Update app.py

app.py

@@ -1,64 +1,182 @@
+import os
+import torch
+from flashsloth.constants import (
+    IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN,
+    DEFAULT_IM_END_TOKEN, LEARNABLE_TOKEN, LEARNABLE_TOKEN_INDEX
+)
+from flashsloth.conversation import conv_templates, SeparatorStyle
+from flashsloth.model.builder import load_pretrained_model
+from flashsloth.utils import disable_torch_init
+from flashsloth.mm_utils import (
+    tokenizer_image_token, process_images, process_images_hd_inference,
+    get_model_name_from_path, KeywordsStoppingCriteria
+)
+from PIL import Image
 import gradio as gr
-from huggingface_hub import InferenceClient
 
-"""
-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
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
+from transformers import TextIteratorStreamer
+from threading import Thread
+
+
+disable_torch_init()
+
+MODEL_PATH = "Tongbo/FlashSloth_HD-3.2B"
+
+model_name = get_model_name_from_path(MODEL_PATH)
+tokenizer, model, image_processor, context_len = load_pretrained_model(MODEL_PATH, None, model_name)
+model.to('cuda')  
+model.eval()      
+
+def generate_description(image, prompt_text, temperature, top_p, max_tokens):
+
+    keywords = ['</s>']
+
+
+    text = DEFAULT_IMAGE_TOKEN + '\n' + prompt_text
+    text = text + LEARNABLE_TOKEN
+    
+
+    image = image.convert('RGB')
+    if model.config.image_hd:
+        image_tensor = process_images_hd_inference([image], image_processor, model.config)[0]
+    else:
+        image_tensor = process_images([image], image_processor, model.config)[0]
+    image_tensor = image_tensor.unsqueeze(0).to(dtype=torch.float16, device='cuda', non_blocking=True)
+    
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+    conv = conv_templates["phi2"].copy()
+    conv.append_message(conv.roles[0], text)
+    conv.append_message(conv.roles[1], None)
+    prompt = conv.get_prompt()
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
 
-    messages.append({"role": "user", "content": message})
+    input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt')
+    input_ids = input_ids.unsqueeze(0).to(device='cuda', non_blocking=True)
 
-    response = ""
+    stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
+    streamer = TextIteratorStreamer(
+        tokenizer=tokenizer, 
+        skip_prompt=True, 
+        skip_special_tokens=True
+    )
+
+    generation_kwargs = dict(
+        inputs=input_ids,
+        images=image_tensor,
+        do_sample=True,
         temperature=temperature,
         top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
+        max_new_tokens=int(max_tokens),  
+        use_cache=True,
+        eos_token_id=tokenizer.eos_token_id,
+        stopping_criteria=[stopping_criteria],
+        streamer=streamer            
+    )
 
-        response += token
-        yield response
+    def _generate():
+        with torch.inference_mode():
+            model.generate(**generation_kwargs)
 
+    # 在单独线程中运行生成，防止阻塞
+    generation_thread = Thread(target=_generate)
+    generation_thread.start()
 
+    # 边生成边yield输出
+    partial_text = ""
+    for new_text in streamer:
+        partial_text += new_text
+        yield partial_text
+
+    generation_thread.join()
+
+# 自定义CSS样式，用于增大字体和美化界面
+custom_css = """
+<style>
+/* 增大标题字体 */
+#title {
+    font-size: 80px !important;
+    text-align: center;
+    margin-bottom: 20px;
+}
+
+/* 增大描述文字字体 */
+#description {
+    font-size: 24px !important;
+    text-align: center;
+    margin-bottom: 40px;
+}
+
+/* 增大标签和输入框的字体 */
+.gradio-container * {
+    font-size: 18px !important;
+}
+
+/* 增大按钮字体 */
+button {
+    font-size: 20px !important;
+    padding: 10px 20px;
+}
+
+/* 增大输出文本的字体 */
+.output_text {
+    font-size: 20px !important;
+}
+</style>
 """
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    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)",
-        ),
-    ],
-)
 
+with gr.Blocks(css=custom_css) as demo:
+    gr.HTML(custom_css)
+    gr.HTML("<h1 style='font-size:70px; text-align:center;'>FlashSloth 多模态大模型 Demo</h1>")
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            image_input = gr.Image(type="pil", label="上传图片")
+
+            temperature_slider = gr.Slider(
+                minimum=0.01, 
+                maximum=1.0, 
+                step=0.05, 
+                value=0.7, 
+                label="Temperature"
+            )
+            topp_slider = gr.Slider(
+                minimum=0.01, 
+                maximum=1.0, 
+                step=0.05, 
+                value=0.9, 
+                label="Top-p"
+            )
+            maxtoken_slider = gr.Slider(
+                minimum=64, 
+                maximum=3072, 
+                step=1, 
+                value=512, 
+                label="Max Tokens"
+            )
+            
+        with gr.Column(scale=1):
+            prompt_input = gr.Textbox(
+                lines=3, 
+                placeholder="Describe this photo in detail.", 
+                label="问题提示"
+            )
+            submit_button = gr.Button("生成答案", variant="primary")
+
+            output_text = gr.Textbox(
+                label="生成的答案", 
+                interactive=False, 
+                lines=15, 
+                elem_classes=["output_text"]
+            )
+
+    submit_button.click(
+        fn=generate_description, 
+        inputs=[image_input, prompt_input, temperature_slider, topp_slider, maxtoken_slider], 
+        outputs=output_text, 
+        show_progress=True
+    )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.queue().launch()