try: import spaces def maybe_spaces_gpu(fn): fn = spaces.GPU(fn) return fn except ModuleNotFoundError: print(f'Cannot import hf `spaces` with `import spaces`.') def maybe_spaces_gpu(fn): return fn import os from gradio.themes import ThemeClass as Theme import numpy as np import argparse import gradio as gr from typing import Any, Iterator from typing import Iterator, List, Optional, Tuple import filelock import glob import json import time from gradio.routes import Request from gradio.utils import SyncToAsyncIterator, async_iteration from gradio.helpers import special_args import anyio from typing import AsyncGenerator, Callable, Literal, Union, cast, Generator from gradio_client.documentation import document, set_documentation_group from gradio.components import Button, Component from gradio.events import Dependency, EventListenerMethod from typing import List, Optional, Union, Dict, Tuple from tqdm.auto import tqdm from huggingface_hub import snapshot_download import inspect from typing import AsyncGenerator, Callable, Literal, Union, cast import anyio from gradio_client import utils as client_utils from gradio_client.documentation import document from gradio.blocks import Blocks from gradio.components import ( Button, Chatbot, Component, Markdown, State, Textbox, get_component_instance, ) from gradio.events import Dependency, on from gradio.helpers import create_examples as Examples # noqa: N812 from gradio.helpers import special_args from gradio.layouts import Accordion, Group, Row from gradio.routes import Request from gradio.themes import ThemeClass as Theme from gradio.utils import SyncToAsyncIterator, async_iteration from .base_demo import register_demo, get_demo_class, BaseDemo from ..configs import ( SYSTEM_PROMPT, MODEL_NAME, MAX_TOKENS, TEMPERATURE, USE_PANEL, CHATBOT_HEIGHT, ) from ..globals import MODEL_ENGINE from .chat_interface import ( CHAT_EXAMPLES, DATETIME_FORMAT, gradio_history_to_conversation_prompt, gradio_history_to_openai_conversations, get_datetime_string, format_conversation, chat_response_stream_multiturn_engine, CustomizedChatInterface, ChatInterfaceDemo ) from .langchain_web_search import ( AnyEnginePipeline, ChatAnyEnginePipeline, create_web_search_engine, ) web_search_llm = None web_search_chat_model = None web_search_engine = None web_search_agent = None @maybe_spaces_gpu def chat_web_search_response_stream_multiturn_engine( message: str, history: List[Tuple[str, str]], temperature: float, max_tokens: int, system_prompt: Optional[str] = SYSTEM_PROMPT, ): # global web_search_engine, web_search_llm, web_search_chat_model, web_search_agent, MODEL_ENGINE global web_search_llm, web_search_chat_model, agent_executor, MODEL_ENGINE temperature = float(temperature) # ! remove frequency_penalty # frequency_penalty = float(frequency_penalty) max_tokens = int(max_tokens) message = message.strip() if len(message) == 0: raise gr.Error("The message cannot be empty!") response_output = agent_executor.invoke({"input": message}) print(response_output) response = response_output['output'] full_prompt = gradio_history_to_conversation_prompt(message.strip(), history=history, system_prompt=system_prompt) num_tokens = len(MODEL_ENGINE.tokenizer.encode(full_prompt)) yield response, num_tokens # # ! skip safety # if DATETIME_FORMAT in system_prompt: # # ! This sometime works sometimes dont # system_prompt = system_prompt.format(cur_datetime=get_datetime_string()) # full_prompt = gradio_history_to_conversation_prompt(message.strip(), history=history, system_prompt=system_prompt) # # ! length checked # num_tokens = len(MODEL_ENGINE.tokenizer.encode(full_prompt)) # if num_tokens >= MODEL_ENGINE.max_position_embeddings - 128: # raise gr.Error(f"Conversation or prompt is too long ({num_tokens} toks), please clear the chatbox or try shorter input.") # print(full_prompt) # outputs = None # response = None # num_tokens = -1 # for j, outputs in enumerate(MODEL_ENGINE.generate_yield_string( # prompt=full_prompt, # temperature=temperature, # max_tokens=max_tokens, # )): # if isinstance(outputs, tuple): # response, num_tokens = outputs # else: # response, num_tokens = outputs, -1 # yield response, num_tokens # print(format_conversation(history + [[message, response]])) # if response is not None: # yield response, num_tokens @register_demo class WebSearchChatInterfaceDemo(BaseDemo): @property def tab_name(self): return "Web Search" def create_demo( self, title: str | None = None, description: str | None = None, **kwargs ) -> gr.Blocks: global web_search_llm, web_search_chat_model, agent_executor system_prompt = kwargs.get("system_prompt", SYSTEM_PROMPT) max_tokens = kwargs.get("max_tokens", MAX_TOKENS) temperature = kwargs.get("temperature", TEMPERATURE) model_name = kwargs.get("model_name", MODEL_NAME) # frequence_penalty = FREQUENCE_PENALTY # presence_penalty = PRESENCE_PENALTY # create_web_search_engine() description = description or "At the moment, Web search is only **SINGLE TURN**, only works well in **English** and may respond unnaturally!" web_search_llm, web_search_chat_model, agent_executor = create_web_search_engine() demo_chat = CustomizedChatInterface( chat_web_search_response_stream_multiturn_engine, chatbot=gr.Chatbot( label=model_name, bubble_full_width=False, latex_delimiters=[ { "left": "$", "right": "$", "display": False}, { "left": "$$", "right": "$$", "display": True}, ], show_copy_button=True, layout="panel" if USE_PANEL else "bubble", height=CHATBOT_HEIGHT, ), textbox=gr.Textbox(placeholder='Type message', lines=1, max_lines=128, min_width=200, scale=8), submit_btn=gr.Button(value='Submit', variant="primary", scale=0), title=title, description=description, additional_inputs=[ gr.Number(value=temperature, label='Temperature (higher -> more random)'), gr.Number(value=max_tokens, label='Max generated tokens (increase if want more generation)'), # gr.Number(value=frequence_penalty, label='Frequency penalty (> 0 encourage new tokens over repeated tokens)'), # gr.Number(value=presence_penalty, label='Presence penalty (> 0 encourage new tokens, < 0 encourage existing tokens)'), gr.Textbox(value=system_prompt, label='System prompt', lines=4, interactive=False) ], examples=[ ["What is Langchain?"], ["Give me latest news about Lawrence Wong."], ['What did Jerome Powell say today?'], ['What is the best model on the LMSys leaderboard?'], ['Where does Messi play right now?'], ], # ] + CHAT_EXAMPLES, cache_examples=False ) return demo_chat """ run export BACKEND=mlx export DEMOS=WebSearchChatInterfaceDemo python app.py """