Update app.py

app.py

@@ -1,232 +1,148 @@
-# Copyright (c) Alibaba Cloud.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
 import os
-import numpy as np
-from urllib3.exceptions import HTTPError
-os.system('pip install dashscope  modelscope oss2 -U')
-
-from argparse import ArgumentParser
-from pathlib import Path
-
-import copy
+import torch
+from threading import Thread
 import gradio as gr
-import oss2
-import os
-import re
-import secrets
+from transformers import AutoProcessor, AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer
+
+# 3D mesh dependencies
+import trimesh
+from trimesh.exchange.gltf import export_glb
+import numpy as np
 import tempfile
-import requests
-from http import HTTPStatus
-from dashscope import MultiModalConversation
-import dashscope
-
-API_KEY = os.environ['API_KEY']
-dashscope.api_key = API_KEY
-
-REVISION = 'v1.0.4'
-BOX_TAG_PATTERN = r"<box>([\s\S]*?)</box>"
-PUNCTUATION = "！？。＂＃＄％＆＇（）＊＋，－／：；＜＝＞＠［＼］＾＿｀｛｜｝～｟｠｢｣､、〃》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘’‛“”„‟…‧﹏."
-
-
-def _get_args():
-    parser = ArgumentParser()
-    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=7860,
-                        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 _parse_text(text):
-    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("$", "&#36;")
-                lines[i] = "<br>" + line
-    text = "".join(lines)
-    return text
-
-
-def _remove_image_special(text):
-    text = text.replace('<ref>', '').replace('</ref>', '')
-    return re.sub(r'<box>.*?(</box>|$)', '', text)
-
-
-def is_video_file(filename):
-    video_extensions = ['.mp4', '.avi', '.mkv', '.mov', '.wmv', '.flv', '.webm', '.mpeg']
-    return any(filename.lower().endswith(ext) for ext in video_extensions)
-
-
-def _launch_demo(args):
-    uploaded_file_dir = os.environ.get("GRADIO_TEMP_DIR") or str(
-        Path(tempfile.gettempdir()) / "gradio"
-    )
 
-    def predict(_chatbot, task_history):
-        chat_query = _chatbot[-1][0]
-        query = task_history[-1][0]
-        if len(chat_query) == 0:
-            _chatbot.pop()
-            task_history.pop()
-            return _chatbot
-        print("User: " + _parse_text(query))
-        history_cp = copy.deepcopy(task_history)
-        full_response = ""
-        messages = []
-        content = []
-        for q, a in history_cp:
-            if isinstance(q, (tuple, list)):
-                if is_video_file(q[0]):
-                    content.append({'video': f'file://{q[0]}'})
-                else:
-                    content.append({'image': f'file://{q[0]}'})
-            else:
-                content.append({'text': q})
-                messages.append({'role': 'user', 'content': content})
-                messages.append({'role': 'assistant', 'content': [{'text': a}]})
-                content = []
-        messages.pop()
-        responses = MultiModalConversation.call(
-            model='qwen2.5-vl-32b-instruct', messages=messages, stream=True,
-        )
-        for response in responses:
-            if not response.status_code == HTTPStatus.OK:
-                raise HTTPError(f'response.code: {response.code}\nresponse.message: {response.message}')
-            response = response.output.choices[0].message.content
-            response_text = []
-            for ele in response:
-                if 'text' in ele:
-                    response_text.append(ele['text'])
-                elif 'box' in ele:
-                    response_text.append(ele['box'])
-            response_text = ''.join(response_text)
-            _chatbot[-1] = (_parse_text(chat_query), _remove_image_special(response_text))
-            yield _chatbot
-
-        if len(response) > 1:
-            result_image = response[-1]['result_image']
-            resp = requests.get(result_image)
-            os.makedirs(uploaded_file_dir, exist_ok=True)
-            name = f"tmp{secrets.token_hex(20)}.jpg"
-            filename = os.path.join(uploaded_file_dir, name)
-            with open(filename, 'wb') as f:
-                f.write(resp.content)
-            response = ''.join(r['box'] if 'box' in r else r['text'] for r in response[:-1])
-            _chatbot.append((None, (filename,)))
-        else:
-            response = response[0]['text']
-            _chatbot[-1] = (_parse_text(chat_query), response)
-        full_response = _parse_text(response)
-
-        task_history[-1] = (query, full_response)
-        print("Qwen2.5-VL-Chat: " + _parse_text(full_response))
-        yield _chatbot
-
-
-    def regenerate(_chatbot, task_history):
-        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))
-        _chatbot_gen = predict(_chatbot, task_history)
-        for _chatbot in _chatbot_gen:
-            yield _chatbot
-
-    def add_text(history, task_history, text):
-        task_text = text
-        history = history if history is not None else []
-        task_history = task_history if task_history is not None else []
-        history = history + [(_parse_text(text), None)]
-        task_history = task_history + [(task_text, None)]
-        return history, task_history, ""
-
-    def add_file(history, task_history, file):
-        history = history if history is not None else []
-        task_history = task_history if task_history is not None else []
-        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 reset_state(task_history):
-        task_history.clear()
-        return []
-
-    with gr.Blocks() as demo:
-        gr.Markdown("""<center><font size=3> Qwen2.5-VL-32B-Instruct Demo </center>""")
-
-        chatbot = gr.Chatbot(label='Qwen2.5-VL-32B-Instruct', elem_classes="control-height", height=500)
-        query = gr.Textbox(lines=2, label='Input')
-        task_history = gr.State([])
-
-        with gr.Row():
-            addfile_btn = gr.UploadButton("📁 Upload (上传文件)", file_types=["image", "video"])
-            submit_btn = gr.Button("🚀 Submit (发送)")
-            regen_btn = gr.Button("🤔️ Regenerate (重试)")
-            empty_bin = gr.Button("🧹 Clear History (清除历史)")
-
-        submit_btn.click(add_text, [chatbot, task_history, query], [chatbot, task_history]).then(
-            predict, [chatbot, task_history], [chatbot], show_progress=True
-        )
-        submit_btn.click(reset_user_input, [], [query])
-        empty_bin.click(reset_state, [task_history], [chatbot], show_progress=True)
-        regen_btn.click(regenerate, [chatbot, task_history], [chatbot], show_progress=True)
-        addfile_btn.upload(add_file, [chatbot, task_history, addfile_btn], [chatbot, task_history], show_progress=True)
-
-
-    demo.queue(default_concurrency_limit=40).launch(
-        share=args.share,
-        # inbrowser=args.inbrowser,
-        # server_port=args.server_port,
-        # server_name=args.server_name,
+# --------- Configuration & Model Loading ---------
+MODEL_DIR = "./qwen2.5-vl-32b-instruct"
+# Load processor, tokenizer, model for Qwen2.5-VL
+processor = AutoProcessor.from_pretrained(MODEL_DIR)
+tokenizer = AutoTokenizer.from_pretrained(MODEL_DIR, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_DIR,
+    torch_dtype=torch.float16,
+    device_map="auto",
+    trust_remote_code=True
+)
+
+# Terminator tokens
+terminators = [tokenizer.eos_token_id]
+
+# --------- Chat Inference Function ---------
+def chat_qwen_vl(message: str, history: list, temperature: float = 0.7, max_new_tokens: int = 1024):
+    """
+    Stream chat response from local Qwen2.5-VL model.
+    """
+    # Build conversation prompt
+    conv = []
+    for u, a in history:
+        conv.append(f"<user> {u}")
+        conv.append(f"<assistant> {a}")
+    conv.append(f"<user> {message}")
+    conv.append("<assistant>")
+
+    # Tokenize
+    inputs = tokenizer(
+        "\n".join(conv),
+        return_tensors="pt",
+        truncation=True,
+        max_length=4096
+    ).to(model.device)
+
+    # Create streamer
+    streamer = TextIteratorStreamer(
+        tokenizer,
+        timeout=10.0,
+        skip_prompt=True,
+        skip_special_tokens=True
     )
 
-
-def main():
-    args = _get_args()
-    _launch_demo(args)
-
-
-if __name__ == '__main__':
-    main()
+    # Generation kwargs
+    gen_kwargs = dict(
+        input_ids=inputs.input_ids,
+        attention_mask=inputs.attention_mask,
+        streamer=streamer,
+        do_sample=(temperature > 0),
+        temperature=temperature,
+        max_new_tokens=max_new_tokens,
+        eos_token_id=terminators,
+    )
+    if temperature == 0:
+        gen_kwargs["do_sample"] = False
+
+    # Launch generation in thread
+    thread = Thread(target=model.generate, kwargs=gen_kwargs)
+    thread.start()
+
+    # Stream outputs
+    output_chunks = []
+    for chunk in streamer:
+        output_chunks.append(chunk)
+        yield "".join(output_chunks)
+
+# --------- 3D Mesh Coloring Function ---------
+def apply_gradient_color(mesh_text: str) -> str:
+    """
+    Apply a Y-axis-based gradient RGBA color to OBJ mesh text and export as GLB.
+    """
+    # Write OBJ to temp file
+    tmp = tempfile.NamedTemporaryFile(suffix=".obj", delete=False)
+    tmp.write(mesh_text.encode('utf-8'))
+    tmp.flush()
+    tmp.close()
+
+    mesh = trimesh.load_mesh(tmp.name, file_type='obj')
+    vertices = mesh.vertices
+    ys = vertices[:, 1]
+    y_norm = (ys - ys.min()) / (ys.max() - ys.min())
+
+    colors = np.zeros((len(vertices), 4))
+    colors[:, 0] = y_norm
+    colors[:, 2] = 1 - y_norm
+    colors[:, 3] = 1.0
+    mesh.visual.vertex_colors = colors
+
+    glb_path = tmp.name.replace('.obj', '.glb')
+    with open(glb_path, 'wb') as f:
+        f.write(export_glb(mesh))
+    return glb_path
+
+# --------- Gradio Interface ---------
+css = """
+h1 { text-align: center; }
+"""
+PLACEHOLDER = (
+    "<div style='padding:30px;text-align:center;display:flex;flex-direction:column;align-items:center;'>"
+    "<h1 style='font-size:28px;opacity:0.55;'>Qwen2.5-VL Local Chat</h1>"
+    "<p style='font-size:18px;opacity:0.65;'>Ask anything or generate images!</p></div>"
+)
+
+demo = gr.Blocks(css=css)
+with demo:
+    gr.Markdown("## Local Qwen2.5-VL + 3D Mesh Visualization")
+    with gr.Row():
+        with gr.Column(scale=3):
+            chatbot = gr.Chatbot(label="Qwen2.5-VL-Local", height=450, placeholder=PLACEHOLDER)
+            gr.ChatInterface(
+                fn=chat_qwen_vl,
+                chatbot=chatbot,
+                temperature=0.7,
+                max_new_tokens=1024,
+                examples=[
+                    ["Hello, what is Qwen2.5-VL?"],
+                    ["Generate a simple Python function to sort a list."],
+                ]
+            )
+        with gr.Column(scale=2):
+            output_model = gr.Model3D(label="3D Mesh Visualization", interactive=False)
+            mesh_input = gr.Textbox(
+                label="3D Mesh OBJ Input",
+                placeholder="Paste your 3D mesh in OBJ format here...",
+                lines=5
+            )
+            visualize_btn = gr.Button("Visualize with Gradient Color")
+            visualize_btn.click(
+                fn=apply_gradient_color,
+                inputs=[mesh_input],
+                outputs=[output_model]
+            )
+
+if __name__ == "__main__":
+    demo.launch()