paper_chat_tab.py

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}<br/>👍{num_comments} 💬{num_upvotes}<br/> <a href='{link}' " \
              f"target='_blank'>View on 🤗 hugging face</a>"
    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'<img src="{PROVIDERS[default_provider]["logo"]}" width="100px" />'
            )

            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'<img src="{logo_url}" width="100px" />'
        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="<div>No information available.</div>"), 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="<div>No information could be retrieved.</div>"), None, []

        text = fetch_pdf_fn(paper_id_value)
        if text is None:
            text = "Paper content could not be retrieved."

        card_html = f"""
        <div style="border:1px solid #ccc; border-radius:6px; background:#f9f9f9; padding:15px; margin-bottom:10px;">
            <center><h3 style="margin-top:0; text-decoration:underline;">You are talking with:</h3></center>
            <h3>{title}</h3>
            <p><strong>Authors:</strong> {authors}</p>
            <p>{details}</p>
        </div>
        """

        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()