app.py

import time
import os
import multiprocessing
import torch
import requests
import asyncio
import json
import aiohttp
from minivectordb.embedding_model import EmbeddingModel
from minivectordb.vector_database import VectorDatabase
from text_util_en_pt.cleaner import structurize_text, detect_language, Language
from webtextcrawler.webtextcrawler import extract_text_from_url
import gradio as gr
from googlesearch import search

torch.set_num_threads(2)
openrouter_key = os.environ.get("sk-proj-sbgYj5kgnU35y0xjMSEyT3BlbkFJRMigEKIR9YdLqyx4y5bD")
model = EmbeddingModel(use_quantized_onnx_model=True)

def fetch_links(query, max_results=10):
    return list(search(query, num_results=max_results))

def fetch_texts(links):
    with multiprocessing.Pool(10) as pool:
        texts = pool.map(extract_text_from_url, links)
    return '\n'.join([t for t in texts if t])

def index_and_search(query, text):
    start = time.time()
    query_embedding = model.extract_embeddings(query)

    # Indexing
    vector_db = VectorDatabase()
    sentences = [s['sentence'] for s in structurize_text(text)]

    for idx, sentence in enumerate(sentences):
        sentence_embedding = model.extract_embeddings(sentence)
        vector_db.store_embedding(idx + 1, sentence_embedding, {'sentence': sentence})

    embedding_time = time.time() - start

    # Retrieval
    start = time.time()
    search_results = vector_db.find_most_similar(query_embedding, k=30)
    retrieval_time = time.time() - start
    return '\n'.join([s['sentence'] for s in search_results[2]]), embedding_time, retrieval_time

def generate_search_terms(message, lang):
    if lang == Language.ptbr:
        prompt = f"A partir do texto a seguir, gere alguns termos de pesquisa: \"{message}\"\nSua resposta deve ser apenas o termo de busca mais adequado, e nada mais."
    else:
        prompt = f"From the following text, generate some search terms: \"{message}\"\nYour answer should be just the most appropriate search term, and nothing else."

    url = "https://openrouter.ai/api/v1/chat/completions"
    headers = {
        "Content-Type": "application/json",
        "Authorization": f"Bearer {openrouter_key}"
    }
    body = {
        "stream": False,
        "models": [
            "mistralai/mistral-7b-instruct:free",
            "openchat/openchat-7b:free"
        ],
        "route": "fallback",
        "max_tokens": 1024,
        "messages": [
            {"role": "user", "content": prompt}
        ]
    }

    response = requests.post(url, headers=headers, json=body)
    response_json = response.json()
    try:
        return response_json['choices'][0]['message']['content']
    except KeyError:
        print(f"Error: 'choices' key not found in the response. Response: {response_json}")
        return None

async def predict(message, history):
    full_response = ""

    query_language = detect_language(message)

    start = time.time()
    full_response += "Generating search terms...\n"
    yield full_response
    search_query = generate_search_terms(message, query_language)
    search_terms_time = time.time() - start

    full_response += f"Search terms: \"{search_query}\"\n"
    yield full_response
    full_response += f"Search terms took: {search_terms_time:.4f} seconds\n"
    yield full_response

    start = time.time()
    full_response += "\nSearching the web...\n"
    yield full_response
    links = fetch_links(search_query)
    websearch_time = time.time() - start

    full_response += f"Web search took: {websearch_time:.4f} seconds\n"
    yield full_response

    full_response += f"Links visited:\n"
    yield full_response
    for link in links:
        full_response += f"{link}\n"
        yield full_response

    full_response += "\nExtracting text from web pages...\n"
    yield full_response
    start = time.time()
    text = fetch_texts(links)
    webcrawl_time = time.time() - start

    full_response += f"Text extraction took: {webcrawl_time:.4f} seconds\n"

    full_response += "\nIndexing in vector database and building prompt...\n"
    yield full_response

    context, embedding_time, retrieval_time = index_and_search(message, text)

    if query_language == Language.ptbr:
        prompt = f"Contexto:\n{context}\n\nResponda: \"{message}\"\n(Você pode utilizar o contexto para responder)\n(Sua resposta deve ser completa, detalhada e bem estruturada)"
    else:
        prompt = f"Context:\n{context}\n\nAnswer: \"{message}\"\n(You can use the context to answer)\n(Your answer should be complete, detailed and well-structured)"

    full_response += f"Embedding time: {embedding_time:.4f} seconds\n"
    full_response += f"Retrieval from VectorDB time: {retrieval_time:.4f} seconds\n"
    yield full_response

    full_response += "\nGenerating response...\n"
    yield full_response

    full_response += "\nResponse: "

    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}
        ]
    }

    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

gr.ChatInterface(
    predict,
    title="Live Web Chat",
    description="",
    retry_btn=None,
    undo_btn=None,
    examples=[
        'What is the current sentiment of the Brazil election?',
        'Compare the current economies of China and India?',
        'What are new shoe design trends in 2024',
    ]
).launch()