Spaces:
Sleeping
Sleeping
File size: 5,418 Bytes
38de785 6f48855 d0a6d13 25bcda8 38de785 846e1c8 d0a6d13 38de785 6f48855 436d21b d0a6d13 436d21b 38de785 d0a6d13 70c3062 d0a6d13 38de785 70c3062 d0a6d13 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 |
import os
import logging
import gradio as gr
from huggingface_hub import hf_hub_download
# Install necessary libraries using os.system
os.system("pip install --upgrade pip")
os.system("pip install llama-cpp-agent huggingface_hub trafilatura beautifulsoup4 requests duckduckgo-search googlesearch-python")
# Attempt to import all required modules
try:
from llama_cpp import Llama
from llama_cpp_agent.providers import LlamaCppPythonProvider
from llama_cpp_agent import LlamaCppAgent, MessagesFormatterType
from llama_cpp_agent.chat_history import BasicChatHistory
from llama_cpp_agent.chat_history.messages import Roles
from llama_cpp_agent.llm_output_settings import (
LlmStructuredOutputSettings,
LlmStructuredOutputType,
)
from llama_cpp_agent.tools import WebSearchTool
from llama_cpp_agent.prompt_templates import web_search_system_prompt, research_system_prompt
from utils import CitingSources
from settings import get_context_by_model, get_messages_formatter_type
except ImportError as e:
raise ImportError(f"Error importing modules: {e}")
# Download the models
hf_hub_download(
repo_id="bartowski/Mistral-7B-Instruct-v0.3-GGUF",
filename="Mistral-7B-Instruct-v0.3-Q6_K.gguf",
local_dir="./models"
)
hf_hub_download(
repo_id="bartowski/Meta-Llama-3-8B-Instruct-GGUF",
filename="Meta-Llama-3-8B-Instruct-Q6_K.gguf",
local_dir="./models"
)
hf_hub_download(
repo_id="TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF",
filename="mixtral-8x7b-instruct-v0.1.Q5_K_M.gguf",
local_dir="./models"
)
# Function to respond to user messages
def respond(message, temperature, top_p, top_k, repeat_penalty):
try:
model = "mixtral-8x7b-instruct-v0.1.Q5_K_M.gguf"
max_tokens = 3000
chat_template = get_messages_formatter_type(model)
llm = Llama(
model_path=f"models/{model}",
flash_attn=True,
n_gpu_layers=81,
n_batch=1024,
n_ctx=get_context_by_model(model),
)
provider = LlamaCppPythonProvider(llm)
logging.info(f"Loaded chat examples: {chat_template}")
search_tool = WebSearchTool(
llm_provider=provider,
message_formatter_type=chat_template,
max_tokens_search_results=12000,
max_tokens_per_summary=2048,
)
web_search_agent = LlamaCppAgent(
provider,
system_prompt=web_search_system_prompt,
predefined_messages_formatter_type=chat_template,
debug_output=True,
)
answer_agent = LlamaCppAgent(
provider,
system_prompt=research_system_prompt,
predefined_messages_formatter_type=chat_template,
debug_output=True,
)
settings = provider.get_provider_default_settings()
settings.stream = False
settings.temperature = temperature
settings.top_k = top_k
settings.top_p = top_p
settings.max_tokens = max_tokens
settings.repeat_penalty = repeat_penalty
output_settings = LlmStructuredOutputSettings.from_functions(
[search_tool.get_tool()]
)
messages = BasicChatHistory()
result = web_search_agent.get_chat_response(
message,
llm_sampling_settings=settings,
structured_output_settings=output_settings,
add_message_to_chat_history=False,
add_response_to_chat_history=False,
print_output=False,
)
outputs = ""
settings.stream = True
response_text = answer_agent.get_chat_response(
f"Write a detailed and complete research document that fulfills the following user request: '{message}', based on the information from the web below.\n\n" +
result[0]["return_value"],
role=Roles.tool,
llm_sampling_settings=settings,
chat_history=messages,
returns_streaming_generator=True,
print_output=False,
)
for text in response_text:
outputs += text
output_settings = LlmStructuredOutputSettings.from_pydantic_models(
[CitingSources], LlmStructuredOutputType.object_instance
)
citing_sources = answer_agent.get_chat_response(
"Cite the sources you used in your response.",
role=Roles.tool,
llm_sampling_settings=settings,
chat_history=messages,
returns_streaming_generator=False,
structured_output_settings=output_settings,
print_output=False,
)
outputs += "\n\nSources:\n"
outputs += "\n".join(citing_sources.sources)
return outputs
except Exception as e:
return f"An error occurred: {e}"
# Gradio interface
demo = gr.Interface(
fn=respond,
inputs=[
gr.Textbox(label="Enter your message:"),
gr.Slider(minimum=0.1, maximum=1.0, value=0.45, step=0.1, label="Temperature"),
gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p"),
gr.Slider(minimum=0, maximum=100, value=40, step=1, label="Top-k"),
gr.Slider(minimum=0.0, maximum=2.0, value=1.1, step=0.1, label="Repetition penalty")
],
outputs="text",
title="Novav2 Web Engine"
)
if __name__ == "__main__":
demo.launch()
|