update

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+example_images/阿能_129888755.jpg filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,2 @@
+/example_images/*
+/env/*

README.md

@@ -1,16 +1,14 @@
 ---
-title: Efficient Test Time Scaling
-emoji: 💬
-colorFrom: yellow
-colorTo: purple
+title: Smolvlm2 500M Illustration Description
+emoji: 📊
+colorFrom: red
+colorTo: yellow
 sdk: gradio
-sdk_version: 5.42.0
+sdk_version: 5.33.0
 app_file: app.py
 pinned: false
-hf_oauth: true
-hf_oauth_scopes:
-- inference-api
-short_description: Efficient Test-Time Scaling for Small Vision-Language Models
+license: apache-2.0
+short_description: Illustration Description
 ---
 
-An example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index).
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -1,70 +1,123 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-
-def respond(
-    message,
-    history: list[dict[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-    hf_token: gr.OAuthToken,
-):
+import torch
+from transformers import (
+    AutoModelForImageTextToText,
+    AutoProcessor,
+    TextIteratorStreamer,
+)
+from peft import PeftModel
+from transformers.image_utils import load_image
+from threading import Thread
+import time
+import html
+
+
+def progress_bar_html(label: str) -> str:
+    """
+    Returns an HTML snippet for a thin progress bar with a label.
+    The progress bar is styled as a dark animated bar.
     """
-    For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
+    return f"""
+<div style="display: flex; align-items: center;">
+    <span style="margin-right: 10px; font-size: 14px;">{label}</span>
+    <div style="width: 110px; height: 5px; background-color: #9370DB; border-radius: 2px; overflow: hidden;">
+        <div style="width: 100%; height: 100%; background-color: #4B0082; animation: loading 1.5s linear infinite;"></div>
+    </div>
+</div>
+<style>
+@keyframes loading {{
+    0% {{ transform: translateX(-100%); }}
+    100% {{ transform: translateX(100%); }}
+}}
+</style>
     """
-    client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b")
 
-    messages = [{"role": "system", "content": system_message}]
 
-    messages.extend(history)
+model_name = "HuggingFaceTB/SmolVLM2-256M-Video-Instruct"
+
+model = AutoModelForImageTextToText.from_pretrained(
+    model_name, dtype=torch.bfloat16, device_map="auto"
+).eval()
+
+processor = AutoProcessor.from_pretrained(model_name)
+
+print(f"Successfully load the model: {model}")
+
 
-    messages.append({"role": "user", "content": message})
+def model_inference(input_dict, history):
+    text = input_dict["text"]
+    files = input_dict["files"]
 
-    response = ""
+    if len(files) > 1:
+        images = [load_image(image) for image in files]
+    elif len(files) == 1:
+        images = [load_image(files[0])]
+    else:
+        images = []
 
-    for message in client.chat_completion(
+    if text == "" and not images:
+        gr.Error("Please input a query and optionally image(s).")
+        return
+    if text == "" and images:
+        gr.Error("Please input a text query along with the image(s).")
+        return
+
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                *[{"type": "image", "image": image} for image in images],
+                {"type": "text", "text": text},
+            ],
+        }
+    ]
+    inputs = processor.apply_chat_template(
         messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        choices = message.choices
-        token = ""
-        if len(choices) and choices[0].delta.content:
-            token = choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-chatbot = gr.ChatInterface(
-    respond,
-    type="messages",
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
+        add_generation_prompt=True,
+        tokenize=True,
+        return_dict=True,
+        return_tensors="pt",
+    ).to(model.device, dtype=model.dtype)
+    streamer = TextIteratorStreamer(
+        processor, skip_prompt=True, skip_special_tokens=True
+    )
+    generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=1024)
+    thread = Thread(target=model.generate, kwargs=generation_kwargs)
+    thread.start()
+    buffer = ""
+    yield progress_bar_html("Processing...")
+    for new_text in streamer:
+        escaped_new_text = html.escape(new_text)
+        buffer += escaped_new_text
+
+        time.sleep(0.001)
+        yield buffer
 
-with gr.Blocks() as demo:
-    with gr.Sidebar():
-        gr.LoginButton()
-    chatbot.render()
 
+examples = [
+    [
+        {
+            "text": "Write a descriptive caption for this image in a formal tone.",
+            "files": ["example_images/example.png"],
+        }
+    ],
+    [
+        {
+            "text": "What are the characters wearing?",
+            "files": ["example_images/example.png"],
+        }
+    ],
+]
+
+demo = gr.ChatInterface(
+    fn=model_inference,
+    description="# **Smolvlm2-500M-illustration-description** \n (running on CPU) The model only sees the last input, it ignores the previous conversation history.",
+    examples=examples,
+    fill_height=True,
+    textbox=gr.MultimodalTextbox(label="Query Input", file_types=["image"]),
+    stop_btn="Stop Generation",
+    multimodal=True,
+    cache_examples=False,
+)
 
-if __name__ == "__main__":
-    demo.launch()
+demo.launch(debug=True)

requirements.txt

@@ -0,0 +1,6 @@
+gradio
+transformers
+peft
+torch
+num2words
+torchvision