Delete app.py

app.py

@@ -1,141 +0,0 @@
-#!/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
-
-# Argparser
-parser = argparse.ArgumentParser(description='demo')
-parser.add_argument('--device', type=str, default='cpu', help='cpu')
-parser.add_argument('--dtype', type=str, default='fp32', help='fp32')
-args = parser.parse_args()
-device = args.device
-assert device in ['cpu']
-
-# Set dtype
-if args.dtype == 'fp32':
-    dtype = torch.float32
-else:
-    dtype = torch.float16
-
-# Load model
-model_path = 'openbmb/MiniCPM-V-2'
-model = AutoModel.from_pretrained(model_path, trust_remote_code=True).to(dtype=dtype)
-tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
-
-model = model.to(device=device)
-model.eval()
-
-ERROR_MSG = "Error, please retry"
-model_name = 'MiniCPM-V 2.0'
-
-# UI Components
-form_radio = {
-    'choices': ['Beam Search', 'Sampling'],
-    'value': 'Sampling',
-    'interactive': True,
-    'label': 'Decode Type'
-}
-
-# Sliders and their settings
-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(**params)
-    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):
-    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, context, _ = model.chat(image=image, msgs=msgs, context=None, tokenizer=tokenizer, **params)
-        res = re.sub(r'(<box>.*</box>)', '', answer).replace('<ref>', '').replace('</ref>', '').replace('<box>', '').replace('</box>', '')
-        return 0, res, 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()
-    _context.append({"role": "user", "content": _question})
-
-    if params_form == 'Beam Search':
-        params = {'sampling': False, 'num_beams': num_beams, 'repetition_penalty': repetition_penalty, "max_new_tokens": 896}
-    else:  # Ensure this block is executed for Sampling
-        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)
-    
-    _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 clear(chat_bot, app_session):
-    app_session['img'] = None
-    chat_bot.clear()
-    return chat_bot
-
-with gr.Blocks() as demo:
-    gr.Markdown("<h1 style='text-align: center;'>Medical Assistant</h1>")
-
-    with gr.Row():
-        with gr.Column(scale=2, min_width=300):
-            app_session = gr.State({'sts': None, 'ctx': None, 'img': None})
-            bt_pic = gr.Image(label="Upload an image to start")
-            txt_message = gr.Textbox(label="Ask your question...")
-        
-        with gr.Column(scale=2, min_width=300):
-            chat_bot = gr.Chatbot(label=f"Chatbot")
-            clear_button = gr.Button(value='Clear')
-            txt_message.submit(
-                respond, 
-                [txt_message, chat_bot, app_session], 
-                [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])
-            clear_button.click(clear, [chat_bot, app_session], chat_bot)
-
-# Launch
-demo.launch(share=True, debug=True, show_api=False)