Update app.py

app.py

@@ -1,78 +1,117 @@
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import torch
-import os
 import gradio as gr
-import sentencepiece
-
-
-os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'max_split_size_mb:120'
-model_id = "01-ai/Yi-6B-200K"
-tokenizer_path = "./"
-eos_token_id = 7
-
-DESCRIPTION = """
-# 👋🏻Welcome to 🙋🏻‍♂️Tonic's🧑🏻‍🚀YI-200K🚀
-You can use this Space to test out the current model [01-ai/Yi-6B-200k](https://huggingface.co/01-ai/Yi-6B-200k) "🦙Llamified" version based on [01-ai/Yi-34B](https://huggingface.co/01-ai/Yi-34B) or try the [OAI-style connector](https://huggingface.co/spaces/Tonic/EasyYI) that we use for [AGYintelligence](https://huggingface.co/spaces/Tonic/AGYIntelligence).
-You can also use 🧑🏻‍🚀YI-200K🚀 by cloning this space. 🧬🔬🔍 Simply click here: <a style="display:inline-block" href="https://huggingface.co/spaces/Tonic1/YiTonic?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a></h3> 
-Join us : 🌟TeamTonic🌟 is always making cool demos! Join our active builder's🛠️community on 👻Discord: [Discord](https://discord.gg/nXx5wbX9) On 🤗Huggingface: [TeamTonic](https://huggingface.co/TeamTonic) & [MultiTransformer](https://huggingface.co/MultiTransformer) On 🌐Github: [Polytonic](https://github.com/tonic-ai) & contribute to 🌟 [PolyGPT](https://github.com/tonic-ai/polygpt-alpha)
-"""
-
-tokenizer = AutoTokenizer.from_pretrained(model_id, device_map="auto", trust_remote_code=True)
-# tokenizer = YiTokenizer.from_pretrained(tokenizer_path)
-model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=True)
-tokenizer.eos_token_id = eos_token_id
-model.config.eos_token_id = eos_token_id
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers.generation import GenerationConfig
+import re
+import copy
+from pathlib import Path
+import secrets
+import torch
+from PIL import Image, ImageDraw
 
-def format_prompt(user_message, system_message="You are YiTonic, an AI language model created by Tonic-AI. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and follow ethical guidelines and promote positive behavior."):
-    prompt = f"<|im_start|>assistant\n{system_message}<|im_end|>\n<|im_start|>\nuser\n{user_message}<|im_end|>\nassistant\n"
-    return prompt
+model_name = "qwen/Qwen-VL-Chat"
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True).eval()
+model.generation_config = GenerationConfig.from_pretrained(model_name, trust_remote_code=True)
 
-def predict(message, system_message, max_new_tokens=4056, temperature=3.5, top_p=0.9, top_k=40, model_max_length = 32000, do_sample=False):
-    formatted_prompt = format_prompt(message, system_message)
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model.to(device)
+task_history = []
 
-    input_ids = tokenizer.encode(formatted_prompt, return_tensors='pt')
-    input_ids = input_ids.to(model.device)
+BOX_TAG_PATTERN = r"<box>([\s\S]*?)</box>"
+PUNCTUATION = "！？。＂＃＄％＆＇（）＊＋，－／：；＜＝＞＠［＼］＾＿｀｛｜｝～｟｠｢｣､、〃》「」『』&#8203;``【oaicite:0】``&#8203;〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘’‛“”„‟…‧﹏."
 
-    response_ids = model.generate(
-        input_ids,
-        max_length=max_new_tokens + input_ids.shape[1],
-        temperature=temperature,  
-        top_p=top_p,              
-        top_k=top_k,              
-        no_repeat_ngram_size=9,
-        pad_token_id=tokenizer.eos_token_id,
-        do_sample=do_sample
-    )
+def save_image(image_file, upload_dir: str) -> str:
+    Path(upload_dir).mkdir(parents=True, exist_ok=True)
+    filename = secrets.token_hex(10) + Path(image_file.name).suffix
+    file_path = Path(upload_dir) / filename
+    with open(image_file, "rb") as f_input, open(file_path, "wb") as f_output:
+        f_output.write(f_input.read())
+    return str(file_path)
 
-    response = tokenizer.decode(response_ids[:, input_ids.shape[-1]:][0], skip_special_tokens=True)
-    truncate_str = "<|im_end|>"
-    if truncate_str and truncate_str in response:
-        response = response.split(truncate_str)[0]
+def clean_response(response: str) -> str:
+    response = re.sub(r'<ref>(.*?)</ref>(?:<box>.*?</box>)*(?:<quad>.*?</quad>)*', r'\1', response).strip()
+    return response
 
-    return [("bot", response)]    
-with gr.Blocks(theme='ParityError/Anime') as demo:
-    gr.Markdown(DESCRIPTION)
-    with gr.Group():
-        textbox = gr.Textbox(placeholder='Your Message Here', label='Your Message', lines=2)
-        system_prompt = gr.Textbox(placeholder='Provide a System Prompt In The First Person', label='System Prompt', lines=2, value="You are YiTonic, an AI language model created by Tonic-AI. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.")
+def chat_with_model(image_path=None, text_query=None, history=None):
+    # Modify this function to use 'history' if your model requires it
+    query_elements = []
+    if image_path:
+        query_elements.append({'image': image_path})
+    if text_query:
+        query_elements.append({'text': text_query})
+    # Add history processing here if needed
+    query = tokenizer.from_list_format(query_elements)
+    tokenized_inputs = tokenizer(query, return_tensors='pt').to(device)
+    output = model.generate(**tokenized_inputs)
+    response = tokenizer.decode(output[0], skip_special_tokens=True)
+    cleaned_response = clean_response(response)
+    return cleaned_response
+def draw_boxes(image_path, response):
+    image = Image.open(image_path)
+    draw = ImageDraw.Draw(image)
+    boxes = re.findall(r'<box>\((\d+),(\d+)\),\((\d+),(\d+)\)</box>', response)
+    for box in boxes:
+        x1, y1, x2, y2 = map(int, box)
+        draw.rectangle([x1, y1, x2, y2], outline="red", width=3)
+    return image
 
-    with gr.Group():
-        chatbot = gr.Chatbot(label='TonicYi-6B-200K-🧠🤯')
+def process_input(text=None, file=None, task_history=None):
+    if task_history is None:
+        task_history = []
+    image_path = None
+    if file is not None:
+        image_path = save_image(file, "uploaded_images")
+    response = chat_with_model(image_path=image_path, text_query=text, history=task_history)
+    task_history.append((text, response))
 
-    with gr.Group():
-        submit_button = gr.Button('Submit', variant='primary')
+    if "<box>" in response:
+        if image_path:
+            image_with_boxes = draw_boxes(image_path, response)
+            image_with_boxes_path = image_path.replace(".jpg", "_boxed.jpg")
+            image_with_boxes.save(image_with_boxes_path)
+            return [("bot", response), "image", image_with_boxes_path], task_history
+        else:
+            return [("bot", response), "text", None], task_history
+    else:
+        # Clean the response if it contains any box-like annotations
+        clean_response = re.sub(r'<ref>(.*?)</ref>(?:<box>.*?</box>)*(?:<quad>.*?</quad>)*', r'\1', response).strip()
+        return [("bot", clean_response)], task_history
 
-    with gr.Accordion(label='Advanced options', open=False):
-        max_new_tokens = gr.Slider(label='Max New Tokens', minimum=1, maximum=55000, step=1, value=4056)
-        temperature = gr.Slider(label='Temperature', minimum=0.1, maximum=4.0, step=0.1, value=1.2)
-        top_p = gr.Slider(label='Top-P (nucleus sampling)', minimum=0.05, maximum=1.0, step=0.05, value=0.9)
-        top_k = gr.Slider(label='Top-K', minimum=1, maximum=1000, step=1, value=40)
-        do_sample_checkbox = gr.Checkbox(label='Disable for faster inference', value=True)
+# Define Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("""
+# 🙋🏻‍♂️欢迎来到🌟Tonic 的🦆Qwen-VL-Chat🤩Bot！🚀
+# 🙋🏻‍♂️Welcome toTonic's Qwen-VL-Chat Bot! 
+该WebUI基于Qwen-VL-Chat，实现聊天机器人功能。 但我必须解决它的很多问题，也许我也能获得一些荣誉。
+Qwen-VL-Chat 是一种多模式输入模型。 您可以使用此空间来测试当前模型 [qwen/Qwen-VL-Chat](https://huggingface.co/qwen/Qwen-VL-Chat) 您也可以使用 🧑🏻‍🚀qwen/Qwen-VL -通过克隆这个空间来聊天🚀。 🧬🔬🔍 只需点击这里：[重复空间](https://huggingface.co/spaces/Tonic1/VLChat?duplicate=true)
+加入我们：🌟TeamTonic🌟总是在制作很酷的演示！ 在 👻Discord 上加入我们活跃的构建者🛠️社区：[Discord](https://discord.gg/nXx5wbX9) 在 🤗Huggingface 上：[TeamTonic](https://huggingface.co/TeamTonic) 和 [MultiTransformer](https:/ /huggingface.co/MultiTransformer) 在 🌐Github 上：[Polytonic](https://github.com/tonic-ai) 并为 🌟 [PolyGPT](https://github.com/tonic-ai/polygpt-alpha) 做出贡献 ）
+This WebUI is based on Qwen-VL-Chat, implementing chatbot functionalities. Qwen-VL-Chat is a multimodal input model. You can use this Space to test out the current model [qwen/Qwen-VL-Chat](https://huggingface.co/qwen/Qwen-VL-Chat) You can also use  qwen/Qwen-VL-Chat🚀 by cloning this space.   Simply click here: [Duplicate Space](https://huggingface.co/spaces/Tonic1/VLChat?duplicate=true)
+Join us:  TeamTonic  is always making cool demos! Join our active builder's community on  Discord: [Discord](https://discord.gg/nXx5wbX9) On  Huggingface: [TeamTonic](https://huggingface.co/TeamTonic) & [MultiTransformer](https://huggingface.co/MultiTransformer) On  Github: [Polytonic](https://github.com/tonic-ai) & contribute to   [PolyGPT](https://github.com/tonic-ai/polygpt-alpha)
+""")
+    with gr.Row():
+        with gr.Column(scale=1):
+            chatbot = gr.Chatbot(label='Qwen-VL-Chat')
+        with gr.Column(scale=1):
+            with gr.Row():
+                query = gr.Textbox(lines=2, label='Input', placeholder="Type your message here...")
+                file_upload = gr.File(label="Upload Image")
+                submit_btn = gr.Button("Submit")
+    
+    task_history = gr.State([])
 
-    submit_button.click(
-        fn=predict,
-        inputs=[textbox, system_prompt, max_new_tokens, temperature, top_p, top_k, do_sample_checkbox],
-        outputs=chatbot
+    submit_btn.click(
+        fn=process_input,
+        inputs=[query, file_upload, task_history],
+        outputs=[chatbot, task_history]
     )
+    
+    gr.Markdown("""
+注意：此演示受 Qwen-VL 原始许可证的约束。我们强烈建议用户不要故意生成或允许他人故意生成有害内容，
+包括仇恨言论、暴力、色情、欺骗等。（注：本演示受Qwen-VL许可协议约束，强烈建议用户不要传播或允许他人传播以下内容，包括但不限于仇恨言论、暴力、色情、欺诈相关的有害信息 .)
+Note: This demo is governed by the original license of Qwen-VL. We strongly advise users not to knowingly generate or allow others to knowingly generate harmful content,
+including hate speech, violence, pornography, deception, etc. (Note: This demo is subject to the license agreement of Qwen-VL. We strongly advise users not to disseminate or allow others to disseminate the following content, including but not limited to hate speech, violence, pornography, and fraud-related harmful information.)
+""")
+    demo.queue().launch()
 
-demo.launch()
+if __name__ == "__main__":
+    demo.launch()