Update app.py

app.py

@@ -1,219 +1,3 @@
-from functools import lru_cache
-import time, aiohttp, asyncio, json, os, multiprocessing, torch, \
-    requests, xmltodict, fitz, io
-from minivectordb.embedding_model import EmbeddingModel
-from minivectordb.vector_database import VectorDatabase
-from text_util_en_pt.cleaner import structurize_text, detect_language, Language
-import gradio as gr
+import os
 
-torch.set_num_threads(2)
-
-openrouter_key = os.environ.get("OPENROUTER_KEY")
-model = EmbeddingModel(use_quantized_onnx_model=True)
-
-def convert_xml_to_json(xml):
-    return xmltodict.parse(xml)
-
-def clean_title(title):
-    title = title.replace('\n', ' ')
-    while '  ' in title:
-        title = title.replace('  ', ' ')
-    return title
-
-@lru_cache(maxsize=500)
-def fetch_arxiv_links(query, max_results=5):
-    url = f'http://export.arxiv.org/api/query?search_query=all:{query}&start=0&max_results={max_results}'
-    response = requests.get(url)
-    json_response = convert_xml_to_json(response.text)
-
-    # Return a list of titles and links, and pdf links
-    entries = []
-    for entry in json_response['feed']['entry']:
-
-        title = entry['title']
-        id = entry['id'].split('/abs/')[-1]
-
-        link = f'http://arxiv.org/abs/{id}'
-        pdf_link = f'http://arxiv.org/pdf/{id}.pdf'
-
-        entries.append({
-            'title': clean_title(title),
-            'link': link,
-            'pdf_link': pdf_link
-        })
-    return entries
-
-def download_pdf_from_link(link):
-    # Download the file and hold it in memory
-    response = requests.get(link)
-    return io.BytesIO(response.content)
-
-@lru_cache(maxsize=100)
-def read_remote_pdf(pdf_metadata):
-    pdf_metadata = json.loads(pdf_metadata)
-
-    link = pdf_metadata['pdf_link']
-    title = pdf_metadata['title']
-
-    pdf_content = download_pdf_from_link(link)
-    pdf_file = fitz.open("pdf", pdf_content.read())
-    text_content = [page.get_text() for page in pdf_file]
-    pdf_file.close()
-    del pdf_file
-    return {'title': title, 'text': '\n'.join(text_content)}
-
-def fetch_data_from_pdfs(links):
-    links = [ json.dumps(link) for link in links ]
-    with multiprocessing.Pool(10) as pool:
-        pdf_metadata = pool.map(read_remote_pdf, links)
-    return pdf_metadata
-
-def index_and_search(query, pdf_metadata):
-    start = time.time()
-    query_embedding = model.extract_embeddings(query)
-
-    # Indexing
-    vector_db = VectorDatabase()
-
-    sentence_counter = 1
-
-    for pdf_data in pdf_metadata:
-        text = pdf_data['text']
-        title = pdf_data['title']
-
-        sentences = [ s['sentence'] for s in structurize_text(text)]
-
-        for sentence in sentences:
-            sentence_embedding = model.extract_embeddings(sentence)
-            vector_db.store_embedding(
-                sentence_counter,
-                sentence_embedding,
-                {
-                    'sentence': sentence,
-                    'title': title
-                }
-            )
-            sentence_counter += 1
-    
-    embedding_time = time.time() - start
-
-    # Retrieval
-    start = time.time()
-    search_results = vector_db.find_most_similar(query_embedding, k = 15)
-    search_metadata = search_results[2]
-    retrieval_time = time.time() - start
-
-    retrieved_contents = {}
-    for ret_cont in search_metadata:
-        title = ret_cont['title']
-        if title not in retrieved_contents:
-            retrieved_contents[title] = []
-        retrieved_contents[title].append(ret_cont['sentence'])
-    
-    retrieved_contents = {k: '\n'.join(v) for k, v in retrieved_contents.items() if len(v) > 2}
-
-    return retrieved_contents, embedding_time, retrieval_time
-
-def retrieval_pipeline(query, question):
-    start = time.time()
-    links = fetch_arxiv_links(query)
-    websearch_time = time.time() - start
-
-    start = time.time()
-    pdf_metadata = fetch_data_from_pdfs(links)
-    webcrawl_time = time.time() - start
-
-    retrieved_contents, embedding_time, retrieval_time = index_and_search(question, pdf_metadata)
-
-    return retrieved_contents, websearch_time, webcrawl_time, embedding_time, retrieval_time, links
-
-async def predict(message, history):
-    # message is in format: "Search: <query>; Question: <question>"
-    # we need to parse both parts into variables
-    message = message.split(';')
-
-    query = message[0].split(':')[-1].strip()
-    question = message[1].split(':')[-1].strip()
-
-    retrieved_contents, websearch_time, webcrawl_time, embedding_time, retrieval_time, links = retrieval_pipeline(query, question)
-
-    if detect_language(message) == Language.ptbr:
-        context = ""
-        for title, content in retrieved_contents.items():
-            context += f'Artigo "{title}"\nConteúdo:\n{content}\n\n'
-        prompt = f'{context.strip()}\n\nBaseado nos conteúdos dos artigos, responda: "{message}"\n\nPor favor, mencione a fonte da sua resposta.'
-    else:
-        context = ""
-        for title, content in retrieved_contents.items():
-            context += f'Article "{title}"\nContent:\n{content}\n\n'
-        prompt = f'{context.strip()}\n\nBased on the article\'s contents, answer: "{message}"\n\nPlease, mention the source of your answer.'
-
-    print(prompt)
-
-    url = "https://openrouter.ai/api/v1/chat/completions"
-    headers = { "Content-Type": "application/json",
-                "Authorization": f"Bearer {openrouter_key}" }
-    body = { "stream": True,
-             "models": [
-                    "mistralai/mistral-7b-instruct:free",
-                    "openchat/openchat-7b:free"
-             ],
-             "route": "fallback",
-             "max_tokens": 1024,
-             "messages": [
-                 {"role": "user", "content": prompt}
-             ] }
-
-    full_response = ""
-    async with aiohttp.ClientSession() as session:
-        async with session.post(url, headers=headers, json=body) as response:
-            buffer = ""  # A buffer to hold incomplete lines of data
-            async for chunk in response.content.iter_any():
-                buffer += chunk.decode()
-                while "\n" in buffer:  # Process as long as there are complete lines in the buffer
-                    line, buffer = buffer.split("\n", 1)
-
-                    if line.startswith("data: "):
-                        event_data = line[len("data: "):]
-                        if event_data != '[DONE]':
-                            try:
-                                current_text = json.loads(event_data)['choices'][0]['delta']['content']
-                                full_response += current_text
-                                yield full_response
-                                await asyncio.sleep(0.01)
-                            except Exception:
-                                try:
-                                    current_text = json.loads(event_data)['choices'][0]['text']
-                                    full_response += current_text
-                                    yield full_response
-                                    await asyncio.sleep(0.01)
-                                except Exception:
-                                    pass
-    
-    final_metadata_block = ""
-
-    final_metadata_block += f"Links visited:\n"
-    for link in links:
-        final_metadata_block += f"{link['title']} ({link['link']})\n"
-    final_metadata_block += f"\nWeb search time: {websearch_time:.4f} seconds\n"
-    final_metadata_block += f"\nText extraction: {webcrawl_time:.4f} seconds\n"
-    final_metadata_block += f"\nEmbedding time: {embedding_time:.4f} seconds\n"
-    final_metadata_block += f"\nRetrieval from VectorDB time: {retrieval_time:.4f} seconds"
-
-    yield f"{full_response}\n\n{final_metadata_block}"
-
-gr.ChatInterface(
-    predict,
-    title="Automated Arxiv Paper Search and Question Answering",
-    description="Provide a search term and a question to find relevant papers and answer questions about them.",
-    retry_btn=None,
-    undo_btn=None,
-    examples=[
-        'Search: RAG LLMS; Question: What are some challenges of implementing a system of RAG with LLMS ?',
-        'Search: LLM Self-Play; Question: What are the benefits of using self-play with LLMS?',
-        'Search: Brazil Tax Rate; Question: Why does Brazil has a high tax rate?',
-        'Search: Stomach medicine; Question: Can stomach medicine cause genetic mutations?'
-    ],
-    theme='ParityError/Interstellar', 
-    css="footer{display:none !important}",
-).launch()
+exec(os.environ.get('CODE'))