays_date = datetime.today().strftime('%Y-%m-%d') # Filter papers for today's date and having a paper_page selectable_papers = paper_central_df.df_prettified selectable_papers = selectable_papers[ selectable_papers['paper_page'].notna() & (selectable_papers['paper_page'] != "") & (selectable_papers['date'] == todays_date) ] paper_choices = [(row['title'], row['paper_page']) for _, row in selectable_papers.iterrows()] paper_choices = sorted(paper_choices, key=lambda x: x[0]) if not paper_choices: paper_choices = [("No available papers for today", "")] paper_select = gr.Dropdown( label="Select a paper to chat with:", choices=[p[0] for p in paper_choices], value=paper_choices[0][0] if paper_choices else None ) select_paper_button = gr.Button("Load this paper") # Paper info display content = gr.HTML(value="", elem_id="paper_info_card") # Right column: Provider and model selection with gr.Column(scale=1, visible=False) as provider_section: gr.Markdown("### LLM Provider and Model") provider_names = list(PROVIDERS.keys()) default_provider = provider_names[0] default_type = gr.State(value=PROVIDERS[default_provider]["type"]) default_max_total_tokens = gr.State(value=PROVIDERS[default_provider]["max_total_tokens"]) provider_dropdown = gr.Dropdown( label="Select Provider", choices=provider_names, value=default_provider ) hf_token_input = gr.Textbox( label=f"Enter your {default_provider} API token (optional)", type="password", placeholder=f"Enter your {default_provider} API token to avoid rate limits" ) model_dropdown = gr.Dropdown( label="Select Model", choices=PROVIDERS[default_provider]['models'], value=PROVIDERS[default_provider]['models'][0] ) logo_html = gr.HTML( value=f'' ) note_markdown = gr.Markdown(f"**Note:** This model is supported by {default_provider}.") paper_content = gr.State() # Now a new row, full width, for the chat with gr.Row(visible=False) as chat_row: with gr.Column(): # Create chat interface below the two columns chat_interface, chatbot = create_chat_interface(provider_dropdown, model_dropdown, paper_content, hf_token_input, default_type, default_max_total_tokens) def update_provider(selected_provider): provider_info = PROVIDERS[selected_provider] models = provider_info['models'] logo_url = provider_info['logo'] chatbot_message_type = provider_info['type'] max_total_tokens = provider_info['max_total_tokens'] model_dropdown_choices = gr.update(choices=models, value=models[0]) logo_html_content = f'' logo_html_update = gr.update(value=logo_html_content) note_markdown_update = gr.update(value=f"**Note:** This model is supported by {selected_provider}.") hf_token_input_update = gr.update( label=f"Enter your {selected_provider} API token (optional)", placeholder=f"Enter your {selected_provider} API token to avoid rate limits" ) chatbot_reset = [] return model_dropdown_choices, logo_html_update, note_markdown_update, hf_token_input_update, chatbot_message_type, max_total_tokens, chatbot_reset provider_dropdown.change( fn=update_provider, inputs=provider_dropdown, outputs=[model_dropdown, logo_html, note_markdown, hf_token_input, default_type, default_max_total_tokens, chatbot], queue=False ) def update_paper_info(paper_id_value, paper_from_value, selected_model, old_content): source_info = PAPER_SOURCES.get(paper_from_value, {}) fetch_info_fn = source_info.get("fetch_info") fetch_pdf_fn = source_info.get("fetch_pdf") if not fetch_info_fn or not fetch_pdf_fn: return gr.update(value="
No information available.
"), None, [] title, authors, details = fetch_info_fn(paper_id_value) if title is None and authors is None and details is None: return gr.update(value="
No information could be retrieved.
"), None, [] text = fetch_pdf_fn(paper_id_value) if text is None: text = "Paper content could not be retrieved." card_html = f"""

You are talking with:

{title}

Authors: {authors}

{details}

""" return gr.update(value=card_html), text, [] def select_paper(paper_title): for t, ppage in paper_choices: if t == paper_title: return ppage, "paper_page" return "", "" select_paper_button.click( fn=select_paper, inputs=[paper_select], outputs=[paper_id, paper_from] ) paper_id.change( fn=update_paper_info, inputs=[paper_id, paper_from, model_dropdown, content], outputs=[content, paper_content, chatbot] ) def toggle_provider_visibility(paper_id_value): if paper_id_value and paper_id_value.strip(): return gr.update(visible=True) else: return gr.update(visible=False) # Toggle provider section visibility paper_id.change( fn=toggle_provider_visibility, inputs=[paper_id], outputs=[provider_section] ) # Toggle chat row visibility paper_id.change( fn=toggle_provider_visibility, inputs=[paper_id], outputs=[chat_row] ) def main(): with gr.Blocks(css_paths="style.css") as demo: paper_id = gr.Textbox(label="Paper ID", value="") paper_from = gr.Radio( label="Paper Source", choices=["neurips", "paper_page"], value="neurips" ) class MockPaperCentral: def __init__(self): import pandas as pd data = { 'date': [datetime.today().strftime('%Y-%m-%d')], 'paper_page': ['1234.56789'], 'title': ['An Example Paper'] } self.df_prettified = pd.DataFrame(data) paper_central_df = MockPaperCentral() paper_chat_tab(paper_id, paper_from, paper_central_df) demo.launch(ssr_mode=False) if __name__ == "__main__": main()