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import os
import sys
import ctypes
import pathlib
from typing import Optional, List
import enum
from pathlib import Path
import argparse
import gradio as gr
import minigpt4_library
from huggingface_hub import hf_hub_download
model_path = hf_hub_download(repo_id='maknee/minigpt4-13b-ggml', filename='minigpt4-13B-f16.bin', repo_type='dataset')
llm_model_path = hf_hub_download(repo_id='maknee/ggml-vicuna-v0-quantized', filename='ggml-vicuna-13B-v0-q5_k.bin', repo_type='dataset')
title = """<h1 align="center">MiniGPT-4.cpp Demo</h1>"""
description = """<h3>This is the demo of MiniGPT-4 with ggml (cpu only!). Upload your images and start chatting!</h3>"""
article = """<div style='display:flex; gap: 0.25rem; '><a href='https://github.com/Vision-CAIR/MiniGPT-4'><img src='https://img.shields.io/badge/Github-Code-blue'></a></div>"""
global minigpt4_chatbot
minigpt4_chatbot: minigpt4_library.MiniGPT4ChatBot
def user(message, history):
history = history or []
# Append the user's message to the conversation history
history.append([message, ""])
return "", history
def chat(history, limit: int = 1024, temp: float = 0.8, top_k: int = 40, top_p: float = 0.9, repeat_penalty: float = 1.1):
history = history or []
message = history[-1][0]
history[-1][1] = ""
for output in minigpt4_chatbot.generate(
message,
limit = int(limit),
temp = float(temp),
top_k = int(top_k),
top_p = float(top_p),
):
answer = output
history[-1][1] += answer
# stream the response
yield history, history
def clear_state(history, chat_message, image):
history = []
minigpt4_chatbot.reset_chat()
return history, gr.update(value=None, interactive=True), gr.update(placeholder='Upload image first', interactive=False), gr.update(value="Upload & Start Chat", interactive=True)
def upload_image(image, history):
if image is None:
return None, None, gr.update(interactive=True), history
history = []
minigpt4_chatbot.upload_image(image.convert('RGB'))
return gr.update(interactive=False), gr.update(interactive=True, placeholder='Type and press Enter'), gr.update(value="Start Chatting", interactive=False), history
def start():
with gr.Blocks() as demo:
gr.Markdown(title)
gr.Markdown(description)
gr.Markdown(article)
with gr.Row():
with gr.Column(scale=0.5):
image = gr.Image(type="pil")
upload_button = gr.Button(value="Upload & Start Chat", interactive=True, variant="primary")
max_tokens = gr.Slider(1, 1024, label="Max Tokens", step=1, value=128)
temperature = gr.Slider(0.0, 1.0, label="Temperature", step=0.05, value=0.8)
top_p = gr.Slider(0.0, 1.0, label="Top P", step=0.05, value=0.95)
top_k = gr.Slider(0, 100, label="Top K", step=1, value=40)
repeat_penalty = gr.Slider(0.0, 2.0, label="Repetition Penalty", step=0.1, value=1.1)
with gr.Column():
chatbot = gr.Chatbot(label='MiniGPT-4')
message = gr.Textbox(label='User', placeholder='Upload image first', interactive=False)
history = gr.State()
with gr.Row():
submit = gr.Button(value="Send message", variant="secondary").style(full_width=True)
clear = gr.Button(value="Reset", variant="secondary").style(full_width=False)
# stop = gr.Button(value="Stop", variant="secondary").style(full_width=False)
clear.click(clear_state, inputs=[history, image, message], outputs=[history, image, message, upload_button], queue=False)
upload_button.click(upload_image, inputs=[image, history], outputs=[image, message, upload_button, history])
submit_click_event = submit.click(
fn=user, inputs=[message, history], outputs=[message, history], queue=True
).then(
fn=chat, inputs=[history, max_tokens, temperature, top_p, top_k, repeat_penalty], outputs=[chatbot, history], queue=True
)
message_submit_event = message.submit(
fn=user, inputs=[message, history], outputs=[message, history], queue=True
).then(
fn=chat, inputs=[history, max_tokens, temperature, top_p, top_k, repeat_penalty], outputs=[chatbot, history], queue=True
)
# stop.click(fn=None, inputs=None, outputs=None, cancels=[submit_click_event, message_submit_event], queue=False)
demo.launch(enable_queue=True)
minigpt4_chatbot = minigpt4_library.MiniGPT4ChatBot(model_path, llm_model_path, verbosity=minigpt4_library.Verbosity.SILENT)
start()
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