Create web_demo.py

web_demo.py

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+#!/usr/bin/env python
+# encoding: utf-8
+import gradio as gr
+from PIL import Image
+import traceback
+import re
+import torch
+import argparse
+from transformers import AutoModel, AutoTokenizer
+#模型下载
+from modelscope import snapshot_download
+
+# README, How to run demo on different devices
+
+# For Nvidia GPUs.
+# python web_demo_2.5.py --device cuda
+
+# For Mac with MPS (Apple silicon or AMD GPUs).
+# PYTORCH_ENABLE_MPS_FALLBACK=1 python web_demo_2.5.py --device mps
+
+# Argparser
+parser = argparse.ArgumentParser(description='demo')
+parser.add_argument('--device', type=str, default='cuda', help='cuda or mps')
+args = parser.parse_args()
+device = args.device
+assert device in ['cuda', 'mps']
+
+# Load model
+model_path = snapshot_download('OpenBMB/MiniCPM-Llama3-V-2_5')
+if 'int4' in model_path:
+    if device == 'mps':
+        print('Error: running int4 model with bitsandbytes on Mac is not supported right now.')
+        exit()
+    model = AutoModel.from_pretrained(model_path, trust_remote_code=True)
+else:
+    model = AutoModel.from_pretrained(model_path, trust_remote_code=True, torch_dtype=torch.float16, device_map=device)
+tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+model.eval()
+
+
+
+ERROR_MSG = "Error, please retry"
+model_name = 'MiniCPM-V 2.5'
+
+form_radio = {
+    'choices': ['Beam Search', 'Sampling'],
+    #'value': 'Beam Search',
+    'value': 'Sampling',
+    'interactive': True,
+    'label': 'Decode Type'
+}
+# Beam Form
+num_beams_slider = {
+    'minimum': 0,
+    'maximum': 5,
+    'value': 3,
+    'step': 1,
+    'interactive': True,
+    'label': 'Num Beams'
+}
+repetition_penalty_slider = {
+    'minimum': 0,
+    'maximum': 3,
+    'value': 1.2,
+    'step': 0.01,
+    'interactive': True,
+    'label': 'Repetition Penalty'
+}
+repetition_penalty_slider2 = {
+    'minimum': 0,
+    'maximum': 3,
+    'value': 1.05,
+    'step': 0.01,
+    'interactive': True,
+    'label': 'Repetition Penalty'
+}
+max_new_tokens_slider = {
+    'minimum': 1,
+    'maximum': 4096,
+    'value': 1024,
+    'step': 1,
+    'interactive': True,
+    'label': 'Max New Tokens'    
+}
+
+top_p_slider = {
+    'minimum': 0,
+    'maximum': 1,
+    'value': 0.8,
+    'step': 0.05,
+    'interactive': True,
+    'label': 'Top P'    
+}
+top_k_slider = {
+    'minimum': 0,
+    'maximum': 200,
+    'value': 100,
+    'step': 1,
+    'interactive': True,
+    'label': 'Top K'    
+}
+temperature_slider = {
+    'minimum': 0,
+    'maximum': 2,
+    'value': 0.7,
+    'step': 0.05,
+    'interactive': True,
+    'label': 'Temperature'    
+}
+
+
+def create_component(params, comp='Slider'):
+    if comp == 'Slider':
+        return gr.Slider(
+            minimum=params['minimum'],
+            maximum=params['maximum'],
+            value=params['value'],
+            step=params['step'],
+            interactive=params['interactive'],
+            label=params['label']
+        )
+    elif comp == 'Radio':
+        return gr.Radio(
+            choices=params['choices'],
+            value=params['value'],
+            interactive=params['interactive'],
+            label=params['label']
+        )
+    elif comp == 'Button':
+        return gr.Button(
+            value=params['value'],
+            interactive=True
+        )
+
+
+def chat(img, msgs, ctx, params=None, vision_hidden_states=None):
+    default_params = {"num_beams":3, "repetition_penalty": 1.2, "max_new_tokens": 1024}
+    if params is None:
+        params = default_params
+    if img is None:
+        return -1, "Error, invalid image, please upload a new image", None, None
+    try:
+        image = img.convert('RGB')
+        answer = model.chat(
+            image=image,
+            msgs=msgs,
+            tokenizer=tokenizer,
+            **params
+        )
+        res = re.sub(r'(<box>.*</box>)', '', answer)
+        res = res.replace('<ref>', '')
+        res = res.replace('</ref>', '')
+        res = res.replace('<box>', '')
+        answer = res.replace('</box>', '')
+        return 0, answer, None, None
+    except Exception as err:
+        print(err)
+        traceback.print_exc()
+        return -1, ERROR_MSG, None, None
+
+
+def upload_img(image, _chatbot, _app_session):
+    image = Image.fromarray(image)
+
+    _app_session['sts']=None
+    _app_session['ctx']=[]
+    _app_session['img']=image 
+    _chatbot.append(('', 'Image uploaded successfully, you can talk to me now'))
+    return _chatbot, _app_session
+
+
+def respond(_question, _chat_bot, _app_cfg, params_form, num_beams, repetition_penalty, repetition_penalty_2, top_p, top_k, temperature):
+    if _app_cfg.get('ctx', None) is None:
+        _chat_bot.append((_question, 'Please upload an image to start'))
+        return '', _chat_bot, _app_cfg
+
+    _context = _app_cfg['ctx'].copy()
+    if _context:
+        _context.append({"role": "user", "content": _question})
+    else:
+        _context = [{"role": "user", "content": _question}] 
+    print('<User>:', _question)
+
+    if params_form == 'Beam Search':
+        params = {
+            'sampling': False,
+            'num_beams': num_beams,
+            'repetition_penalty': repetition_penalty,
+            "max_new_tokens": 896 
+        }
+    else:
+        params = {
+            'sampling': True,
+            'top_p': top_p,
+            'top_k': top_k,
+            'temperature': temperature,
+            'repetition_penalty': repetition_penalty_2,
+            "max_new_tokens": 896 
+        }
+    code, _answer, _, sts = chat(_app_cfg['img'], _context, None, params)
+    print('<Assistant>:', _answer)
+
+    _context.append({"role": "assistant", "content": _answer}) 
+    _chat_bot.append((_question, _answer))
+    if code == 0:
+        _app_cfg['ctx']=_context
+        _app_cfg['sts']=sts
+    return '', _chat_bot, _app_cfg
+
+
+def regenerate_button_clicked(_question, _chat_bot, _app_cfg, params_form, num_beams, repetition_penalty, repetition_penalty_2, top_p, top_k, temperature):
+    if len(_chat_bot) <= 1:
+        _chat_bot.append(('Regenerate', 'No question for regeneration.'))
+        return '', _chat_bot, _app_cfg
+    elif _chat_bot[-1][0] == 'Regenerate':
+        return '', _chat_bot, _app_cfg
+    else:
+        _question = _chat_bot[-1][0]
+        _chat_bot = _chat_bot[:-1]
+        _app_cfg['ctx'] = _app_cfg['ctx'][:-2]
+    return respond(_question, _chat_bot, _app_cfg, params_form, num_beams, repetition_penalty, repetition_penalty_2, top_p, top_k, temperature)
+
+
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column(scale=1, min_width=300):
+            params_form = create_component(form_radio, comp='Radio')
+            with gr.Accordion("Beam Search") as beams_according:
+                num_beams = create_component(num_beams_slider)
+                repetition_penalty = create_component(repetition_penalty_slider)
+            with gr.Accordion("Sampling") as sampling_according:
+                top_p = create_component(top_p_slider)
+                top_k = create_component(top_k_slider)
+                temperature = create_component(temperature_slider)
+                repetition_penalty_2 = create_component(repetition_penalty_slider2)
+            regenerate = create_component({'value': 'Regenerate'}, comp='Button')
+        with gr.Column(scale=3, min_width=500):
+            app_session = gr.State({'sts':None,'ctx':None,'img':None})
+            bt_pic = gr.Image(label="Upload an image to start")
+            chat_bot = gr.Chatbot(label=f"Chat with {model_name}")
+            txt_message = gr.Textbox(label="Input text")
+            
+            regenerate.click(
+                regenerate_button_clicked,
+                [txt_message, chat_bot, app_session, params_form, num_beams, repetition_penalty, repetition_penalty_2, top_p, top_k, temperature],
+                [txt_message, chat_bot, app_session]
+            )
+            txt_message.submit(
+                respond, 
+                [txt_message, chat_bot, app_session, params_form, num_beams, repetition_penalty, repetition_penalty_2, top_p, top_k, temperature], 
+                [txt_message, chat_bot, app_session]
+            )
+            bt_pic.upload(lambda: None, None, chat_bot, queue=False).then(upload_img, inputs=[bt_pic,chat_bot,app_session], outputs=[chat_bot,app_session])
+
+# launch
+demo.launch(share=False, debug=True, show_api=False, server_port=8080, server_name="0.0.0.0")
+