Update app.py

app.py

@@ -1,418 +1,98 @@
-import logging
 import os
-import sys
-from contextlib import contextmanager, redirect_stdout
-from io import StringIO
-from typing import Callable, Generator, Optional, List, Dict
-import requests
-import json
-from consts import AUTO_SEARCH_KEYWORD, SEARCH_TOOL_INSTRUCTION, RELATED_QUESTIONS_TEMPLATE_SEARCH, SEARCH_TOOL_INSTRUCTION, RAG_TEMPLATE, GOOGLE_SEARCH_ENDPOINT, DEFAULT_SEARCH_ENGINE_TIMEOUT, RELATED_QUESTIONS_TEMPLATE_NO_SEARCH
-import re
-import asyncio
-import random
+import gradio as gr
+import tempfile
+from pathlib import Path
+from utils import get_file_name, run_pdf2text, run_paper2slides
 
-import streamlit as st
-import yaml
+# Create a temporary directory for outputs
+OUTPUT_FOLDER = tempfile.mkdtemp(prefix="pdf_slides_")
 
-current_dir = os.path.dirname(os.path.abspath(__file__))
-kit_dir = os.path.abspath(os.path.join(current_dir, '..'))
-repo_dir = os.path.abspath(os.path.join(kit_dir, '..'))
-
-sys.path.append(kit_dir)
-sys.path.append(repo_dir)
-
-
-from visual_env_utils import are_credentials_set, env_input_fields, initialize_env_variables, save_credentials
-
-logging.basicConfig(level=logging.INFO)
-GOOGLE_API_KEY = st.secrets["google_api_key"]
-GOOGLE_CX = st.secrets["google_cx"]
-BACKUP_KEYS = [st.secrets["backup_key_1"], st.secrets["backup_key_2"], st.secrets["backup_key_3"], st.secrets["backup_key_4"], st.secrets["backup_key_5"]]
-
-CONFIG_PATH = os.path.join(current_dir, "config.yaml")
-
-USER_AGENTS = [
-    "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36",
-    "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/14.0.3 Safari/605.1.15",
-]
-
-def load_config():
-    with open(CONFIG_PATH, 'r') as yaml_file:
-        return yaml.safe_load(yaml_file)
-
-
-config = load_config()
-prod_mode = config.get('prod_mode', False)
-additional_env_vars = config.get('additional_env_vars', None)
-
-@contextmanager
-def st_capture(output_func: Callable[[str], None]) -> Generator:
-    """
-    context manager to catch stdout and send it to an output streamlit element
-    Args:
-        output_func (function to write terminal output in
-    Yields:
-        Generator:
-    """
-    with StringIO() as stdout, redirect_stdout(stdout):
-        old_write = stdout.write
-
-        def new_write(string: str) -> int:
-            ret = old_write(string)
-            output_func(stdout.getvalue())
-            return ret
-
-        stdout.write = new_write  # type: ignore
-        yield
-
-async def run_samba_api_inference(query, system_prompt = None, ignore_context=False, max_tokens_to_generate=None, num_seconds_to_sleep=1, over_ride_key=None):
-    # First construct messages
-    messages = []
-    if system_prompt is not None:
-        messages.append({"role": "system", "content": system_prompt})
-
-    if not ignore_context:
-        for ques, ans in zip(
-            st.session_state.chat_history[::3],
-            st.session_state.chat_history[1::3],
-        ):
-            messages.append({"role": "user", "content": ques})
-            messages.append({"role": "assistant", "content": ans})
-    messages.append({"role": "user", "content": query})
-
-    # Create payloads
-    payload = {
-        "messages": messages,
-        "model": config.get("model")
-    }
-    if max_tokens_to_generate is not None:
-        payload["max_tokens"] = max_tokens_to_generate
-
-    if over_ride_key is None:
-        api_key = st.session_state.SAMBANOVA_API_KEY
-    else:
-        api_key = over_ride_key
-    headers = {
-        "Authorization": f"Bearer {api_key}",
-        "Content-Type": "application/json"
-    }
-
-    try:
-        post_response = await asyncio.get_event_loop().run_in_executor(None, lambda: requests.post(config.get("url"), json=payload, headers=headers, stream=True))
-        post_response.raise_for_status()
-    except requests.exceptions.HTTPError as e:
-        if post_response.status_code in {401, 503}:
-            st.info(f"Invalid Key! Please make sure you have a valid SambaCloud key from https://cloud.sambanova.ai/.")
-            return "Invalid Key! Please make sure you have a valid SambaCloud key from https://cloud.sambanova.ai/."
-        if post_response.status_code in {429, 504}:
-            await asyncio.sleep(num_seconds_to_sleep)
-            return await run_samba_api_inference(query, over_ride_key=random.choice(BACKUP_KEYS))  # Retry the request
-        else:
-            print(f"Request failed with status code: {post_response.status_code}. Error: {e}")
-            return "Invalid Key! Please make sure you have a valid SambaCloud key from https://cloud.sambanova.ai/."
-
-    response_data = json.loads(post_response.text)
-
-    return response_data["choices"][0]["message"]["content"]
-
-def extract_query(text):
-    # Regular expression to capture the query within the quotes
-    match = re.search(r'query="(.*?)"', text)
+def paper2slides(pdf_path: str, logo_path: str='logo.npg'):
+    # Ensure the output directory exists
+    os.makedirs(OUTPUT_FOLDER, exist_ok=True)
     
-    # If a match is found, return the query, otherwise return None
-    if match:
-        return match.group(1)
-    return None
-
-def extract_text_between_brackets(text):
-    # Using regular expressions to find all text between brackets
-    matches = re.findall(r'\[(.*?)\]', text)
-    return matches
+    output_path = os.path.join(OUTPUT_FOLDER, "converted_slides.pptx")
+    file_name = get_file_name(full_path=pdf_path)
+    run_pdf2text(paper_pdf_path=pdf_path, save_json_name=file_name + '.json') ## pdf to json file
+    run_paper2slides(paper_json_name=file_name + '.json', model='llama3_70b', logo_path=logo_path, save_file_name=output_path)  ## json file to slides
+    return output_path
 
-def search_with_google(query: str):
+def convert_pdf_to_slides(pdf_path):
     """
-    Search with google and return the contexts.
-    """
-    params = {
-        "key": GOOGLE_API_KEY,
-        "cx": GOOGLE_CX,
-        "q": query,
-        "num": 5,
-    }
-    response = requests.get(
-        GOOGLE_SEARCH_ENDPOINT, params=params, timeout=DEFAULT_SEARCH_ENGINE_TIMEOUT
-    )
-
-    if not response.ok:
-        raise Exception(response.status_code, "Search engine error.")
-    json_content = response.json()
-
-    contexts = json_content["items"][:5]
-
-    return contexts
-
-async def get_related_questions(query, contexts = None):
-    if contexts:
-        related_question_system_prompt = RELATED_QUESTIONS_TEMPLATE_SEARCH.format(
-            context="\n\n".join([c["snippet"] for c in contexts])
-        )
-    else:
-        # When no search is performed, use a generic prompt
-        related_question_system_prompt = RELATED_QUESTIONS_TEMPLATE_SEARCH
-
-    related_questions_raw = await run_samba_api_inference(query, related_question_system_prompt)
-
-    try:
-        return json.loads(related_questions_raw)  
-    except:
-        try:
-            extracted_related_questions = extract_text_between_brackets(related_questions_raw)
-            return json.loads(extracted_related_questions)
-        except:
-            return []
-
-def process_citations(response: str, search_result_contexts: List[Dict]) -> str:
-    """
-    Process citations in the response and replace them with numbered icons.
+    Convert a PDF file to a slide deck.
     
     Args:
-        response (str): The original response with citations.
-        search_result_contexts (List[Dict]): The search results with context information.
-    
+        pdf_path (str): Path to the input PDF file
+        
     Returns:
-        str: The processed response with numbered icons for citations.
+        str: Path to the created slide deck file
     """
-    citations = re.findall(r'\[citation:(\d+)\]', response)
+    print(f"Processing PDF: {pdf_path}")
+    
+    # Ensure the output directory exists
+    os.makedirs(OUTPUT_FOLDER, exist_ok=True)
     
-    for i, citation in enumerate(citations, 1):
-        response = response.replace(f'[citation:{citation}]', f'<sup>[{i}]</sup>')
+    # Create output filename in the temporary directory
+    output_filename = f"{Path(pdf_path).stem}_slides.pptx"
+    output_path = os.path.join(OUTPUT_FOLDER, output_filename)
     
-    return response
+    output_path = paper2slides(pdf_path, 'logo.png')
+    
+    print(f"Conversion complete. Slides saved to: {output_path}")
+    return output_path
 
-def generate_citation_links(search_result_contexts: List[Dict]) -> str:
+def process_upload(pdf_file):
     """
-    Generate HTML for citation links.
+    Process the uploaded PDF file and return the path to the generated slide deck.
     
     Args:
-        search_result_contexts (List[Dict]): The search results with context information.
-    
+        pdf_file (tempfile): The uploaded PDF file
+        
     Returns:
-        str: HTML string with numbered citation links.
+        str: Path to the slide deck for download
     """
-    citation_links = []
-    for i, context in enumerate(search_result_contexts, 1):
-        title = context.get('title', 'No title')
-        link = context.get('link', '#')
-        citation_links.append(f'<p>[{i}] <a href="{link}" target="_blank">{title}</a></p>')
+    if pdf_file is None:
+        return None
     
-    return ''.join(citation_links)
-
-        
-async def run_auto_search_pipe(query):
-    full_context_answer = asyncio.create_task(run_samba_api_inference(query))
-    related_questions_no_search = asyncio.create_task(get_related_questions(query))
-
-    # First call Llama3.1 8B with special system prompt for auto search
-    with st.spinner('Checking if web search is needed...'):
-        auto_search_result = await run_samba_api_inference(query, SEARCH_TOOL_INSTRUCTION, True, max_tokens_to_generate=100)
-
-    # If Llama3.1 8B returns a search query then run search pipeline
-    if AUTO_SEARCH_KEYWORD in auto_search_result:
-        st.session_state.search_performed = True
-        # search
-        with st.spinner('Searching the internet...'):
-            search_result_contexts = search_with_google(extract_query(auto_search_result))
-
-        # RAG response
-        with st.spinner('Generating response based on web search...'):
-            rag_system_prompt = RAG_TEMPLATE.format(
-                context="\n\n".join(
-                    [f"[[citation:{i+1}]] {c['snippet']}" for i, c in enumerate(search_result_contexts)]
-                )
-            )
-
-            model_response = asyncio.create_task(run_samba_api_inference(query, rag_system_prompt))
-            related_questions = asyncio.create_task(get_related_questions(query, search_result_contexts))
-            # Process citations and generate links
-            citation_links = generate_citation_links(search_result_contexts)
-            
-            model_response_complete = await model_response
-            processed_response = process_citations(model_response_complete, search_result_contexts)
-            related_questions_complete = await related_questions
-
-
-        return processed_response, citation_links, related_questions_complete
+    try:
+        # Convert the PDF to slides
+        slide_path = convert_pdf_to_slides(pdf_file.name)
+        return slide_path
+    except Exception as e:
+        print(f"Error during conversion: {str(e)}")
+        return None
+
+# Create the Gradio interface
+with gr.Blocks(title="PDF to Slides Converter") as app:
+    gr.Markdown("# PDF to Slides Converter")
+    gr.Markdown("Upload a PDF file to convert it into a slide deck presentation.")
     
-    # If Llama3.1 8B returns an answer directly, then please query Llama 405B to get the best possible answer
-    else:
-        st.session_state.search_performed = False
-        result = await full_context_answer
-        related_questions = await related_questions_no_search
-        return result, "", related_questions
-
-
-def handle_userinput(user_question: Optional[str]) -> None:
-    """
-    Handle user input and generate a response, also update chat UI in streamlit app
-    Args:
-        user_question (str): The user's question or input.
-    """
-    if user_question:
-        # Clear any existing related question buttons
-        if 'related_questions' in st.session_state:
-            st.session_state.related_questions = []
-
-        async def run_search():
-            return await run_auto_search_pipe(user_question)
+    with gr.Row():
+        with gr.Column():
+            pdf_input = gr.File(label="Upload PDF File", file_types=[".pdf"])
+            convert_btn = gr.Button("Convert to Slides", variant="primary")
         
-        response, citation_links, related_questions = asyncio.run(run_search())
-        if st.session_state.search_performed:
-            search_or_not_text = "🔍 Web search was performed for this query."
-        else:
-            search_or_not_text = "📚 This response was generated from the model's knowledge."
-
-        st.session_state.chat_history.append(user_question)
-        st.session_state.chat_history.append((response, citation_links))
-        st.session_state.chat_history.append(search_or_not_text)
-
-        # Store related questions in session state
-        st.session_state.related_questions = related_questions
-
-    for ques, ans, search_or_not_text in zip(
-        st.session_state.chat_history[::3],
-        st.session_state.chat_history[1::3],
-        st.session_state.chat_history[2::3],
-    ):
-        with st.chat_message('user'):
-            st.write(f'{ques}')
+        with gr.Column():
+            output_file = gr.File(label="Download Slides")
+            status = gr.Markdown("Upload a PDF and click 'Convert to Slides'")
     
-        with st.chat_message(
-            'ai',
-            avatar='https://sambanova.ai/hubfs/logotype_sambanova_orange.png',
-        ):
-            st.markdown(f'{ans[0]}', unsafe_allow_html=True)
-            if ans[1]:
-                st.markdown("### Sources", unsafe_allow_html=True)
-                st.markdown(ans[1], unsafe_allow_html=True)
-            st.info(search_or_not_text)
-    if len(st.session_state.related_questions) > 0:
-        st.markdown("### Related Questions")
-        for question in st.session_state.related_questions:
-            if st.button(question):
-                setChatInputValue(question)
-
-def setChatInputValue(chat_input_value: str) -> None:
-    js = f"""
-    <script>
-        function insertText(dummy_var_to_force_repeat_execution) {{
-            var chatInput = parent.document.querySelector('textarea[data-testid="stChatInputTextArea"]');
-            var nativeInputValueSetter = Object.getOwnPropertyDescriptor(window.HTMLTextAreaElement.prototype, "value").set;
-            nativeInputValueSetter.call(chatInput, "{chat_input_value}");
-            var event = new Event('input', {{ bubbles: true}});
-            chatInput.dispatchEvent(event);
-        }}
-        insertText(3);
-    </script>
-    """
-    st.components.v1.html(js)
-
-def main() -> None:
-    st.set_page_config(
-        page_title='Auto Web Search Demo',
-        page_icon='https://sambanova.ai/hubfs/logotype_sambanova_orange.png',
+    convert_btn.click(
+        fn=process_upload,
+        inputs=[pdf_input],
+        outputs=[output_file],
+        api_name="convert"
     )
-
-
-    initialize_env_variables(prod_mode, additional_env_vars)
-
-    if 'input_disabled' not in st.session_state:
-        if 'SAMBANOVA_API_KEY' in st.session_state:
-            st.session_state.input_disabled = False
-        else:
-            st.session_state.input_disabled = True
-    if 'chat_history' not in st.session_state:
-        st.session_state.chat_history = []
-    if 'search_performed' not in st.session_state:
-        st.session_state.search_performed = False
-    if 'related_questions' not in st.session_state:
-        st.session_state.related_questions = [] 
-
-    st.title(' Auto Web Search')
-    st.subheader('Powered by :orange[SambaNova Cloud] and Llama405B')
-
-    with st.sidebar:
-        st.title('Get your :orange[SambaNova Cloud] API key [here](https://cloud.sambanova.ai/apis)')
-
-        if not are_credentials_set(additional_env_vars):
-            api_key, additional_vars = env_input_fields(additional_env_vars)
-            if st.button('Save Credentials'):
-                message = save_credentials(api_key, additional_vars, prod_mode)
-                st.session_state.input_disabled = False
-                st.success(message)
-                st.rerun()
-
-        else:
-            st.success('Credentials are set')
-            if st.button('Clear Credentials'):
-                save_credentials('', {var: '' for var in (additional_env_vars or [])}, prod_mode)
-                st.session_state.input_disabled = True
-                st.rerun()
-
-
-        if are_credentials_set(additional_env_vars):
-            with st.expander('**Example Queries With Search**', expanded=True):
-                if st.button('What is the population of Virginia?'):
-                    setChatInputValue(
-                        'What is the population of Virginia?'
-                    )
-                if st.button('SNP 500 stock market moves'):
-                    setChatInputValue('SNP 500 stock market moves')
-                if st.button('What is the weather in Palo Alto?'):
-                    setChatInputValue(
-                        'What is the weather in Palo Alto?'
-                    )
-            with st.expander('**Example Queries No Search**', expanded=True):
-                if st.button('write a short poem following a specific pattern: the first letter of every word should spell out the name of a country.'):
-                    setChatInputValue(
-                        'write a short poem following a specific pattern: the first letter of every word should spell out the name of a country.'
-                    )
-                if st.button('Write a python program to find the longest root to leaf path in a tree, and some test cases for it.'):
-                    setChatInputValue('Write a python program to find the longest root to leaf path in a tree, and some test cases for it.')
-
-            st.markdown('**Reset chat**')
-            st.markdown('**Note:** Resetting the chat will clear all interactions history')
-            if st.button('Reset conversation'):
-                st.session_state.chat_history = []
-                st.session_state.sources_history = []
-                if 'related_questions' in st.session_state:
-                    st.session_state.related_questions = []
-                st.toast('Interactions reset. The next response will clear the history on the screen')
-
-        # Add a footer with the GitHub citation
-        footer_html = """
-        <style>
-        .footer {
-            position: fixed;
-            right: 10px;
-            bottom: 10px;
-            width: auto;
-            background-color: transparent;
-            color: grey;
-            text-align: right;
-            padding: 10px;
-            font-size: 16px;
-        }
-        </style>
-        <div class="footer">
-            Inspired by: <a href="https://github.com/leptonai/search_with_lepton" target="_blank">search_with_lepton</a>
-        </div>
-        """
-        st.markdown(footer_html, unsafe_allow_html=True)
-
-    user_question = st.chat_input('Ask something', disabled=st.session_state.input_disabled, key='TheChatInput')
-    handle_userinput(user_question)
-
     
+    pdf_input.change(
+        fn=lambda x: "PDF uploaded. Click 'Convert to Slides' to process." if x else "Upload a PDF and click 'Convert to Slides'",
+        inputs=[pdf_input],
+        outputs=[status]
+    )
+
+# Launch the app
+if __name__ == "__main__":
+    app.launch()
 
-if __name__ == '__main__':
-    main()
+    # Optional: Clean up the temporary directory when the app exits
+    import shutil
+    shutil.rmtree(OUTPUT_FOLDER, ignore_errors=True)