Update app.py

app.py

@@ -19,274 +19,41 @@ from transformers import AutoModel, AutoTokenizer
 # 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 = 'y0un92/test'
+# test.py
+import torch
+from PIL import Image
+from transformers import AutoModel, AutoTokenizer
 
-# Load model directly
-from transformers import AutoModel
-model = AutoModel.from_pretrained("y0un92/test", trust_remote_code=True)
-tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+model = AutoModel.from_pretrained('openbmb/MiniCPM-Llama3-V-2_5-int4', trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-Llama3-V-2_5-int4', trust_remote_code=True)
 model.eval()
 
-
-
-ERROR_MSG = "Error, please retry"
-model_name = 'MiniCPM-Llama3-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
-        )
-
-@spaces.GPU(duration=120)
-def chat(img, msgs, ctx, params=None, vision_hidden_states=None):
-    default_params = {"stream": False, "sampling": False, "num_beams":3, "repetition_penalty": 1.2, "max_new_tokens": 1024}
-    if params is None:
-        params = default_params
-    if img is None:
-        yield "Error, invalid image, please upload a new image"
-    else:
-        try:
-            image = img.convert('RGB')
-            answer = model.chat(
-                image=image,
-                msgs=msgs,
-                tokenizer=tokenizer,
-                **params
-            )
-            # if params['stream'] is False:
-                # res = re.sub(r'(<box>.*</box>)', '', answer)
-                # res = res.replace('<ref>', '')
-                # res = res.replace('</ref>', '')
-                # res = res.replace('<box>', '')
-                # answer = res.replace('</box>', '')
-            # else:
-            for char in answer:
-                yield char
-        except Exception as err:
-            print(err)
-            traceback.print_exc()
-            yield ERROR_MSG
-
-
-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(_chat_bot, _app_cfg, params_form, num_beams, repetition_penalty, repetition_penalty_2, top_p, top_k, temperature):
-    _question = _chat_bot[-1][0]
-    print('<Question>:', _question)
-    if _app_cfg.get('ctx', None) is None:
-        _chat_bot[-1][1] = 'Please upload an image to start'
-        yield (_chat_bot, _app_cfg)
-    else:
-        _context = _app_cfg['ctx'].copy()
-        if _context:
-            _context.append({"role": "user", "content": _question})
-        else:
-            _context = [{"role": "user", "content": _question}]
-        if params_form == 'Beam Search':
-            params = {
-                'sampling': False,
-                'stream': False,
-                'num_beams': num_beams,
-                'repetition_penalty': repetition_penalty,
-                "max_new_tokens": 896 
-            }
-        else:
-            params = {
-                'sampling': True,
-                'stream': True,
-                'top_p': top_p,
-                'top_k': top_k,
-                'temperature': temperature,
-                'repetition_penalty': repetition_penalty_2,
-                "max_new_tokens": 896 
-            }
-    
-        gen = chat(_app_cfg['img'], _context, None, params)
-        _chat_bot[-1][1] = ""
-        for _char in gen:
-            _chat_bot[-1][1] += _char
-            _context[-1]["content"] += _char
-            yield (_chat_bot, _app_cfg)
-
-
-def request(_question, _chat_bot, _app_cfg):
-    _chat_bot.append((_question, None))
-    return '', _chat_bot, _app_cfg
-
-
-def regenerate_button_clicked(_question, _chat_bot, _app_cfg):
-    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 request(_question, _chat_bot, _app_cfg)
-    # return respond(_chat_bot, _app_cfg, params_form, num_beams, repetition_penalty, repetition_penalty_2, top_p, top_k, temperature)
-
-
-def clear_button_clicked(_question, _chat_bot, _app_cfg, _bt_pic):
-    _chat_bot.clear()
-    _app_cfg['sts'] = None
-    _app_cfg['ctx'] = None
-    _app_cfg['img'] = None
-    _bt_pic = None
-    return '', _chat_bot, _app_cfg, _bt_pic
-    
-
-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')
-            clear = create_component({'value': 'Clear'}, 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")
-            
-            clear.click(
-                clear_button_clicked,
-                [txt_message, chat_bot, app_session, bt_pic],
-                [txt_message, chat_bot, app_session, bt_pic],
-                queue=False
-            )
-            txt_message.submit(
-                request, 
-                #[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, chat_bot, app_session],
-                queue=False
-            ).then(
-                respond,
-                [chat_bot, app_session, params_form, num_beams, repetition_penalty, repetition_penalty_2, top_p, top_k, temperature],
-                [chat_bot, app_session]
-            )
-            regenerate.click(
-                regenerate_button_clicked,
-                [txt_message, chat_bot, app_session],
-                [txt_message, chat_bot, app_session],
-                queue=False
-            ).then(
-                respond,
-                [chat_bot, app_session, params_form, num_beams, repetition_penalty, repetition_penalty_2, top_p, top_k, temperature],
-                [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")
-demo.queue()
-demo.launch()
+image = Image.open('xx.jpg').convert('RGB')
+question = 'What is in the image?'
+msgs = [{'role': 'user', 'content': question}]
+
+res = model.chat(
+    image=image,
+    msgs=msgs,
+    tokenizer=tokenizer,
+    sampling=True, # if sampling=False, beam_search will be used by default
+    temperature=0.7,
+    # system_prompt='' # pass system_prompt if needed
+)
+print(res)
+
+## if you want to use streaming, please make sure sampling=True and stream=True
+## the model.chat will return a generator
+res = model.chat(
+    image=image,
+    msgs=msgs,
+    tokenizer=tokenizer,
+    sampling=True,
+    temperature=0.7,
+    stream=True
+)
+
+generated_text = ""
+for new_text in res:
+    generated_text += new_text
+    print(new_text, flush=True, end='')