Update app.py

app.py

@@ -1,439 +1,46 @@
-# --------------------------------------------------------
-# InternVL
-# Copyright (c) 2024 OpenGVLab
-# Licensed under The MIT License [see LICENSE for details]
-# --------------------------------------------------------
-
-import argparse
-import base64
-import datetime
-import hashlib
-import json
-import os
-import random
-import re
+import subprocess
 import sys
-# from streamlit_js_eval import streamlit_js_eval
-from functools import partial
-from io import BytesIO
-
-import cv2
-import numpy as np
-import requests
-import streamlit as st
-from constants import LOGDIR, server_error_msg
-from library import Library
-from PIL import Image, ImageDraw, ImageFont
-from streamlit_image_select import image_select
-
-custom_args = sys.argv[1:]
-parser = argparse.ArgumentParser()
-parser.add_argument('--controller_url', type=str, default='http://10.140.60.209:10075', help='url of the controller')
-parser.add_argument('--sd_worker_url', type=str, default='http://0.0.0.0:40006', help='url of the stable diffusion worker')
-parser.add_argument('--max_image_limit', type=int, default=4, help='maximum number of images')
-args = parser.parse_args(custom_args)
-controller_url = args.controller_url
-sd_worker_url = args.sd_worker_url
-max_image_limit = args.max_image_limit
-print('args:', args)
-
-
-def get_model_list():
-    ret = requests.post(controller_url + '/refresh_all_workers')
-    assert ret.status_code == 200
-    ret = requests.post(controller_url + '/list_models')
-    models = ret.json()['models']
-    return models
-
-
-def load_upload_file_and_show():
-    if uploaded_files is not None:
-        images = []
-        for file in uploaded_files:
-            file_bytes = np.asarray(bytearray(file.read()), dtype=np.uint8)
-            img = cv2.imdecode(file_bytes, cv2.IMREAD_COLOR)
-            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-            img = Image.fromarray(img)
-            images.append(img)
-        with upload_image_preview.container():
-            Library(images)
-
-        image_hashes = [hashlib.md5(image.tobytes()).hexdigest() for image in images]
-        for image, hash in zip(images, image_hashes):
-            t = datetime.datetime.now()
-            filename = os.path.join(LOGDIR, 'serve_images', f'{t.year}-{t.month:02d}-{t.day:02d}', f'{hash}.jpg')
-            if not os.path.isfile(filename):
-                os.makedirs(os.path.dirname(filename), exist_ok=True)
-                image.save(filename)
-    return images
-
-
-def get_selected_worker_ip():
-    ret = requests.post(controller_url + '/get_worker_address',
-            json={'model': selected_model})
-    worker_addr = ret.json()['address']
-    return worker_addr
-
-
-def generate_response(messages):
-    send_messages = [{'role': 'system', 'content': persona_rec}]
-    for message in messages:
-        if message['role'] == 'user':
-            user_message = {'role': 'user', 'content': message['content']}
-            if 'image' in message and len('image') > 0:
-                user_message['image'] = []
-                for image in message['image']:
-                    user_message['image'].append(pil_image_to_base64(image))
-            send_messages.append(user_message)
-        else:
-            send_messages.append({'role': 'assistant', 'content': message['content']})
-    pload = {
-        'model': selected_model,
-        'prompt': send_messages,
-        'temperature': float(temperature),
-        'top_p': float(top_p),
-        'max_new_tokens': max_length,
-        'max_input_tiles': max_input_tiles,
-        'repetition_penalty': float(repetition_penalty),
-    }
-    worker_addr = get_selected_worker_ip()
-    headers = {'User-Agent': 'InternVL-Chat Client'}
-    placeholder, output = st.empty(), ''
-    try:
-        response = requests.post(worker_addr + '/worker_generate_stream',
-                                 headers=headers, json=pload, stream=True, timeout=10)
-        for chunk in response.iter_lines(decode_unicode=False, delimiter=b'\0'):
-            if chunk:
-                data = json.loads(chunk.decode())
-                if data['error_code'] == 0:
-                    output = data['text']
-                    # Phi3-3.8B will produce abnormal `�` output
-                    if '4B' in selected_model and '�' in output[-2:]:
-                        output = output.replace('�', '')
-                        break
-                    placeholder.markdown(output + '▌')
-                else:
-                    output = data['text'] + f" (error_code: {data['error_code']})"
-                    placeholder.markdown(output)
-        placeholder.markdown(output)
-    except requests.exceptions.RequestException as e:
-        placeholder.markdown(server_error_msg)
-    return output
-
-
-def pil_image_to_base64(image):
-    buffered = BytesIO()
-    image.save(buffered, format='PNG')
-    return base64.b64encode(buffered.getvalue()).decode('utf-8')
-
-
-def clear_chat_history():
-    st.session_state.messages = []
-    st.session_state['image_select'] = -1
-
-
-def clear_file_uploader():
-    st.session_state.uploader_key += 1
-    st.rerun()
-
-
-def combined_func(func_list):
-    for func in func_list:
-        func()
-
-
-def show_one_or_multiple_images(message, total_image_num, is_input=True):
-    if 'image' in message:
-        if is_input:
-            total_image_num = total_image_num + len(message['image'])
-            if lan == 'English':
-                if len(message['image']) == 1 and total_image_num == 1:
-                    label = f"(In this conversation, {len(message['image'])} image was uploaded, {total_image_num} image in total)"
-                elif len(message['image']) == 1 and total_image_num > 1:
-                    label = f"(In this conversation, {len(message['image'])} image was uploaded, {total_image_num} images in total)"
-                else:
-                    label = f"(In this conversation, {len(message['image'])} images were uploaded, {total_image_num} images in total)"
-            else:
-                label = f"(在本次对话中，上传了{len(message['image'])}张图片，总共上传了{total_image_num}张图片)"
-        upload_image_preview = st.empty()
-        with upload_image_preview.container():
-            Library(message['image'])
-        if is_input and len(message['image']) > 0:
-            st.markdown(label)
-
-
-def find_bounding_boxes(response):
-    pattern = re.compile(r'<ref>\s*(.*?)\s*</ref>\s*<box>\s*(\[\[.*?\]\])\s*</box>')
-    matches = pattern.findall(response)
-    results = []
-    for match in matches:
-        results.append((match[0], eval(match[1])))
-    returned_image = None
-    for message in st.session_state.messages:
-        if message['role'] == 'user' and 'image' in message and len(message['image']) > 0:
-            last_image = message['image'][-1]
-            width, height = last_image.size
-            returned_image = last_image.copy()
-            draw = ImageDraw.Draw(returned_image)
-    for result in results:
-        line_width = max(1, int(min(width, height) / 200))
-        random_color = (random.randint(0, 128), random.randint(0, 128), random.randint(0, 128))
-        category_name, coordinates = result
-        coordinates = [(float(x[0]) / 1000, float(x[1]) / 1000, float(x[2]) / 1000, float(x[3]) / 1000) for x in coordinates]
-        coordinates = [(int(x[0] * width), int(x[1] * height), int(x[2] * width), int(x[3] * height)) for x in coordinates]
-        for box in coordinates:
-            draw.rectangle(box, outline=random_color, width=line_width)
-            font = ImageFont.truetype('static/SimHei.ttf', int(20 * line_width / 2))
-            text_size = font.getbbox(category_name)
-            text_width, text_height = text_size[2] - text_size[0], text_size[3] - text_size[1]
-            text_position = (box[0], max(0, box[1] - text_height))
-            draw.rectangle(
-                [text_position, (text_position[0] + text_width, text_position[1] + text_height)],
-                fill=random_color
-            )
-            draw.text(text_position, category_name, fill='white', font=font)
-    return returned_image if len(matches) > 0 else None
-
-
-def query_image_generation(response, sd_worker_url, timeout=15):
-    sd_worker_url = f'{sd_worker_url}/generate_image/'
-    pattern = r'```drawing-instruction\n(.*?)\n```'
-    match = re.search(pattern, response, re.DOTALL)
-    if match:
-        payload = {'caption': match.group(1)}
-        print('drawing-instruction:', payload)
-        response = requests.post(sd_worker_url, json=payload, timeout=timeout)
-        response.raise_for_status()  # 检查HTTP请求是否成功
-        image = Image.open(BytesIO(response.content))
-        return image
-    else:
-        return None
-
-
-def regenerate():
-    st.session_state.messages = st.session_state.messages[:-1]
-    st.rerun()
-
-
-logo_code = """
-<svg width="1700" height="200" xmlns="http://www.w3.org/2000/svg">
-  <defs>
-    <linearGradient id="gradient1" x1="0%" y1="0%" x2="100%" y2="0%">
-      <stop offset="0%" style="stop-color: red; stop-opacity: 1" />
-      <stop offset="100%" style="stop-color: orange; stop-opacity: 1" />
-    </linearGradient>
-  </defs>
-  <text x="000" y="160" font-size="180" font-weight="bold" fill="url(#gradient1)" style="font-family: Arial, sans-serif;">
-    InternVL2 Demo
-  </text>
-</svg>
-"""
-
-# App title
-st.set_page_config(page_title='InternVL2')
-
-if 'uploader_key' not in st.session_state:
-    st.session_state.uploader_key = 0
-
-# 如果用户要求绘图，请以生成符合Stable Diffusion要求的、满足```drawing-instruction\n{instruction}\n```格式的绘图指令。
-system_message = """我是书生·万象，英文名是InternVL，是由上海人工智能实验室、清华大学及多家合作单位联合开发的多模态大语言模型。人工智能实验室致力于原始技术创新，开源开放，共享共创，推动科技进步和产业发展。
-
-对于目标检测任务，请按照以下格式输出坐标框：<ref>某类物体</ref><box>[[x1, y1, x2, y2], ...]</box>
-
-对于画画任务，请按照以下格式输出绘图指令，注意指令需要英文：```drawing-instruction\n{instruction}\n```
-
-在处理输入包含多张图像的情况下，请严格按照以下规则区分和处理每一张图像，并小心区分用户的提问针对的是哪一张图片：
-
-1. 图像编号和标记：每张图像都将使用明确的编号标记，例如 "Image-1: <img></img>"，"Image-2: <img></img>"，"Image-3: <img></img>" 等等。
-
-2. 用户提问关联：用户的提问可能会具体指向某一张编号的图像，请仔细辨别用户问题中提到的图像编号。
-
-请尽可能详细地回答用户的问题。"""
-
-# Replicate Credentials
-with st.sidebar:
-    model_list = get_model_list()
-    # "[![Open in GitHub](https://github.com/codespaces/badge.svg)](https://github.com/OpenGVLab/InternVL)"
-    lan = st.selectbox('#### Language / 语言', ['English', '中文'], on_change=st.rerun)
-    if lan == 'English':
-        st.logo(logo_code, link='https://github.com/OpenGVLab/InternVL', icon_image=logo_code)
-        st.subheader('Models and parameters')
-        selected_model = st.sidebar.selectbox('Choose a InternVL2 chat model', model_list, key='selected_model', on_change=clear_chat_history)
-        with st.expander('🤖 System Prompt'):
-            persona_rec = st.text_area('System Prompt', value=system_message,
-                                       help='System prompt is a pre-defined message used to instruct the assistant at the beginning of a conversation.',
-                                       height=200)
-        with st.expander('🔥 Advanced Options'):
-            temperature = st.slider('temperature', min_value=0.0, max_value=1.0, value=0.8, step=0.1)
-            top_p = st.slider('top_p', min_value=0.0, max_value=1.0, value=0.7, step=0.1)
-            repetition_penalty = st.slider('repetition_penalty', min_value=1.0, max_value=1.5, value=1.1, step=0.02)
-            max_length = st.slider('max_length', min_value=0, max_value=4096, value=2048, step=128)
-            max_input_tiles = st.slider('max_input_tiles (control image resolution)', min_value=1, max_value=24, value=12, step=1)
-        upload_image_preview = st.empty()
-        uploaded_files = st.file_uploader('Upload files', accept_multiple_files=True,
-                                         type=['png', 'jpg', 'jpeg', 'webp'],
-                                         help='You can upload multiple images (max to 4) or a single video.',
-                                         key=f'uploader_{st.session_state.uploader_key}',
-                                         on_change=st.rerun)
-        uploaded_pil_images = load_upload_file_and_show()
-    else:
-        st.subheader('模型和参数')
-        selected_model = st.sidebar.selectbox('选择一个 InternVL2 对话模型', model_list, key='selected_model', on_change=clear_chat_history)
-        with st.expander('🤖 系统提示'):
-            persona_rec = st.text_area('系统提示', value=system_message,
-                                       help='系统提示是在对话开始时用于指示助手的预定义消息。',
-                                       height=200)
-        with st.expander('🔥 高级选项'):
-            temperature = st.slider('temperature', min_value=0.0, max_value=1.0, value=0.8, step=0.1)
-            top_p = st.slider('top_p', min_value=0.0, max_value=1.0, value=0.7, step=0.1)
-            repetition_penalty = st.slider('重复惩罚', min_value=1.0, max_value=1.5, value=1.1, step=0.02)
-            max_length = st.slider('最大输出长度', min_value=0, max_value=4096, value=2048, step=128)
-            max_input_tiles = st.slider('最大图像块数 (控制图像分辨率)', min_value=1, max_value=24, value=12, step=1)
-        upload_image_preview = st.empty()
-        uploaded_files = st.file_uploader('上传文件', accept_multiple_files=True,
-                                         type=['png', 'jpg', 'jpeg', 'webp'],
-                                         help='你可以上传多张图像（最多4张）或者一个视频。',
-                                         key=f'uploader_{st.session_state.uploader_key}',
-                                         on_change=st.rerun)
-        uploaded_pil_images = load_upload_file_and_show()
-
-
-gradient_text_html = """
-<style>
-.gradient-text {
-    font-weight: bold;
-    background: -webkit-linear-gradient(left, red, orange);
-    background: linear-gradient(to right, red, orange);
-    -webkit-background-clip: text;
-    -webkit-text-fill-color: transparent;
-    display: inline;
-    font-size: 3em;
-}
-</style>
-<div class="gradient-text">InternVL2</div>
-"""
-if lan == 'English':
-    st.markdown(gradient_text_html, unsafe_allow_html=True)
-    st.caption('Expanding Performance Boundaries of Open-Source Multimodal Large Language Models')
-else:
-    st.markdown(gradient_text_html.replace('InternVL2', '书生·万象'), unsafe_allow_html=True)
-    st.caption('扩展开源多模态大语言模型的性能边界')
-
-# Store LLM generated responses
-if 'messages' not in st.session_state.keys():
-    clear_chat_history()
-
-gallery_placeholder = st.empty()
-with gallery_placeholder.container():
-    images = ['gallery/prod_en_17.png', 'gallery/astro_on_unicorn.png',
-              'gallery/prod_12.png', 'gallery/prod_9.jpg',
-              'gallery/prod_4.png', 'gallery/cheetah.png', 'gallery/prod_1.jpeg']
-    images = [Image.open(image) for image in images]
-    if lan == 'English':
-        captions = ["I'm on a diet, but I really want to eat them.",
-                    'Could you help me draw a picture like this one?',
-                    'What are the consequences of the easy decisions shown in this image?',
-                    "What's at the far end of the image?",
-                    'Is this a real plant? Analyze the reasons.',
-                    'Detect the <ref>the middle leopard</ref> in the image with its bounding box.',
-                    'What is the atmosphere of this image?']
-    else:
-        captions = ['我在减肥，但我真的很想吃这个。',
-                    '请画一张类似这样的画',
-                    '这张图上 easy decisions 导致了什么后果?',
-                    '画面最远处是什么?',
-                    '这是真的植物吗？分析原因',
-                    '在以下图像中进行目标检测，并标出所有物体。',
-                    '这幅图的氛围如何？']
-    img_idx = image_select(
-        label='',
-        images=images,
-        captions=captions,
-        use_container_width=True,
-        index=-1,
-        return_value='index',
-        key='image_select'
-    )
-    if lan == 'English':
-        st.caption('Note: For non-commercial research use only. AI responses may contain errors. Users should not spread or allow others to spread hate speech, violence, pornography, or fraud-related harmful information.')
-    else:
-        st.caption('注意：仅限非商业研究使用。用户应不传播或允许他人传播仇恨言论、暴力、色情内容或与欺诈相关的有害信息。')
-    if img_idx != -1 and len(st.session_state.messages) == 0 and selected_model is not None:
-        gallery_placeholder.empty()
-        st.session_state.messages.append({'role': 'user', 'content': captions[img_idx], 'image': [images[img_idx]]})
-        st.rerun() # Fixed an issue where examples were not emptied
-
-if len(st.session_state.messages) > 0:
-    gallery_placeholder.empty()
-
-# Display or clear chat messages
-total_image_num = 0
-for message in st.session_state.messages:
-    with st.chat_message(message['role']):
-        st.markdown(message['content'])
-        show_one_or_multiple_images(message, total_image_num, is_input=message['role'] == 'user')
-        if 'image' in message and message['role'] == 'user':
-            total_image_num += len(message['image'])
-
-input_disable_flag = (len(model_list) == 0) or total_image_num + len(uploaded_files) > max_image_limit
-if lan == 'English':
-    st.sidebar.button('Clear Chat History', on_click=partial(combined_func, func_list=[clear_chat_history, clear_file_uploader]))
-    if input_disable_flag:
-        prompt = st.chat_input('Too many images have been uploaded. Please clear the history.', disabled=input_disable_flag)
-    else:
-        prompt = st.chat_input('Send messages to InternVL', disabled=input_disable_flag)
-else:
-    st.sidebar.button('清空聊天记录', on_click=partial(combined_func, func_list=[clear_chat_history, clear_file_uploader]))
-    if input_disable_flag:
-        prompt = st.chat_input('输入的图片太多了，请清空历史记录。', disabled=input_disable_flag)
-    else:
-        prompt = st.chat_input('给 “InternVL” 发送消息', disabled=input_disable_flag)
-
-alias_instructions = {
-    '目标检测': '在以下图像中进行目标检测，并标出所有物体。',
-    '检测': '在以下图像中进行目标检测，并标出所有物体。',
-    'object detection': 'Please identify and label all objects in the following image.',
-    'detection': 'Please identify and label all objects in the following image.'
-}
-
-if prompt:
-    prompt = alias_instructions[prompt] if prompt in alias_instructions else prompt
-    gallery_placeholder.empty()
-    image_list = uploaded_pil_images
-    st.session_state.messages.append({'role': 'user', 'content': prompt, 'image': image_list})
-    with st.chat_message('user'):
-        st.write(prompt)
-        show_one_or_multiple_images(st.session_state.messages[-1], total_image_num, is_input=True)
-    if image_list:
-        clear_file_uploader()
-
-# Generate a new response if last message is not from assistant
-if len(st.session_state.messages) > 0 and st.session_state.messages[-1]['role'] != 'assistant':
-    with st.chat_message('assistant'):
-        with st.spinner('Thinking...'):
-            if not prompt:
-                prompt = st.session_state.messages[-1]['content']
-            response = generate_response(st.session_state.messages)
-            message = {'role': 'assistant', 'content': response}
-        with st.spinner('Drawing...'):
-            if '<ref>' in response:
-                has_returned_image = find_bounding_boxes(response)
-                message['image'] = [has_returned_image] if has_returned_image else []
-            if '```drawing-instruction' in response:
-                has_returned_image = query_image_generation(response, sd_worker_url=sd_worker_url)
-                message['image'] = [has_returned_image] if has_returned_image else []
-            st.session_state.messages.append(message)
-            show_one_or_multiple_images(message, total_image_num, is_input=False)
-
-if len(st.session_state.messages) > 0:
-    col1, col2, col3, col4 = st.columns([1, 1, 1, 1.3])
-    text1 = 'Clear Chat History' if lan == 'English' else '清空聊天记录'
-    text2 = 'Regenerate' if lan == 'English' else '重新生成'
-    text3 = 'Copy' if lan == 'English' else '复制回答'
-    with col1:
-        st.button(text1, on_click=partial(combined_func, func_list=[clear_chat_history, clear_file_uploader]),
-                  key='clear_chat_history_button')
-    with col2:
-        st.button(text2, on_click=regenerate, key='regenerate_button')
 
-print(st.session_state.messages)
+# Function to install a package using pip
+def install_package(package):
+    subprocess.check_call([sys.executable, "-m", "pip", "install", package])
+
+# Install Flask
+install_package("flask")
+
+from flask import Flask, render_template_string
+
+app = Flask(__name__)
+
+@app.route('/')
+def index():
+    return render_template_string('''
+        <!DOCTYPE html>
+        <html lang="en">
+        <head>
+            <meta charset="UTF-8">
+            <meta name="viewport" content="width=device-width, initial-scale=1.0">
+            <title>Embed Webpage</title>
+            <style>
+                body, html {
+                    margin: 0;
+                    padding: 0;
+                    height: 100%;
+                    overflow: hidden;
+                }
+                iframe {
+                    position: absolute;
+                    width: 100%;
+                    height: 100%;
+                    border: none;
+                }
+            </style>
+        </head>
+        <body>
+            <iframe src="http://101.132.98.120:10003/"></iframe>
+        </body>
+        </html>
+    ''')
+
+if __name__ == '__main__':
+    app.run(debug=True)