Delete app.py

app.py

@@ -1,301 +0,0 @@
-description = '''
-# 🙋🏻‍♂️Welcome to🌟Tonic's🦄Qwen-VL-Chat🤩Bot!🚀
-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)
-'''
-disclaimer = """
-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.)
-"""
-
-from modelscope import AutoModelForCausalLM, AutoTokenizer, GenerationConfig, snapshot_download
-from argparse import ArgumentParser
-from pathlib import Path
-import shutil
-import copy
-import gradio as gr
-import os
-import re
-import secrets
-import tempfile
-
-#GlobalVariables
-os.environ['CUDA_VISIBLE_DEVICES'] = '0,1'
-DEFAULT_CKPT_PATH = 'qwen/Qwen-VL-Chat'
-REVISION = 'v1.0.4'
-BOX_TAG_PATTERN = r"<box>([\s\S]*?)</box>"
-PUNCTUATION = "ï¼ï¼Ÿã€‚ï¼‚ï¼ƒï¼„ï¼…ï¼†ï¼‡ï¼ˆï¼‰ï¼Šï¼‹ï¼Œï¼ï¼ï¼šï¼›ï¼œï¼ï¼žï¼ ï¼»ï¼¼ï¼½ï¼¾ï¼¿ï½€ï½›ï½œï½ï½žï½Ÿï½ ï½¢ï½£ï½¤ã€ã€ƒã€‹ã€Œã€ã€Žã€ã€ã€‘ã€”ã€•ã€–ã€—ã€˜ã€™ã€šã€›ã€œã€ã€žã€Ÿã€°ã€¾ã€¿â€“â€”â€˜â€™â€›â€œâ€â€žâ€Ÿâ€¦â€§ï¹."
-uploaded_file_dir = os.environ.get("GRADIO_TEMP_DIR") or str(Path(tempfile.gettempdir()) / "gradio")
-tokenizer = None
-model = None
-
-def _get_args() -> ArgumentParser:
-    parser = ArgumentParser()
-    parser.add_argument("-c", "--checkpoint-path", type=str, default=DEFAULT_CKPT_PATH,
-                        help="Checkpoint name or path, default to %(default)r")
-    parser.add_argument("--revision", type=str, default=REVISION)
-    parser.add_argument("--cpu-only", action="store_true", help="Run demo with CPU only")
-
-    parser.add_argument("--share", action="store_true", default=False,
-                        help="Create a publicly shareable link for the interface.")
-    parser.add_argument("--inbrowser", action="store_true", default=False,
-                        help="Automatically launch the interface in a new tab on the default browser.")
-    parser.add_argument("--server-port", type=int, default=8000,
-                        help="Demo server port.")
-    parser.add_argument("--server-name", type=str, default="127.0.0.1",
-                        help="Demo server name.")
-
-    args = parser.parse_args()
-    return args
-
-def handle_image_submission(_chatbot, task_history, file) -> tuple:
-    print("handle_image_submission called")
-    if file is None:
-        print("No file uploaded")
-        return _chatbot, task_history
-    print("File received:", file)
-    file_path = save_image(file, uploaded_file_dir)
-    print("File saved at:", file_path)
-    history_item = ((file_path,), None)
-    _chatbot.append(history_item)
-    task_history.append(history_item)
-    return predict(_chatbot, task_history, tokenizer, model)
-
-    
-def _load_model_tokenizer(args) -> tuple:
-    global tokenizer, model
-    model_id = args.checkpoint_path
-    model_dir = snapshot_download(model_id, revision=args.revision)
-    tokenizer = AutoTokenizer.from_pretrained(
-        model_dir, trust_remote_code=True, resume_download=True,
-    )
-
-    if args.cpu_only:
-        device_map = "cpu"
-    else:
-        device_map = "auto"
-
-    model = AutoModelForCausalLM.from_pretrained(
-        model_dir,
-        device_map=device_map,
-        trust_remote_code=True,
-        bf16=True,
-        resume_download=True,
-    ).eval()
-    model.generation_config = GenerationConfig.from_pretrained(
-        model_dir, trust_remote_code=True, resume_download=True,
-    )
-
-    return model, tokenizer
-
-
-def _parse_text(text: str) -> str:
-    lines = text.split("\n")
-    lines = [line for line in lines if line != ""]
-    count = 0
-    for i, line in enumerate(lines):
-        if "```" in line:
-            count += 1
-            items = line.split("`")
-            if count % 2 == 1:
-                lines[i] = f'<pre><code class="language-{items[-1]}">'
-            else:
-                lines[i] = f"<br></code></pre>"
-        else:
-            if i > 0:
-                if count % 2 == 1:
-                    line = line.replace("`", r"\`")
-                    line = line.replace("<", "&lt;")
-                    line = line.replace(">", "&gt;")
-                    line = line.replace(" ", "&nbsp;")
-                    line = line.replace("*", "&ast;")
-                    line = line.replace("_", "&lowbar;")
-                    line = line.replace("-", "&#45;")
-                    line = line.replace(".", "&#46;")
-                    line = line.replace("!", "&#33;")
-                    line = line.replace("(", "&#40;")
-                    line = line.replace(")", "&#41;")
-                    line = line.replace("$", "&#36;")
-                lines[i] = "<br>" + line
-    text = "".join(lines)
-    return text
-
-def save_image(image_file, upload_dir: str) -> str:
-    print("save_image called with:", image_file)
-    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
-    print("Saving to:", file_path)
-    with open(image_file.name, "rb") as f_input, open(file_path, "wb") as f_output:
-        f_output.write(f_input.read())
-    return str(file_path)
-
-
-def add_file(history, task_history, file):
-    if file is None:
-        return history, task_history
-    file_path = save_image(file)
-    history = history + [((file_path,), None)]
-    task_history = task_history + [((file_path,), None)]
-    return history, task_history
-
-
-def predict(_chatbot, task_history) -> list:
-    print("predict called")
-    if not _chatbot:
-        return _chatbot 
-    chat_query = _chatbot[-1][0]
-    print("Chat query:", chat_query)
-
-    if isinstance(chat_query, tuple):
-        query = [{'image': chat_query[0]}]
-    else:
-        query = [{'text': _parse_text(chat_query)}]
-
-    print("Query for model:", query)
-    inputs = tokenizer.from_list_format(query)
-    tokenized_inputs = tokenizer(inputs, return_tensors='pt')
-    tokenized_inputs = tokenized_inputs.to(model.device)
-
-    pred = model.generate(**tokenized_inputs)
-    response = tokenizer.decode(pred.cpu()[0], skip_special_tokens=False)
-    print("Model response:", response)
-    if 'image' in query[0]:
-        image = tokenizer.draw_bbox_on_latest_picture(response)
-        if image is not None:
-            image_path = save_image(image, uploaded_file_dir)
-            _chatbot[-1] = (chat_query, (image_path,))
-        else:
-            _chatbot[-1] = (chat_query, "No image to display.")
-    else:
-        _chatbot[-1] = (chat_query, response)
-    return _chatbot
-
-def save_uploaded_image(image_file, upload_dir):
-    if image is None:
-        return None
-    temp_dir = secrets.token_hex(20)
-    temp_dir = Path(uploaded_file_dir) / temp_dir
-    temp_dir.mkdir(exist_ok=True, parents=True)
-    name = f"tmp{secrets.token_hex(5)}.jpg"
-    filename = temp_dir / name
-    image.save(str(filename))
-    return str(filename)
-
-def regenerate(_chatbot, task_history) -> list:
-    if not task_history:
-        return _chatbot
-    item = task_history[-1]
-    if item[1] is None:
-        return _chatbot
-    task_history[-1] = (item[0], None)
-    chatbot_item = _chatbot.pop(-1)
-    if chatbot_item[0] is None:
-        _chatbot[-1] = (_chatbot[-1][0], None)
-    else:
-        _chatbot.append((chatbot_item[0], None))
-    return predict(_chatbot, task_history, tokenizer, model)
-
-def add_text(history, task_history, text) -> tuple:
-    task_text = text
-    if len(text) >= 2 and text[-1] in PUNCTUATION and text[-2] not in PUNCTUATION:
-        task_text = text[:-1]
-        history = history + [(_parse_text(text), None)]
-        task_history = task_history + [(task_text, None)]
-        return history, task_history, ""
-
-def add_file(history, task_history, file):
-    if file is None:
-        return history, task_history  # Return if no file is uploaded
-    file_path = file.name
-    history = history + [((file.name,), None)]
-    task_history = task_history + [((file.name,), None)]
-    return history, task_history
-
-def reset_user_input():
-    return gr.update(value="")
-    
-def process_response(response: str) -> str:
-    response = response.replace("<ref>", "").replace(r"</ref>", "")
-    response = re.sub(BOX_TAG_PATTERN, "", response)
-    return response
-    
-def process_history_for_model(task_history) -> list:
-    processed_history = []
-    for query, response in task_history:
-        if isinstance(query, tuple): 
-            query = {'image': query[0]}
-        else:
-            query = {'text': query}
-        response = response or ""
-        processed_history.append((query, response))
-    return processed_history
-
-def reset_state(task_history) -> list:
-    task_history.clear()
-    return []
-
-
-def _launch_demo(args, model, tokenizer):
-    uploaded_file_dir = os.environ.get("GRADIO_TEMP_DIR") or str(
-        Path(tempfile.gettempdir()) / "gradio"
-    )
-
-    with gr.Blocks() as demo:
-        gr.Markdown(description)
-        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...")
-                    submit_btn = gr.Button("🚀 Submit")
-                with gr.Row():
-                    file_upload = gr.UploadButton("📁 Upload Image", file_types=["image"])
-                    submit_file_btn = gr.Button("Submit Image")
-                    regen_btn = gr.Button("🤔️ Regenerate")
-                    empty_bin = gr.Button("🧹 Clear History")
-                task_history = gr.State([])
-
-        submit_btn.click(
-            fn=predict,
-            inputs=[chatbot, task_history],
-            outputs=[chatbot]
-        )
-        
-        submit_file_btn.click(
-            fn=handle_image_submission,
-            inputs=[chatbot, task_history, file_upload],
-            outputs=[chatbot, task_history]
-        )
-
-        regen_btn.click(
-            fn=regenerate,
-            inputs=[chatbot, task_history],
-            outputs=[chatbot]
-        )
-
-        empty_bin.click(
-            fn=reset_state,
-            inputs=[task_history],
-            outputs=[task_history],
-        )
-
-        query.submit(
-            fn=add_text,
-            inputs=[chatbot, task_history, query],
-            outputs=[chatbot, task_history, query]
-        )
-
-        gr.Markdown(disclaimer)
-
-    demo.queue().launch()
-
-
-def main():
-    args = _get_args()
-    model, tokenizer = _load_model_tokenizer(args)
-    _launch_demo(args, model, tokenizer)
-
-if __name__ == '__main__':
-    main()